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Top 10 Software Engineer Research Topics for 2024

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Software engineering, in general, is a dynamic and rapidly changing field that demands a thorough understanding of concepts related to programming, computer science, and mathematics. As software systems become more complicated in the future, software developers must stay updated on industry innovations and the latest trends. Working on software engineering research topics is an important part of staying relevant in the field of software engineering. 

Software engineers can do research to learn about new technologies, approaches, and strategies for developing and maintaining complex software systems. Software engineers can conduct research on a wide range of topics. Software engineering research is also vital for increasing the functionality, security, and dependability of software systems. Going for the Top Programming Certification course contributes to the advancement of the field's state of the art and assures that software engineers can continue to build high-quality, effective software systems.

What are Software Engineer Research Topics?

Software engineer research topics are areas of exploration and study in the rapidly evolving field of software engineering. These research topics include various software development approaches, quality of software, testing of software, maintenance of software, security measures for software, machine learning models in software engineering, DevOps, and architecture of software. Each of these software engineer research topics has distinct problems and opportunities for software engineers to investigate and make major contributions to the field. In short, research topics for software engineering provide possibilities for software engineers to investigate new technologies, approaches, and strategies for developing and managing complex software systems. 

For example, research on agile software development could identify the benefits and drawbacks of using agile methodology, as well as develop new techniques for effectively implementing agile practices. Software testing research may explore new testing procedures and tools, as well as assess the efficacy of existing ones. Software quality research may investigate the elements that influence software quality and develop approaches for enhancing software system quality and minimizing the faults and errors. Software metrics are quantitative measures that are used to assess the quality, maintainability, and performance of software. 

The research papers on software engineering topics in this specific area could identify novel measures for evaluating software systems or techniques for using metrics to improve the quality of software. The practice of integrating code changes into a common repository and pushing code changes to production in small, periodic batches is known as continuous integration and deployment (CI/CD). This research could investigate the best practices for establishing CI/CD or developing tools and approaches for automating the entire CI/CD process.

Top Software Engineer Research Topics

1. artificial intelligence and software engineering.

Intersections between AI and SE

The creation of AI-powered software engineering tools is one potential research area at the intersection of artificial intelligence (AI) and software engineering. These technologies use AI techniques that include machine learning, natural language processing, and computer vision to help software engineers with a variety of tasks throughout the software development lifecycle. An AI-powered code review tool, for example, may automatically discover potential flaws or security vulnerabilities in code, saving developers a lot of time and lowering the chance of human error. Similarly, an AI-powered testing tool might build test cases and analyze test results automatically to discover areas for improvement. 

Furthermore, AI-powered project management tools may aid in the planning and scheduling of projects, resource allocation, and risk management in the project. AI can also be utilized in software maintenance duties such as automatically discovering and correcting defects or providing code refactoring solutions. However, the development of such tools presents significant technical and ethical challenges, such as the necessity of large amounts of high-quality data, the risk of bias present in AI algorithms, and the possibility of AI replacing human jobs. Continuous study in this area is therefore required to ensure that AI-powered software engineering tools are successful, fair, and responsible.

Knowledge-based Software Engineering

Another study area that overlaps with AI and software engineering is knowledge-based software engineering (KBSE). KBSE entails creating software systems capable of reasoning about knowledge and applying that knowledge to enhance software development processes. The development of knowledge-based systems that can help software engineers in detecting and addressing complicated problems is one example of KBSE in action. To capture domain-specific knowledge, these systems use knowledge representation techniques such as ontologies, and reasoning algorithms such as logic programming or rule-based systems to derive new knowledge from already existing data. 

KBSE can be utilized in the context of AI and software engineering to create intelligent systems capable of learning from past experiences and applying that information to improvise future software development processes. A KBSE system, for example, may be used to generate code based on previous code samples or to recommend code snippets depending on the requirements of a project. Furthermore, KBSE systems could be used to improve the precision and efficiency of software testing and debugging by identifying and prioritizing bugs using knowledge-based techniques. As a result, continued research in this area is critical to ensuring that AI-powered software engineering tools are productive, fair, and responsible.

2. Natural Language Processing

Multimodality

Multimodality in Natural Language Processing (NLP) is one of the appealing research ideas for software engineering at the nexus of computer vision, speech recognition, and NLP. The ability of machines to comprehend and generate language from many modalities, such as text, speech, pictures, and video, is referred to as multimodal NLP. The goal of multimodal NLP is to develop systems that can learn from and interpret human communication across several modalities, allowing them to engage with humans in more organic and intuitive ways. 

The building of conversational agents or chatbots that can understand and create responses using several modalities is one example of multimodal NLP in action. These agents can analyze text input, voice input, and visual clues to provide more precise and relevant responses, allowing users to have a more natural and seamless conversational experience. Furthermore, multimodal NLP can be used to enhance language translation systems, allowing them to more accurately and effectively translate text, speech, and visual content.

The development of multimodal NLP systems must take efficiency into account. as multimodal NLP systems require significant computing power to process and integrate information from multiple modalities, optimizing their efficiency is critical to ensuring that they can operate in real-time and provide users with accurate and timely responses. Developing algorithms that can efficiently evaluate and integrate input from several modalities is one method for improving the efficiency of multimodal NLP systems. 

Overall, efficiency is a critical factor in the design of multimodal NLP systems. Researchers can increase the speed, precision, and scalability of these systems by inventing efficient algorithms, pre-processing approaches, and hardware architectures, allowing them to run successfully and offer real-time replies to consumers. Software Engineering training will help you level up your career and gear up to land you a job in the top product companies as a skilled Software Engineer. 

3. Applications of Data Mining in Software Engineering

Mining Software Engineering Data

The mining of software engineering data is one of the significant research paper topics for software engineering, involving the application of data mining techniques to extract insights from enormous datasets that are generated during software development processes. The purpose of mining software engineering data is to uncover patterns, trends, and various relationships that can inform software development practices, increase software product quality, and improve software development process efficiency. 

Mining software engineering data, despite its potential benefits, has various obstacles, including the quality of data, scalability, and privacy of data. Continuous research in this area is required to develop more effective data mining techniques and tools, as well as methods for ensuring data privacy and security, to address these challenges. By tackling these issues, mining software engineering data can continue to promote many positive aspects in software development practices and the overall quality of product.

Clustering and Text Mining

Clustering is a data mining approach that is used to group comparable items or data points based on their features or characteristics. Clustering can be used to detect patterns and correlations between different components of software, such as classes, methods, and modules, in the context of software engineering data. 

On the other hand, text mining is a method of data mining that is used to extract valuable information from unstructured text data such as software manuals, code comments, and bug reports. Text mining can be applied in the context of software engineering data to find patterns and trends in software development processes

4. Data Modeling

Data modeling is an important area of research paper topics in software engineering study, especially in the context of the design of databases and their management. It involves developing a conceptual model of the data that a system will need to store, organize, and manage, as well as establishing the relationships between various data pieces. One important goal of data modeling in software engineering research is to make sure that the database schema precisely matches the system's and its users' requirements. Working closely with stakeholders to understand their needs and identify the data items that are most essential to them is necessary.

5. Verification and Validation

Verification and validation are significant research project ideas for software engineering research because they help us to ensure that software systems are correctly built and suit the needs of their users. While most of the time, these terms are frequently used interchangeably, they refer to distinct stages of the software development process. The process of ensuring that a software system fits its specifications and needs is referred to as verification. This involves testing the system to confirm that it behaves as planned and satisfies the functional and performance specifications. In contrast, validation is the process of ensuring that a software system fulfils the needs of its users and stakeholders. 

This includes ensuring that the system serves its intended function and meets the requirements of its users. Verification and validation are key components of the software development process in software engineering research. Researchers can help to improve the functionality and dependability of software systems, minimize the chance of faults and mistakes, and ultimately develop better software products for their consumers by verifying that software systems are designed correctly and that they satisfy the needs of their users.

6. Software Project Management

Software project management is an important component of software engineering research because it comprises the planning, organization, and control of resources and activities to guarantee that software projects are finished on time, within budget, and to the needed quality standards. One of the key purposes of software project management in research is to guarantee that the project's stakeholders, such as users, clients, and sponsors, are satisfied with their needs. This includes defining the project's requirements, scope, and goals, as well as identifying potential risks and restrictions to the project's success.

7. Software Quality

The quality of a software product is defined as how well it fits in with its criteria, how well it performs its intended functions, and meets the needs of its consumers. It includes features such as dependability, usability, maintainability, effectiveness, and security, among others. Software quality is a prominent and essential research topic in software engineering. Researchers are working to provide methodologies, strategies, and tools for evaluating and improving software quality, as well as forecasting and preventing software faults and defects. Overall, software quality research is a large and interdisciplinary field that combines computer science, engineering, and statistics. Its mission is to increase the reliability, accessibility, and overall quality of software products and systems, thereby benefiting both software developers and end consumers.

8. Ontology

Ontology is a formal specification of a conception of a domain used in computer science to allow knowledge sharing and reuse. Ontology is a popular and essential area of study in the context of software engineering research. The construction of ontologies for specific domains or application areas could be a research topic in ontology for software engineering. For example, a researcher may create an ontology for the field of e-commerce to give common knowledge and terminology to software developers as well as stakeholders in that domain. The integration of several ontologies is another intriguing study topic in ontology for software engineering. As the number of ontologies generated for various domains and applications grows, there is an increasing need to integrate them in order to enable interoperability and reuse.

9. Software Models

In general, a software model acts as an abstract representation of a software system or its components. Software models can be used to help software developers, different stakeholders, and users communicate more effectively, as well as to properly evaluate, design, test, and maintain software systems. The development and evaluation of modeling languages and notations is one research example connected to software models. Researchers, for example, may evaluate the usefulness and efficiency of various modeling languages, such as UML or BPMN, for various software development activities or domains. 

Researchers could also look into using software models for software testing and verification. They may investigate how models might be used to produce test cases or to do model checking, a formal technique for ensuring the correctness of software systems. They may also examine the use of models for monitoring at runtime and software system adaptation.

The Software Development Life Cycle (SDLC) is a software engineering process for planning, designing, developing, testing, and deploying software systems. SDLC is an important research issue in software engineering since it is used to manage software projects and ensure the quality of the resultant software products by software developers and project managers. The development and evaluation of novel software development processes is one SDLC-related research topic. SDLC research also includes the creation and evaluation of different software project management tools and practices. 

Researchers may also check the implementation of SDLC in specific sectors or applications. They may, for example, investigate the use of SDLC in the development of systems that are more safety-critical, such as medical equipment or aviation systems, and develop new processes or tools to ensure the safety and reliability of these systems. They may also look into using SDLC to design software systems in new sectors like the Internet of Things or in blockchain technology.

Why is Software Engineering Required?

Software engineering is necessary because it gives a systematic way to developing, designing, and maintaining reliable, efficient, and scalable software. As software systems have become more complicated over time, software engineering has become a vital discipline to ensure that software is produced in a way that is fully compatible with end-user needs, reliable, and long-term maintainable.

When the cost of software development is considered, software engineering becomes even more important. Without a disciplined strategy, developing software can result in overinflated costs, delays, and a higher probability of errors that require costly adjustments later. Furthermore, software engineering can help reduce the long-term maintenance costs that occur by ensuring that software is designed to be easy to maintain and modify. This can save money in the long run by lowering the number of resources and time needed to make software changes as needed.

2. Scalability

Scalability is an essential factor in software development, especially for programs that have to manage enormous amounts of data or an increasing number of users. Software engineering provides a foundation for creating scalable software that can evolve over time. The capacity to deploy software to diverse contexts, such as cloud-based platforms or distributed systems, is another facet of scalability. Software engineering can assist in ensuring that software is built to be readily deployed and adjusted for various environments, resulting in increased flexibility and scalability.

3. Large Software

Developers can break down huge software systems into smaller, simpler parts using software engineering concepts, making the whole system easier to maintain. This can help to reduce the software's complexity and makes it easier to maintain the system over time. Furthermore, software engineering can aid in the development of large software systems in a modular fashion, with each module doing a specific function or set of functions. This makes it easier to push new features or functionality to the product without causing disruptions to the existing codebase.

4. Dynamic Nature

Developers can utilize software engineering techniques to create dynamic content that is modular and easily modifiable when user requirements change. This can enable adding new features or functionality to dynamic content easier without disturbing the existing codebase. Another factor to consider for dynamic content is security. Software engineering can assist in ensuring that dynamic content is generated in a secure manner that protects user data and information.

5. Better Quality Management

An organized method of quality management in software development is provided by software engineering. Developers may ensure that software is conceived, produced, and maintained in a way that fulfills quality requirements and provides value to users by adhering to software engineering principles. Requirement management is one component of quality management in software engineering. Testing and validation are another part of quality control in software engineering. Developers may verify that their software satisfies its requirements and is error-free by using an organized approach to testing.

In conclusion, the subject of software engineering provides a diverse set of research topics with the ability to progress the discipline while enhancing software development and maintenance procedures. This article has dived deep into various research topics in software engineering for masters and research topics for software engineering students such as software testing and validation, software security, artificial intelligence, Natural Language Processing, software project management, machine learning, Data Mining, etc. as research subjects. Software engineering researchers have an interesting chance to explore these and other research subjects and contribute to the development of creative solutions that can improve software quality, dependability, security, and scalability. 

Researchers may make important contributions to the area of software engineering and help tackle some of the most serious difficulties confronting software development and maintenance by staying updated with the latest research trends and technologies. As software grows more important in business and daily life, there is a greater demand for current research topics in software engineering into new software engineering processes and techniques. Software engineering researchers can assist in shaping the future of software creation and maintenance through their research, ensuring that software stays dependable, safe, reliable and efficient in an ever-changing technological context. KnowledgeHut’s top Programming certification course will help you leverage online programming courses from expert trainers.

Frequently Asked Questions (FAQs)

Ans: To find a research topic in software engineering, you can review recent papers and conference proceedings, talk to different experts in the field, and evaluate your own interests and experience. You can use a combination of these approaches. 

Ans: You should study software development processes, various programming languages and their frameworks, software testing and quality assurance, software architecture, various design patterns that are currently being used, and software project management as a software engineering student. 

Ans: Empirical research, experimental research, surveys, case studies, and literature reviews are all types of research in software engineering. Each sort of study has advantages and disadvantages, and the research method chosen is determined by the research objective, resources, and available data. 

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Eshaan Pandey

Eshaan is a Full Stack web developer skilled in MERN stack. He is a quick learner and has the ability to adapt quickly with respect to projects and technologies assigned to him. He has also worked previously on UI/UX web projects and delivered successfully. Eshaan has worked as an SDE Intern at Frazor for a span of 2 months. He has also worked as a Technical Blog Writer at KnowledgeHut upGrad writing articles on various technical topics.

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research software engineering topics

150 Best Research Paper Topics For Software Engineering

Software Engineering is a branch which deals with the creation and improvement of software applications using specific methodologies and clearly defined scientific principles. When developing software products, certain procedures must be followed, the outcome of which is a reliable and reliable software product. Software is a collection of executable code for programs with associated libraries. Software that is designed to meet certain requirements is referred to as a Software Product . This is an excellent subject for a master's thesis, research, or project. There are a variety of topics within Software Engineering which will be useful to M.Tech and other students studying for their masters to write their software thesis.

What is the reason Software Engineering is required?

Software Engineering is necessary due to the frequent shifts in the requirements of users as well as the environment. Through yourch and thesis, you will learn more about the significance of Software Engineering. Here are some other areas in software engineering that are needed:

  • Big Software: The massive dimension of software makes it necessary for the requirements in software engineering .
  • Scalability The concept of scaling Software Engineering makes it possible to increase the size of existing software rather than develop brand-new software.
  • Cost Price Software Engineering also cuts down the manufacturing cost that is incurred during software development.
  • The dynamic nature of Software - Software Engineering is a crucial factor when the need for new features is to be made in software in place, in the event that the nature of software is fluid.
  • Better Quality Management - Software Engineering can provide more efficient software development processes to provide superior-high-quality services .

Best Research Paper Topics on Software

  • Software Engineering Management Unified Software Development Process and Extreme ProgrammingThere are a lot of difficulties with managing the development of software for web-based applications and projects for systems integration that were completed in recent times.
  • The Blue Sky Software Consulting Company Analysis
  • Blue Sky Software Consulting Blue Sky Software Consulting company has seen great success over 15 years. The company is not as well-equipped for the current market.
  • LabVIEW Software: Design Systems of Measurement
  • LabVIEW is a software program that was created to design systems for measurement. LabVIEW gives you a range of instruments to control the process in an experiment.
  • Software-producing Firm Reducing Inventory
  • The link between the reduction in inventory levels and the number of orders is evident. An organization that produces software may think of increasing the amount of software to a lower level.
  • Moet Hennessy - Louis Vuitton: Enterprise Software
  • The report will demonstrate how the introduction of ERP will help LVHM Group improve its results by improving its inventories, logistics and accounting.
  • Virtualization and Software-Defined Networking
  • The goal of this paper is to analyze the developments in the field of virtualization, software-defined networks and security for networks in the last three years.
  • Computer Hardware and Software Components
  • Computers that were developed at the time of the 40s of 1940 have evolved into complex machines that require software and hardware for their operation.
  • Applications, Software and System Development
  • The usage the Microsoft Office applications greatly enhance productivity in the classroom as well as at work and during everyday activities at home.
  • PeopleSoft Inc.'s Software Architecture and Design
  • With the PIA architecture, any company with an ERP application can access all of its operations through a Web browser.
  • Co-operative Banking Group's Enterprise Software
  • The report demonstrates how the implementation of the ERP system within the Co-operative Banking Group will help in improving the company's accounting, inventory and accounting practices as well as logistics processes.
  • Software Testing: Manual and Automated Web-Application Testing Tools
  • This research is an empirical study of automated and manual web-based application testing tools to determine the best tool for testing software.
  • JDA Software Company's Services
  • JDA Software is a company that has proven its worth in the development of services in areas like manufacturing, wholesale distribution, retailing and travel.
  • Data Management, Networking and Enterprise Software
  • Enterprise software is typically developed "in-house" and thus has an inflated cost when contrasted to purchasing the software from another firm.
  • Software Workshops and Seminars Reflections
  • Most seminars inspire participants to use their potential as they strive to attain their goals.
  • The Various Enterprise Resource Planning Software Packages
  • This paper's purpose is to provide an overview of the various Enterprise Resource Planning (ERP) software applications that are widely employed by companies to manage their business operations.
  • Explore Factors in IBM SPSS Statistical Software
  • The "Explore" or "Explore" command in IBM SPSS generates an output with a variety of stats for a single variable, across the entire sample or in sections of the sample.
  • Split Variables in IBM SPSS Statistical Software
  • It is the IBM SPSS software provides an option to split files into groups. The members of cases within groups can be determined by the values of split variables in this particular instance.
  • Syntax Code Writing in Statistical Software
  • The process of analyzing quantitative data by using IBM SPSS software package IBM SPSS software package often involves performing a variety of operations to calculate the statistical data for the information.
  • Data Coding in Statistical Software
  • Data coding is of utmost importance when a proper analysis of this data has to be conducted. Data coding plays an important function when you need to make use of statistical software.
  • Software Piracy at Kaspersky Cybersecurity Company
  • Software piracy is a pressing current issue that is manifested both locally with respect to an individual company and also globally.
  • Hotjar: Web Analytics Software Difference
  • This report examines Hotjar, which is a web-based analytics tool that comes with a full set of tools to evaluate. This paper examines its strengths and advantages, as well showing how it can aid in the management of decision-making.
  • Avast Software: Company Analysis
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  • Project Failure, Project Planning Fundamentals, and Software Tools and Techniques for Alternative Scheduling
  • From lack of communication to generally unfavourable working conditions, Projects may fail when managers fail to prepare for their implementation.
  • Computer Elements such as Hardware and Software
  • Personal computers are usually different from computers used for business in terms of capabilities and the extent of technology used within the equipment.
  • Review of a New Framework for Software Reliability Measurement
  • This study draws upon the in-depth study of the software reliability measurement methods and the suggestion of a fresh foundation for reliability measurement built on the software metrics studied in the work of Amar as well as Rabai.

Good Software Research Topics & Essay Examples

  • Task Management Software in Organization
  • The goal of the plan for managing projects is to present the process of creating task management software that can be integrated into the context of the company.
  • A task management software plan's risk management strategy
  • The present study introduces us to the techniques for risk identification as well as quality assurance and a control plan and explains their significance.
  • Computer Software Development and Reality Shows
  • The growth of software in computers has been at such a fast rate over the last 10 years that it has impacted all aspects of our lives and every fibre of our being.
  • Scrum - Software Development Process
  • Digital systems and computerized systems have brought life to many areas. Scrum is a process for software development that guarantees high quality and efficiency.
  • Distribution of Anti-Virus Software
  • Numerous new threats are reported every fortnight. Cyberattacks, viruses, and other cyber-related threats are becoming an issue.
  • Marketing Plan: Innovative Type of Software Product
  • This paper will create an advertisement plan for the new kind of software, which will help to define the segment of clients and the price and communications platform.
  • Marketing System of Sakhr Software Co
  • The principal objective of this paper is to examine the marketing process in the same type of organization, like Sakhr Software Co.
  • Managing Information of Sakhr Software Co
  • This paper will examine the ideas of managing information for Sakhr Software, which is a well-known language software firm.
  • CRM Software in Amazon: Gains
  • The software for managing customers that Amazon.com developed is, from the beginning, one of the latest technology.
  • Neurofeedback Software and Technology Comparison
  • MIDI technology helps make the making of, learning or playing more enjoyable. Mobile phones and computer keyboards for music, computers etc., utilize MIDI.
  • PeopleSoft Software and HR.net Enterprise Software
  • With the help of HRIS software, HR employees are able to manage their own benefits updates and make changes, allowing them to take more time to focus on other important tasks.
  • Business Applications: Revelation HelpDesk by Yellow Fish Software
  • "Revelation HelpDesk" is an online Tracking and Support Software that facilitates seamless coordination to occur between the most important divisions within an organization.
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  • ERP Software in Inventory Management
  • Management of inventory ERP applications will be useful when a business has to manage the manner in which it gets goods and cleans up the merchandise.
  • The Capabilities of Compiere Software and How Well It Fits Into Different Industries
  • It is the ERP software Compiere can be used by a wide variety of users, including governments, businesses as well as non-governmental organizations (NGOs).
  • Software Tools for Qualitative Research
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  • Data Scientist and Software Development
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  • The copyright law protects only the declaration but not the software concept. It prohibits copying code from the source without asking permission.
  • Health IT: Epic Software Analysis
  • Implementation and adoption of Health IT systems are crucial to improve the efficiency of medical practices, efficiency of workflow as well as patient outcomes.
  • Agile Software Development Process
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  • Project Management Software and Tools Comparison
  • The software is used by managers to ensure that there isn't any worker who is receiving more work than others and also to ensure that no worker is falling behind in their job.
  • Visually impaired people: challenges in Assistive Technology Software
  • Blind people suffer from a number of disadvantages each day while using digital technology. The various types of software and software discussed in this paper have been specifically designed to help improve the lives of blind people.
  • WBS completion and software project management
  • The PERT's results resulted in the development of The Gantt chart. This essay provides an account of the method of working with the Gantt chart.
  • International Software Development's Ethical Challenges: User-Useful Software
  • The importance of ethics is when it comes to software development. It helps the creator to create software that will be useful for the user as well as the management.
  • Achieving the Optimal Process. Software Development
  • The industry of software development is growing rapidly as the requirements of users change. This requires applications to meet these needs.

Innovative Software to Blog About

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  • Sakhr Software Co.'s Marketing System
  • The principal goal of this paper is to study the uniqueness of the system of marketing in such an organization as Sakhr Software Co from Kuwait, which specializes in NLP.
  • Program Code in Assembly Language Using Easy68K Software
  • A typical scenario is described in the report to write program code in assembly language with Easy68K software. The appropriate tests were carried out with success and outputs.
  • Benefits and Drawbacks of Agile Software Development Techniques
  • The use of agile methodologies in the software development process contributes to the improvement of work as well as the effectiveness of performance.
  • The use of agile methodologies in the development of software contributes to the efficiency of work and efficiency of performance.
  • Large Scale Software Development
  • This report gives information on this Resource Scheduling project. It can be useful to an advisory firm that offers various types of resources.
  • Penguin Sleuth, a Forensic Software Tool
  • The primary goal of this paper is to examine the various tools for forensic analysis and also provide a comprehensive overview of the functions available for each tool or tool pack.
  • System Software: Computer System Management
  • Computer software comprises precise preprogrammed instructions that regulate and coordinate hardware components of the computer.
  • Ethical Issues Involved in Software Project Management
  • Ethics within IT have been proven to be very different from other areas of ethics. Ethics issues in IT are usually described as having little.
  • Advantages and Disadvantages of Software Suites
  • Computer software comprises specific preprogrammed commands that control and coordinate computer hardware components of an info system.
  • Descriptive Statistics Using SPSS Software Suite
  • This paper focuses on the process of producing the descriptive statistical analysis by using SPSS. The purpose of this article is to make use of SPSS to perform an analysis of descriptive data.
  • Software Development: Creating a Prototype
  • The aim of this article is to develop an experimental software program that can be utilized to aid breast cancer patients.
  • Software Engineering and Methodologies
  • The paper explains how the author learned the software engineering process and methods as an outcome of his experiences at BTR IT Consulting Company.
  • Information System Hardware and Software
  • Information technology covers a wide variety of applications in which computer software, along with hardware, is employed.
  • Software Development Project Using Agile Methods
  • The report will provide reasons behind why the agile methodology was chosen, the method used, how the team applied this methodology, and also the lessons learned from the massive project of software development.
  • Flight Planning Software and Aircraft Incidents
  • Software for flight planning refers to programs utilized to control and manage flights and other procedures while the plane is in flight.
  • Hardware and Software Systems and Criminal Justice
  • One of the primary techniques used to decrease the chance of criminal activity is crime mapping. This involves collecting information on crimes and their causes and then analyzing it in order to identify issues.
  • Why Open-Source Software Will (Or Will Not) Soon Dominate the Field of Database Management Tools
  • The research aims to determine whether open-source software will rule the field of the database since there is an evolution in the market for business.
  • Business HRM Software and the Affordable Care Act
  • The Affordable Care Act has its strengths but also flaws. The reason is the complex nature of the law that creates a variety of challenges.
  • Antivirus Software Ensuring Security Online
  • Although it's not perfect and fragmentary, it can be seen as a supplement and not the sole instrument; antivirus software will help protect one's privacy online.
  • Evaluating Teaching Instructional Software for 21st-Century Technology Resources
  • The software for teaching Joe Rock and Friends Book 2 is designed for third-grade students who are studying English as an additional language to read and learn new vocabulary.
  • Britam Insurance Company's Sales and Marketing Management Software
  • Britam Insurance Company needs to implement the latest marketing and management software in order to keep its place at the forefront of the extremely competitive insurance market.
  • Software Programs: Adobe Illustrator
  • With Adobe Illustrator, users can quickly and precisely create various products, like logos, icons as well as drawings.
  • Strawberry Business: Software Project Management
  • Although the company has an established management strategy as well as a team of employees and efficient information systems, it lacks a standardized workplace culture and customer relations systems.
  • Value of Salesforce Software Using VRIO Model
  • Salesforce CRM software is created to help managers manage their businesses effectively. It connects all teams and managers and collects and manages customer information.
  • Agile software development, as well as popular variations like Scrum, are the foundation for the work of a variety of testers and developers. No matter what team or method you're currently using, you can get expert guidance on process structure and the skills required to use Lean, Agile, DevOps, Waterfall and more to help you implement it for your business.

Most Interesting Software Research Titles

  • What Are the Essential Attributes of Good Software?
  • How Computer Software Can Be Used as a Tool for Education
  • Accounting Software and Application Software
  • Online National Polling Software Requirements Specification
  • Building Their Software for a Company's Success
  • The Role of Antivirus Software in Protecting Your Computer Data
  • Intellectual Property Rights, Innovation and Software Technologies
  • Software Piracy and the Canadian Piracy Act
  • For the development of software projects, agile methodologies and their Waterscrumfall derivative are used.
  • Software Tools for Improving Underground Mine Access Layouts
  • How Software Can Support Academic Librarians' Changing Role
  • Using the Untangle Software to Overcome Obstacles for Small Businesses
  • By employing travel portal software, online booking sales will increase.
  • Analysis of Network Externality and Commercial Software Piracy
  • Accounting Software and Business Solutions
  • Analysis of Key Issues and Effects Relating to International Software Piracy
  • The Distinction Between Computer Science and Software Engineering
  • Modulation: Computer Software and Unknown Music Virus
  • Math Software for High School Students with Disabilities
  • Keyboarding Software Packages: Analysis and Purchase Recommended
  • Basic Software Development Life Cycle
  • India's Problems with Software Patents, Copyright, and Piracy
  • Why Has India Been Able to Build a Thriving Software Industry
  • Does Social Software Increase Labour Productivity
  • The Role of Open Source Software for Database Servers

Simple Software Essay Ideas

  • Human Capital and the Indian Software Industry
  • Input-Output Computer Windows Software
  • Business Software Development and Its Implementation
  • Evaluating Financial Management Software: Quicken Software
  • Which governance tools are important in Africa for combating software piracy?
  • Distinguish Between Proprietary Software and Off-The-Shelf
  • Does Social Software Support Service Innovation
  • Ambulatory Revenue Management Software
  • Difference Between Operating Systems and Application Software
  • Leading a Global Insurgency in the Software Sector are China and India
  • Call Accounting Software for Every Enterprise
  • Technology Standards for Software Outsourcing
  • The Importance of the Agile Approach for Software Development
  • Application Software: Publisher, Word, and Excel
  • Employee Monitoring Through Computer Software
  • Software Development Lifecycle and Testing's Importance
  • Tools for Global Conditional Policy to Combat Software Piracy
  • Software for Designing Solar Water Heating Systems
  • Open Source Software, Competition, and Potential Entry
  • Indian Software Industry: Gains are distorted and consolidated
  • Software Programs for Disabled Computer Users and Assistive Technology
  • Agile Software Architecture, Written by Christine Miyachi
  • Software Development: The Disadvantages of Agile Methods
  • Computer Software Technology for Early Childhood
  • Developing Test Automation Software Development

Easy Software Essay Topics

  • Growth Trends, Barriers, and Government Initiatives in the Indian Software Industry
  • How Does Enterprise Software Enable a Business to Use
  • Integrated Management Software the Processing of Information
  • Computer Software Training for Doctor's Office
  • Software Intellectual Property Rights and Venture Capitalist Access
  • Computer Science Software Specification
  • Software Projects and Student Software Risk Exposure
  • Why It Is Difficult to Create Software for Wireless Devices
  • Affiliate Tracking Software Your Payment Options
  • How Can Volkswagen Recover From the Cheating Issues It Had Because Illegal Software Was Installed?
  • Principles of Best Forensic Software Tool
  • The American Software Industry: A Historical Analysis
  • How Peripheral Developers Contribute to the Development of Open-Source Software
  • Agile Methodologies for Software Development
  • Key Macroeconomic Factors That Affect Software Industry
  • The Software Industry and India's Economic Development
  • Improving Customer Service Through Help Desk Software
  • Enterprise Resource Planning and Sap Software
  • Antivirus Software and Its Importance
  • Hardware and Software Used in Public Bank
  • The Effects of Computer Software Piracy on the Global Economy
  • Using the Winqsb Software in Critical Path Analysis
  • General Information About Interactive Multimedia-Based Educational Software
  • How Affiliate Tracking Software Can Benefit You
  • Computer Software and Recent Technologies

Frequently asked questions

What are the main topics of software engineering .

software development.

  • Introduction
  • Models and architecture for software development
  • Project management for software (SPM)
  • Software prerequisites
  • Testing and debugging software

What makes good research in software engineering ?

The most typical research strategy in software engineering is coming up with a novel method or methodology, validating it through analysis, or demonstrating its application through a case study;

What projects are good for software engineering ?

  • monitoring of Android tasks.
  • Analyzing attitudes to rate products
  • ATM with a fingerprint-based method.
  • a modern system for managing employees.
  • Using the AES technique for image encryption.
  • vote-by-fingerprint technology.
  • system for predicting the weather

What are the research methods in software engineering ?

We list and contrast the five categories of research methodology that, in our opinion, are most pertinent to software engineering: controlled experiments (including quasi-experiments); case studies (both exploratory and confirmatory); survey research; ethnographies; action research; and controlled experiments.

Is software engineering a research area ?

A relatively recent area of research, software engineering is derived from computer science. Its significance has been generally acknowledged by more and more academics in the field of computers throughout the course of six decades, from 1948 to the present, and it has developed into a vibrant and promising division of the computing profession.

Is software engineering easy ?

Yes, learning software engineering can be challenging at first, especially for those without programming or coding experience or any background in technology. However, numerous courses, tools, and other resources are available to assist with learning how to become a software engineer.

Who is the father of software engineering ?

The "father of software quality," Watts S. Humphrey, was an American software engineering pioneer who lived in Battle Creek, Michigan (U.S.) from July 4, 1927, to October 28, 2010.

What do you do in software engineering ?

  • roles and tasks for software engineers
  • creating and keeping up software systems.
  • testing and evaluating new software applications.
  • software speed and scalability optimization.
  • code creation and testing.
  • consulting with stakeholders such as clients, engineers, security experts, and others.

Which is better it or software engineering ?

IT support engineers cannot build sophisticated solutions, while software engineers can. In a word, they are in charge of creating and putting into use software. Knowing the distinctions makes it easier to choose the right individual to handle our tech-related problems.

Are junior software engineers in demand ?

Yes, there is a need for young coders.

Is software engineering going down ?

Software experts and software goods are oversaturating the job market for software engineers.

What degree do I need to be a software engineer ?

undergraduate degree

Can I be a software engineer without a degree ?

Many software developers lack a degree from a reputable university (or, in some circumstances, none at all).

How many years can a software engineer work ?

An engineer who wants to work in IT has a 15–20 year window.

How many hours do software engineers work ?

Software developers put in 8 to 9 hours each day, or 40 to 45 hours per week.

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Software Engineering Institute

Cite this post.

AMS Citation

Carleton, A., 2021: Architecting the Future of Software Engineering: A Research and Development Roadmap. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed February 16, 2024, https://insights.sei.cmu.edu/blog/architecting-the-future-of-software-engineering-a-research-and-development-roadmap/.

APA Citation

Carleton, A. (2021, July 12). Architecting the Future of Software Engineering: A Research and Development Roadmap. Retrieved February 16, 2024, from https://insights.sei.cmu.edu/blog/architecting-the-future-of-software-engineering-a-research-and-development-roadmap/.

Chicago Citation

Carleton, Anita. "Architecting the Future of Software Engineering: A Research and Development Roadmap." Carnegie Mellon University, Software Engineering Institute's Insights (blog) . Carnegie Mellon's Software Engineering Institute, July 12, 2021. https://insights.sei.cmu.edu/blog/architecting-the-future-of-software-engineering-a-research-and-development-roadmap/.

IEEE Citation

A. Carleton, "Architecting the Future of Software Engineering: A Research and Development Roadmap," Carnegie Mellon University, Software Engineering Institute's Insights (blog) . Carnegie Mellon's Software Engineering Institute, 12-Jul-2021 [Online]. Available: https://insights.sei.cmu.edu/blog/architecting-the-future-of-software-engineering-a-research-and-development-roadmap/. [Accessed: 16-Feb-2024].

BibTeX Code

@misc{carleton_2021, author={Carleton, Anita}, title={Architecting the Future of Software Engineering: A Research and Development Roadmap}, month={Jul}, year={2021}, howpublished={Carnegie Mellon University, Software Engineering Institute's Insights (blog)}, url={https://insights.sei.cmu.edu/blog/architecting-the-future-of-software-engineering-a-research-and-development-roadmap/}, note={Accessed: 2024-Feb-16} }

Architecting the Future of Software Engineering: A Research and Development Roadmap

Headshot of Anita Carleton.

Anita Carleton

July 12, 2021, published in.

Software Engineering Research and Development

This post has been shared 10 times.

This post is coauthored by John Robert, Mark Klein, Doug Schmidt, Forrest Shull, John Foreman, Ipek Ozkaya, Robert Cunningham, Charlie Holland, Erin Harper, and Edward Desautels

Software is vital to our country’s global competitiveness, innovation, and national security. It also ensures our modern standard of living and enables continued advances in defense, infrastructure, healthcare, commerce, education, and entertainment. As the DoD’s federally funded research and development center (FFRDC) focused on improving the practice of software engineering, the Carnegie Mellon University (CMU) Software Engineering Institute (SEI) is leading the community in creating a multi-year research and development vision and roadmap for engineering next-generation software-reliant systems. This blog post describes that effort.

Software Engineering as Strategic Advantage

In a 2020 National Academy of Science Study on Air Force software sustainment , the U.S. Air Force recognized that “to continue to be a world-class fighting force, it needs to be a world-class software developer.” This concept clearly applies far beyond the Department of Defense . Software systems enable world-class healthcare, commerce, education, energy generation, and more. These systems that run our world are rapidly becoming more data intensive and interconnected, increasingly utilize AI, require larger-scale integration, and must be considerably more resilient. Consequently, significant investment in software engineering R&D is needed now to enable and ensure future capability.

Goals of This Work

The SEI has leveraged its connections with academic institutions and communities, DoD leaders and members of the Defense Industrial Base , and industry innovators and research organizations to:

  • identify future challenges in engineering software-reliant and intelligent systems in emerging, national-priority technical domains, including gaps between current engineering techniques and future domains that will be more reliant on continuous evolution and AI
  • develop a research roadmap that will drive advances in foundational software engineering principles across a range of system types, such as intelligent, safety-critical, and data-intensive systems
  • raise the visibility of software to the point where it receives the sustained recognition commensurate with its importance to national security and competitiveness
  • enable strategic partnerships and collaborations to drive innovation among industry, academia, and government.

Guided by an Advisory Board of U.S. Visionaries and Senior Thought Leaders

To succeed in developing our vision and roadmap for software engineering research and development, it is vital to coordinate the academic, defense, and commercial communities to define an effective agenda and implement impactful results. To help represent the views of all these software engineering constituencies, the SEI formed an advisory board from DoD, industry, academia, research labs, and technology companies to offer guidance. Members of this advisory board include the following:

  • Deb Frincke , advisory board chair, Associate Laboratory Director for National Security Sciences, Oak Ridge National Laboratory
  • Michael McQuade , vice president for research, Carnegie Mellon University
  • Vint Cerf , vice president and chief internet evangelist, Google
  • Penny Compton , vice president for software systems, cyber, and operations, Lockheed Martin Space
  • Tim Dare , deputy director for prototyping and software, Office of the Under Secretary of Defense for Research and Engineering (previous position)
  • Sara Manning Dawson , chief technology officer enterprise security, Microsoft
  • Jeff Dexter , senior director of flight software & cybersecurity, SPACEX
  • Yolanda Gil, president, Association for the Advancement of Artificial Intelligence (AAAI); Director of Knowledge Technologies, Information Sciences Institute at University of Southern California
  • Tim McBride , president, Zoic Studios
  • Nancy Pendleton , vice president and senior chief engineer for mission systems, payloads and sensors, Boeing Defense, Space and Security
  • William Scherlis , director Information Innovation Office, DARPA

In June 2020, the SEI assembled this board to leverage their diverse perspectives and provide strategic advice, influence stakeholders, develop connections, assist in executing the roadmap, and advocate for the use of our results.

Future Systems and Fundamental Shifts in Software Engineering Require New Research Focus

Rapidly deploying software with confidence requires fundamental shifts in software engineering. New types of systems will continue to push beyond the bounds of what current software engineering theories, tools, and practices can support, including (but not limited to):

  • Systems that fuse data at a huge scale, whether for news, entertainment, or intelligence: We will need to continuously mine vast amounts of open-source data streams (e.g., YouTube videos and Twitter feeds) for important information that will in turn drive decision making. This vast stream of data will also drive new ways of constructing systems.
  • Smart cities, buildings, roads, cars, and transport: How will these highly connected systems work together seamlessly? How will we enable safe and affordable transportation and living?
  • Personal digital assistants: How will these assistants learn, adapt, and engage in home and business workflows?
  • Dynamically integrated healthcare: Data from your personal device will be combined with hospital data. How do we meet stringent safety and privacy requirements? How do we evaluate assurance in a highly data-driven environment?
  • Mission-level adaptation for DoD systems: DoD systems will feature mission-level construction of new integrated systems that combine a range of capabilities, such as intel, weapons, and human/machine teaming. The DoD is already moving in this direction, but how can we increase confidence that there will be no unintended consequences?

A Guiding Vision of the Future of Software Engineering

Our guiding vision is one in which the current notion of software development is replaced by the concept of a software pipeline consisting of humans and software as trustworthy collaborators who rapidly evolve systems based on user intent. To achieve this vision, we anticipate the need for not only new development paradigms but also new architectural paradigms for engineering new kinds of systems.

Advanced development paradigms, such as those listed below, lead to efficiency and trust at scale:

  • Humans leverage trusted AI as a workforce multiplier for all aspects of software creation.
  • Formal assurance arguments are evolved to assure and efficiently re-assure continuously evolving software.
  • Advanced software composition mechanisms enable predictable construction of systems at increasingly large scale.

Advanced architectural paradigms, as outlined below, enable the predictable use of new computational models:

  • Theories and techniques drawn from the behavioral sciences are used to design large-scale socio-technical systems, leading to predictable social outcomes.
  • New analysis and design methods facilitate the development of quantum-enabled systems.

AI and non-AI components interact in predictable ways to achieve enhanced mission, societal, and business goals.

Research Focus Areas

The fundamental shifts and needed advances in software engineering described above require new areas of research. In close collaboration with our advisory board and other leaders in the software engineering community, we have developed a research roadmap with six focus areas. Figure 1 shows those areas and outlines a suggested course of research topics to undertake. Short descriptions of each focus area and its challenges follow.

Figure 1: Software Engineering Research Roadmap with Research Focus Areas and Research Objectives (10-15 Year Horizon)

  • AI-Augmented Software Development . At almost every stage of the software development process, AI holds the promise of assisting humans. By relieving humans of tedious tasks, they will be better able to focus on tasks that require the creativity and innovation that only humans can provide. To reach this goal, we need to re-envision the entire software development process with increased AI and automation tool support for developers, and we need to ensure we take advantage of the data generated throughout the entire lifecycle. The focus of this research area is on what AI-augmented software development will look like at each stage of the development process and during continuous evolution, where it will be particularly useful in taking on routine tasks.
  • Assuring Continuously Evolving Systems . When we consider the software-reliant systems of today, we see that they are not static (or even infrequently updated) engineering artifacts. Instead, they are fluid—meaning that they are expected to undergo continuing updates and improvements throughout their lifespan. The goal of this research area is therefore to develop a theory and practice of rapid and assured software evolution that enables efficient and bounded re-assurance of continuously evolving systems.
  • Software Construction through Compositional Correctness . As the scope and scale of software-reliant systems continues to grow and change continuously, the complexity of these systems makes it unrealistic for any one person or group to understand the entire system. It is therefore necessary to integrate (and continually re-integrate) software-reliant systems using technologies and platforms that support the composition of modular components, many of which are reused from existing elements that were not designed to be integrated or evolved together. The goal of this research area is to create methods and tools (such as domain specific modeling language and annotation-based dependency injection) that enable the specification and enforcement of composition rules that allow (1) the creation of required behaviors (both functionality and quality attributes) and (2) the assurance of these behaviors.
  • Engineering Socio-Technical Systems . Societal-scale software systems, such as today’s commercial social media systems, are designed to keep users engaged to influence them. However, avoiding bias and ensuring the accuracy of information are not always goals or outcomes of these systems. Engineering societal-scale systems focuses on prediction of such outcomes (which we refer to as socially inspired quality attributes) that arise when we humans as integral components of the system. The goal is to leverage insights from the social sciences to build and evolve societal-scale software systems that consider qualities such as bias and influence.
  • Engineering AI-enabled Software Systems . AI-enabled systems, which are software-reliant systems that include AI and non-AI components, have some inherently different characteristics than those without AI. However, AI-enabled systems are, above all, a type of software system. These systems have many parallels with the development and sustainment of more conventional software-reliant systems. This research area focuses on exploring which existing software engineering practices can reliably support the development of AI systems, as well as identifying and augmenting software engineering techniques for the specification, design, architecture, analysis, deployment, and sustainment of systems with AI components.
  • Engineering Quantum Computing Systems . Advances in software engineering for quantum are as important as the hardware advances. The goals of this research area are to first enable current quantum computers so they can be programmed more easily and reliably, and then enable increasing abstraction as larger, fully fault-tolerant quantum computing systems become available. Eventually, it should be possible fully integrate these types of systems into a unified classical and quantum software development lifecycle.

Help Shape Our National Software Research Agenda

Along with the advisory board, our research team has examined future trends in the computing landscape and emerging technologies; conducted a series of expert interviews; and convened multiple workshops for broad engagement and diverse perspectives, including a workshop on Software Engineering Grand Challenges and Future Visions co-hosted with the Defense Advanced Research Projects Agency (DARPA) . This workshop brought together leaders in the software engineering research and development community to describe (1) important classes of future software-reliant systems and their associated software engineering challenges, and (2) research methods, tools, and practices that are needed to make those systems feasible. An upcoming SEI blog post will provide a synopsis of what was covered in this workshop.

Your feedback would be appreciated on the software engineering challenges and proposed research focus areas to help inform the National Agenda for Software Engineering Study. Please email [email protected] to send your thoughts and comments on the software engineering study & research roadmap or to volunteer as a potential reviewer of study drafts. Thank you.

Headshot of Anita Carleton.

Author Page

Digital library publications, send a message, more by the author, application of large language models (llms) in software engineering: overblown hype or disruptive change, october 2, 2023 • by ipek ozkaya , anita carleton , john e. robert , douglas schmidt (vanderbilt university), join the sei and white house ostp to explore the future of software and ai engineering, may 30, 2023 • by anita carleton , john e. robert , mark h. klein , douglas schmidt (vanderbilt university) , erin harper, software engineering as a strategic advantage: a national roadmap for the future, november 15, 2021 • by anita carleton , john e. robert , mark h. klein , erin harper, more in software engineering research and development, applying the sei sbom framework, february 5, 2024 • by carol woody, 10 benefits and 10 challenges of applying large language models to dod software acquisition, january 22, 2024 • by john e. robert , douglas schmidt (vanderbilt university), the latest work from the sei, january 15, 2024 • by douglas schmidt (vanderbilt university), the top 10 blog posts of 2023, january 8, 2024 • by douglas schmidt (vanderbilt university), applying generative ai to software engineering: navigating ethical and educational landscapes, december 11, 2023 • by john e. robert , douglas schmidt (vanderbilt university), get updates on our latest work..

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

IT & Computer Science Research Topics

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

  • Algorithms & data structures
  • Artificial intelligence ( AI )
  • Computer networking
  • Database systems
  • Human-computer interaction
  • Information security (IS)
  • Software engineering
  • Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

  • An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
  • A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
  • An evaluation of machine learning algorithms used for recommender systems in streaming services
  • A review of approximation algorithm approaches for solving NP-hard problems
  • An analysis of parallel algorithms for high-performance computing of genomic data
  • The influence of data structures on optimal algorithm design and performance in Fintech
  • A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
  • A comparison of streaming algorithm performance for the detection of elephant flows
  • A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
  • Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
  • Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
  • A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
  • A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
  • Exploring the feasibility of applying information theory to feature extraction in retail datasets.
  • Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

  • Applying deep learning algorithms for speech recognition in speech-impaired children
  • A review of the impact of artificial intelligence on decision-making processes in stock valuation
  • An evaluation of reinforcement learning algorithms used in the production of video games
  • An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
  • An analysis of the ethical and social implications of artificial intelligence-based automated marking
  • The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
  • An examination of the use of artificial intelligence in orthopaedic surgery
  • The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
  • An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
  • A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Research topic evaluator

Topics & Ideas: Networking

  • An analysis of the impact of 5G technology on internet penetration in rural Tanzania
  • Assessing the role of software-defined networking (SDN) in modern cloud-based computing
  • A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
  • Exploring the influence of cloud computing on security risks in fintech.
  • An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
  • Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
  • An evaluation of the challenges and opportunities in 6G wireless network adoption
  • The role of network congestion control algorithms in improving network performance on streaming platforms
  • An analysis of network coding-based approaches for data security
  • Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Free Webinar: How To Find A Dissertation Research Topic

Topics & Ideas: Database Systems

  • An analysis of big data management systems and technologies used in B2B marketing
  • The impact of NoSQL databases on data management and analysis in smart cities
  • An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
  • Exploring the role of data warehousing and business intelligence in global consultancies
  • An analysis of the use of graph databases for data modelling and analysis in recommendation systems
  • The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
  • An examination of the challenges and opportunities of distributed databases in supply chain management
  • Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
  • An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
  • Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

  • An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
  • An exploration of how artificial intelligence is changing human-computer interaction patterns in children
  • An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
  • Assessing the influence of virtual and augmented reality on consumer purchasing patterns
  • An examination of the use of gesture-based interfaces in architecture
  • Exploring the impact of ease of use in wearable technology on geriatric user
  • Evaluating the ramifications of gamification in the Metaverse
  • A systematic review of user experience (UX) design advances associated with Augmented Reality
  • A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
  • Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Research Topic Kickstarter - Need Help Finding A Research Topic?

Topics & Ideas: Information Security

  • A bibliometric review of current trends in cryptography for secure communication
  • An analysis of secure multi-party computation protocols and their applications in cloud-based computing
  • An investigation of the security of blockchain technology in patient health record tracking
  • A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
  • A systematic review of secure data storage solutions used for cloud computing in the fintech industry
  • An analysis of intrusion detection and prevention systems used in the healthcare sector
  • Assessing security best practices for IoT devices in political offices
  • An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
  • A comparative study of digital signature schemes adoption in property transfers
  • An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

  • A study of agile software development methodologies and their impact on project success in pharmacology
  • Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
  • A study of the impact of DevOps practices on software development and delivery in the healthcare sector
  • An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
  • A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
  • An investigation of software testing techniques and methodologies for subscription-based offerings
  • A review of software security practices and techniques for protecting against phishing attacks from social media
  • An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
  • Exploring the impact of software development outsourcing on project success in multinational contexts
  • An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

  • An array-based optimization framework for query processing and data analytics (Chen, 2021)
  • Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
  • Embedding constructural documentation in unit tests (Nassif, 2019)
  • PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
  • Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
  • Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
  • Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
  • Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
  • Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
  • Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
  • Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
  • A state-based approach to context modeling and computing (Yue, 2019)
  • A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
  • HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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  • CAREER Q&A
  • 31 May 2022

Why science needs more research software engineers

  • Chris Woolston 0

Chris Woolston is a freelance writer in Billings, Montana.

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Paul Richmond poses for a portrait in his garden

Paul Richmond is a research software engineer in the United Kingdom. Credit: Shelley Richmond

In March 2012, a group of like-minded software developers gathered at the University of Oxford, UK, for what they called the Collaborations Workshop. They had a common vocation — building code to support scientific research — but different job titles. And they had no clear career path. The attendees coined a term to describe their line of work: research software engineer (RSE).

A decade later, RSE societies have sprung up in the United Kingdom, mainland Europe, Australia and the United States. In the United Kingdom, at least 31 universities have their own RSE groups, a sign of the growing importance of the profession, says Paul Richmond, an RSE group leader at the University of Sheffield and a past president of the country’s Society of Research Software Engineering. Nature spoke with Richmond about life as an RSE, the role of software in the research enterprise and the state of the field as it reaches its tenth anniversary.

What do RSEs do?

Fundamentally, RSEs build software to support scientific research. They generally don’t have research questions of their own — they develop the computer tools to help other people to do cool things. They might add features to existing software, clear out bugs or build something from scratch. But they don’t just sit in front of a computer and write code. They have to be good communicators who can embed themselves in a team.

What sorts of projects do they work on?

Almost every field of science runs on software, so an RSE could find themselves working on just about anything. In my career, I’ve worked on software for imaging cancer cells and modelling pedestrian traffic. As a postdoc, I worked on computational neuroscience. I don’t know very much about these particular research fields, so I work closely with the oncologists or neuroscientists or whomever to develop the software that’s needed.

Close up of multi-coloured code on a computer screen

Building code is just one part of the role of a research software engineer. Credit: Norman Posselt/Getty

Why do so many universities support their own RSE groups?

Some high-powered researchers at the top of the academic ladder can afford to hire their own RSE. That engineer might be dedicated to maintaining a single piece of software that’s been around for 10 or 20 years. But most research groups need — or can afford —an RSE only on an occasional basis. If their university has an RSE group, they can hire an in-house engineer for one day a week, or for a month at a time, or whatever they need. In that way, the RSE group is like a core facility. The university tries to ensure a steady workflow for the group, but that’s usually not a problem — there’s no shortage of projects to work on.

What else do RSEs do?

A big part of the job is raising awareness about the importance of quality software. An RSE might train a postdoc or graduate student to develop software on their own. Or they might run a seminar on good software practices. In theory, training 50 people could be more impactful than working on a single project. In practice, it’s often hard for RSEs to find the time for teaching, mentorship and advocacy because they’re so busy supporting research.

Do principal investigators (PIs) appreciate the need for RSEs?

It’s mixed. In the past, researchers weren’t always incentivized to use or create good software. But that’s changing. Many journals now require authors to publish code, and that code has to be FAIR: findable, accessible, interoperable and reproducible. That last term is very important: good software is a crucial component of research reproducibility. We explain to PIs that they need reliable code so they won’t have to retract their paper six months later.

Who should consider a career as an RSE?

Many RSEs started out as PhD students or postdocs who worked on software to support their own project. They realized that they enjoyed that part of the job more than the actual research. RSEs certainly have the skills to work in industry but they thrive in an environment of cutting-edge science in academia.

Most RSEs have a PhD — I have a PhD in computer graphics — but that’s not necessarily a requirement. Some RSEs end up on the tenure track; I was recently promoted to professor. Many others work as laboratory technicians or service staff. I would encourage any experienced developers with an interest in research to consider RSE as a career. I would also love to see more people from under-represented groups join the field. We need more diversity going forward.

What’s your advice for RSE hopefuls?

Try working on a piece of open-source software. If possible, do some training in a collaborative setting. If you have questions, talk to a working RSE. Consider joining an association. The UK Society of Research Software Engineering is always happy to advise people about getting into the field or how to stand out in a job application. People in the United States can reach out to the US Research Software Engineer Association.

research software engineering topics

NatureTech hub

If you’re a PhD student or postdoc, give yourself a challenge: try to convince your supervisors or PI that they really need to embrace good software techniques. If you can change their minds, it’s a good indication that you have the passion and drive to succeed.

What do you envision for the profession over the next 10 years?

I want to see RSEs as equals in the academic environment. Software runs through the entire research process, but professors tend to get most of the recognition and prestige. Pieces of software can have just as much impact as certain research papers, some of them much more so. If RSEs can get the recognition and rewards that they deserve, then the career path will be that much more visible and attractive.

doi: https://doi.org/10.1038/d41586-022-01516-2

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Identifying Non-Technical Skill Gaps in Software Engineering Education: What Experts Expect But Students Don’t Learn

As the importance of non-technical skills in the software engineering industry increases, the skill sets of graduates match less and less with industry expectations. A growing body of research exists that attempts to identify this skill gap. However, only few so far explicitly compare opinions of the industry with what is currently being taught in academia. By aggregating data from three previous works, we identify the three biggest non-technical skill gaps between industry and academia for the field of software engineering: devoting oneself to continuous learning , being creative by approaching a problem from different angles , and thinking in a solution-oriented way by favoring outcome over ego . Eight follow-up interviews were conducted to further explore how the industry perceives these skill gaps, yielding 26 sub-themes grouped into six bigger themes: stimulating continuous learning , stimulating creativity , creative techniques , addressing the gap in education , skill requirements in industry , and the industry selection process . With this work, we hope to inspire educators to give the necessary attention to the uncovered skills, further mitigating the gap between the industry and the academic world.

Opportunities and Challenges in Code Search Tools

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique used to build code search tools, and classified existing tools into focusing on supporting seven different search tasks. Based on our findings, we identified a set of outstanding challenges in existing studies and a research roadmap for future code search research.

Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines

A meaningful and deep understanding of the human aspects of software engineering (SE) requires psychological constructs to be considered. Psychology theory can facilitate the systematic and sound development as well as the adoption of instruments (e.g., psychological tests, questionnaires) to assess these constructs. In particular, to ensure high quality, the psychometric properties of instruments need evaluation. In this article, we provide an introduction to psychometric theory for the evaluation of measurement instruments for SE researchers. We present guidelines that enable using existing instruments and developing new ones adequately. We conducted a comprehensive review of the psychology literature framed by the Standards for Educational and Psychological Testing. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We provide an openly available example of a psychometric evaluation based on our guideline. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology. To improve the quality of behavioral research in SE, studies focusing on introducing, validating, and then using psychometric instruments need to be more common.

Towards an Anatomy of Software Craftsmanship

Context: The concept of software craftsmanship has early roots in computing, and in 2009, the Manifesto for Software Craftsmanship was formulated as a reaction to how the Agile methods were practiced and taught. But software craftsmanship has seldom been studied from a software engineering perspective. Objective: The objective of this article is to systematize an anatomy of software craftsmanship through literature studies and a longitudinal case study. Method: We performed a snowballing literature review based on an initial set of nine papers, resulting in 18 papers and 11 books. We also performed a case study following seven years of software development of a product for the financial market, eliciting qualitative, and quantitative results. We used thematic coding to synthesize the results into categories. Results: The resulting anatomy is centered around four themes, containing 17 principles and 47 hierarchical practices connected to the principles. We present the identified practices based on the experiences gathered from the case study, triangulating with the literature results. Conclusion: We provide our systematically derived anatomy of software craftsmanship with the goal of inspiring more research into the principles and practices of software craftsmanship and how these relate to other principles within software engineering in general.

On the Reproducibility and Replicability of Deep Learning in Software Engineering

Context: Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge. Objective: Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) reproducibility —whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and (2) replicability —whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a different experimental setup. We observed that DL studies commonly overlook these two factors and declare them as minor threats or leave them for future work. This is mainly due to high model complexity with many manually set parameters and the time-consuming optimization process, unlike classical supervised machine learning (ML) methods (e.g., random forest). This study aims to investigate the urgency and importance of reproducibility and replicability for DL studies on SE tasks. Method: In this study, we conducted a literature review on 147 DL studies recently published in 20 SE venues and 20 AI (Artificial Intelligence) venues to investigate these issues. We also re-ran four representative DL models in SE to investigate important factors that may strongly affect the reproducibility and replicability of a study. Results: Our statistics show the urgency of investigating these two factors in SE, where only 10.2% of the studies investigate any research question to show that their models can address at least one issue of replicability and/or reproducibility. More than 62.6% of the studies do not even share high-quality source code or complete data to support the reproducibility of their complex models. Meanwhile, our experimental results show the importance of reproducibility and replicability, where the reported performance of a DL model could not be reproduced for an unstable optimization process. Replicability could be substantially compromised if the model training is not convergent, or if performance is sensitive to the size of vocabulary and testing data. Conclusion: It is urgent for the SE community to provide a long-lasting link to a high-quality reproduction package, enhance DL-based solution stability and convergence, and avoid performance sensitivity on different sampled data.

Predictive Software Engineering: Transform Custom Software Development into Effective Business Solutions

The paper examines the principles of the Predictive Software Engineering (PSE) framework. The authors examine how PSE enables custom software development companies to offer transparent services and products while staying within the intended budget and a guaranteed budget. The paper will cover all 7 principles of PSE: (1) Meaningful Customer Care, (2) Transparent End-to-End Control, (3) Proven Productivity, (4) Efficient Distributed Teams, (5) Disciplined Agile Delivery Process, (6) Measurable Quality Management and Technical Debt Reduction, and (7) Sound Human Development.

Software—A New Open Access Journal on Software Engineering

Software (ISSN: 2674-113X) [...]

Improving bioinformatics software quality through incorporation of software engineering practices

Background Bioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software. Methodology A systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software. Results The findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers. Conclusions While strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.

Inter-team communication in large-scale co-located software engineering: a case study

AbstractLarge-scale software engineering is a collaborative effort where teams need to communicate to develop software products. Managers face the challenge of how to organise work to facilitate necessary communication between teams and individuals. This includes a range of decisions from distributing work over teams located in multiple buildings and sites, through work processes and tools for coordinating work, to softer issues including ensuring well-functioning teams. In this case study, we focus on inter-team communication by considering geographical, cognitive and psychological distances between teams, and factors and strategies that can affect this communication. Data was collected for ten test teams within a large development organisation, in two main phases: (1) measuring cognitive and psychological distance between teams using interactive posters, and (2) five focus group sessions where the obtained distance measurements were discussed. We present ten factors and five strategies, and how these relate to inter-team communication. We see three types of arenas that facilitate inter-team communication, namely physical, virtual and organisational arenas. Our findings can support managers in assessing and improving communication within large development organisations. In addition, the findings can provide insights into factors that may explain the challenges of scaling development organisations, in particular agile organisations that place a large emphasis on direct communication over written documentation.

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Study examined AI and SE transdisciplinarity to find ways of aligning them to enable development of AI-SE transdisciplinary theory. Literature review and analysis method was used. The findings are AI and SE transdisciplinarity is tacit with islands within and between them that can be linked to accelerate their transdisciplinary orientation by codification, internally developing and externally borrowing and adapting transdisciplinary theories. Lack of theory has been identified as the major barrier toward towards maturing the two disciplines as engineering disciplines. Creating AI and SE transdisciplinary theory would contribute to maturing AI and SE engineering disciplines.  Implications of study are transdisciplinary theory can support mode 2 and 3 AI and SE innovations; provide an alternative for maturing two disciplines as engineering disciplines. Study’s originality it’s first in SE, AI or their intersections.

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Our mission is to make everyone a programmer and maximize the productivity of every programmer. This will democratize computing to empower every person and every organization to achieve more. We achieve our vision through open-ended fundamental research in programming languages, software engineering, and automated reasoning. We strongly believe in pushing our research to its logical extreme to positively impact people’s lives.

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Software Engineer Research Paper Topics 2021: Top 5

research software engineering topics

Whether you’re studying in advance or you’re close to getting that Software Engineering degree, it’s crucial that you look for possible research paper topics in advance. This will help you have an advantage in your course.

First off, remember that software engineering revolves around tech development and improvement.

Hence, your research paper should have the same goal. It shouldn’t be too complex so that you can go through it smoothly. At the same time, it shouldn’t be too easy to the point that it can be looked up online.

Choosing can be a difficult task. Students are often choosing buy assignment from a professional writer because of the wrong topic choice. Thus, to help you land on the best topic for your needs, we have listed the top 5 software engineer research paper topics in the next sections.

Machine Learning

Machine learning is one of the most used research topics of software engineers. If you’re not yet familiar with this, it’s a field that revolves around producing programs that improve its algorithm on its own just by the use of existing data and experience.

Basically, the art of machine learning aims to make intelligent tools. Here, you will need to use various statistical methods for your computers’ algorithms. This somehow makes it a complex and long topic.

Even so, the good thing about the said field is it covers a lot of subtopics. These can include using machine learning for face spoof detection, iris detection, sentiment analysis technique, and likes. Usually, though, machine learning will go hand in hand with certain detection systems.

Artificial Intelligence

Artificial Intelligence is a much easier concept than machine learning. Note, though, that the latter is just another type of AI tool.

AI refers to the human-like intelligence integrated into machines and computer programs. Focusing on this will give you much more topics to write about. Since it’s present in a lot of fields like gaming, marketing, and even random automated tasks, you will have more materials to refer to.

Some things that you can write about in your paper include AI’s relationship with software engineering, robotics, and natural processing. You can also write about the different types of artificial intelligence tools for a more guided research paper.

Internet Of Things

Another topic that you can write about is the Internet of Things, or more commonly known as IoT . This refers to interconnected devices, machines, or even living beings as long as a network exists.

Writing about IoT will open a huge array of possibilities to write about. You can talk about whether the topic is a problem that needs additional solutions or improvements. At the same time, you will be able to talk about specific machine requirements since IoT works mainly with communication servers.

In addition, the concept of the Internet of Things is also used in several fields like agriculture, e-commerce, and medicine. Because of this, you can rest assured that you won’t run out of things to talk about or refer to.

Software Development Models

Next up, we have software development models. If you want to write about a research paper(or maybe you decided to purchase custom research paper ?) relating to how one can start building an app or software, then using software development models as a topic is a good choice.

Here, you can choose to write about what the concept is or delve deeper into its different types. You can look into the Waterfall Model, V-Model, Incremental, RAD, Agile, Iterative, Spiral, and Prototype. You can choose either one or all of the models and then relate them to software engineering.

Clone Management

One of the most important elements in software engineering is the clone base. Hence, using this as a research topic will help you stay relevant to your course and its needs. In particular, you can focus on clone management.

Clone management is a task that revolves around ensuring that a database is free from error and duplicated codes. What makes this a good topic is its materials are still limited in the field of software engineering. This is compared to other clone-related topics. Hence, you can ensure a distinct topic for your paper.

To land on the best topic, take your interest into account. Look for the field that makes you curious and entertained. In this way, you can build motivation to actually know more about it, and not just for the sake of submitting.

Another good tip is to choose a unique topic. The ones we discussed above can be considered unique since they are some of the latest software-related topics. If you’re going to use a common one, then make sure that you put your own little twist to it. You can also consider seeing the topic in a different light.

Anyhow, your research paper, its grade, and overall quality will greatly depend on what you choose to write about.

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research software engineering topics

Research Topics in Software Engineering

research software engineering topics

This seminar is an opportunity to become familiar with current research in software engineering and more generally with the methods and challenges of scientific research.

Each student will be asked to study some papers from the recent software engineering literature and review them. This is an exercise in critical review and analysis. Active participation is required (a presentation of a paper as well as participation in discussions).

The aim of this seminar is to introduce students to recent research results in the area of programming languages and software engineering. To accomplish that, students will study and present research papers in the area as well as participate in paper discussions. The papers will span topics in both theory and practice, including papers on program verification, program analysis, testing, programming language design, and development tools.

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📚 A curated list of papers for Software Engineers

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Folders and files, repository files navigation, papers for software engineers.

A curated list of papers that may be of interest to Software Engineering students or professionals. See the sources and selection criteria below.

Von Neumann's First Computer Program. Knuth (1970) . Computer History; Early Programming

  • The Education of a Computer. Hopper (1952) .
  • Recursive Programming. Dijkstra (1960) .
  • Programming Considered as a Human Activity. Dijkstra (1965) .
  • Goto Statement Considered Harmful. Dijkstra (1968) .
  • Program development by stepwise refinement. Wirth (1971) .
  • The Humble Programmer. Dijkstra (1972) .
  • Computer Programming as an Art. Knuth (1974) .
  • The paradigms of programming. Floyd (1979) .
  • Literate Programming. Knuth (1984) .

Computing Machinery and Intelligence. Turing (1950) . Early Artificial Intelligence

  • Some Moral and Technical Consequences of Automation. Wiener (1960) .
  • Steps towards Artificial Intelligence. Minsky (1960) .
  • ELIZA—a computer program for the study of natural language communication between man and machine. Weizenbaum (1966) .
  • A Theory of the Learnable. Valiant (1984) .

A Method for the Construction of Minimum-Redundancy Codes. Huffman (1952) . Information Theory

  • A Universal Algorithm for Sequential Data Compression. Ziv, Lempel (1977) .
  • Fifty Years of Shannon Theory. Verdú (1998) .

Engineering a Sort Function. Bentley, McIlroy (1993) . Data Structures; Algorithms

  • On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Kruskal (1956) .
  • A Note on Two Problems in Connexion with Graphs. Dijkstra (1959) .
  • Quicksort. Hoare (1962) .
  • Space/Time Trade-offs in Hash Coding with Allowable Errors. Bloom (1970) .
  • The Ubiquitous B-Tree. Comer (1979) .
  • Programming pearls: Algorithm design techniques. Bentley (1984) .
  • Programming pearls: The back of the envelope. Bentley (1984) .
  • Making data structures persistent. Driscoll et al (1986) .

A Design Methodology for Reliable Software Systems. Liskov (1972) . Software Design

  • On the Criteria To Be Used in Decomposing Systems into Modules. Parnas (1971) .
  • Information Distribution Aspects of Design Methodology. Parnas (1972) .
  • Designing Software for Ease of Extension and Contraction. Parnas (1979) .
  • Programming as Theory Building. Naur (1985) .
  • Software Aging. Parnas (1994) .
  • Towards a Theory of Conceptual Design for Software. Jackson (2015) .

Programming with Abstract Data Types. Liskov, Zilles (1974) . Abstract Data Types; Object-Oriented Programming

  • The Smalltalk-76 Programming System Design and Implementation. Ingalls (1978) .
  • A Theory of Type Polymorphism in Programming. Milner (1978) .
  • On understanding types, data abstraction, and polymorphism. Cardelli, Wegner (1985) .
  • SELF: The Power of Simplicity. Ungar, Smith (1991) .

Why Functional Programming Matters. Hughes (1990) . Functional Programming

  • Recursive Functions of Symbolic Expressions and Their Computation by Machine. McCarthy (1960) .
  • The Semantics of Predicate Logic as a Programming Language. Van Emden, Kowalski (1976) .
  • Can Programming Be Liberated from the von Neumann Style? Backus (1978) .
  • The Semantic Elegance of Applicative Languages. Turner (1981) .
  • The essence of functional programming. Wadler (1992) .
  • QuickCheck: A Lightweight Tool for Random Testing of Haskell Programs. Claessen, Hughes (2000) .
  • Church's Thesis and Functional Programming. Turner (2006) .

An Incremental Approach to Compiler Construction. Ghuloum (2006) . Language Design; Compilers

  • The Next 700 Programming Languages. Landin (1966) .
  • Programming pearls: little languages. Bentley (1986) .
  • The Essence of Compiling with Continuations. Flanagan et al (1993) .
  • A Brief History of Just-In-Time. Aycock (2003) .
  • LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation. Lattner, Adve (2004) .
  • A Unified Theory of Garbage Collection. Bacon, Cheng, Rajan (2004) .
  • A Nanopass Framework for Compiler Education. Sarkar, Waddell, Dybvig (2005) .
  • Bringing the Web up to Speed with WebAssembly. Haas (2017) .

No Silver Bullet: Essence and Accidents of Software Engineering. Brooks (1987) . Software Engineering; Project Management

  • How do committees invent? Conway (1968) .
  • Managing the Development of Large Software Systems. Royce (1970) .
  • The Mythical Man Month. Brooks (1975) .
  • On Building Systems That Will Fail. Corbató (1991) .
  • The Cathedral and the Bazaar. Raymond (1998) .
  • Out of the Tar Pit. Moseley, Marks (2006) .

Communicating sequential processes. Hoare (1978) . Concurrency

  • Solution Of a Problem in Concurrent Program Control. Dijkstra (1965) .
  • Monitors: An operating system structuring concept. Hoare (1974) .
  • On the Duality of Operating System Structures. Lauer, Needham (1978) .
  • Software Transactional Memory. Shavit, Touitou (1997) .

The UNIX Time- Sharing System. Ritchie, Thompson (1974) . Operating Systems

  • An Experimental Time-Sharing System. Corbató, Merwin Daggett, Daley (1962) .
  • The Structure of the "THE"-Multiprogramming System. Dijkstra (1968) .
  • The nucleus of a multiprogramming system. Hansen (1970) .
  • Reflections on Trusting Trust. Thompson (1984) .
  • The Design and Implementation of a Log-Structured File System. Rosenblum, Ousterhout (1991) .

A Relational Model of Data for Large Shared Data Banks. Codd (1970) . Databases

  • Granularity of Locks and Degrees of Consistency in a Shared Data Base. Gray et al (1975) .
  • Access Path Selection in a Relational Database Management System. Selinger et al (1979) .
  • The Transaction Concept: Virtues and Limitations. Gray (1981) .
  • The design of POSTGRES. Stonebraker, Rowe (1986) .
  • Rules of Thumb in Data Engineering. Gray, Shenay (1999) .

A Protocol for Packet Network Intercommunication. Cerf, Kahn (1974) . Networking

  • Ethernet: Distributed packet switching for local computer networks. Metcalfe, Boggs (1978) .
  • End-To-End Arguments in System Design. Saltzer, Reed, Clark (1984) .
  • An algorithm for distributed computation of a Spanning Tree in an Extended LAN. Perlman (1985) .
  • The Design Philosophy of the DARPA Internet Protocols. Clark (1988) .
  • TOR: The second generation onion router. Dingledine et al (2004) .
  • Why the Internet only just works. Handley (2006) .
  • The Network is Reliable. Bailis, Kingsbury (2014) .

New Directions in Cryptography. Diffie, Hellman (1976) . Cryptography

  • A Method for Obtaining Digital Signatures and Public-Key Cryptosystems. Rivest, Shamir, Adleman (1978) .
  • How To Share A Secret. Shamir (1979) .
  • A Digital Signature Based on a Conventional Encryption Function. Merkle (1987) .
  • The Salsa20 family of stream ciphers. Bernstein (2007) .

Time, Clocks, and the Ordering of Events in a Distributed System. Lamport (1978) . Distributed Systems

  • Self-stabilizing systems in spite of distributed control. Dijkstra (1974) .
  • The Byzantine Generals Problem. Lamport, Shostak, Pease (1982) .
  • Impossibility of Distributed Consensus With One Faulty Process. Fisher, Lynch, Patterson (1985) .
  • Implementing Fault-Tolerant Services Using the State Machine Approach: A Tutorial. Schneider (1990) .
  • Practical Byzantine Fault Tolerance. Castro, Liskov (1999) .
  • Paxos made simple. Lamport (2001) .
  • Paxos made live - An Engineering Perspective. Chandra, Griesemer, Redstone (2007) .
  • In Search of an Understandable Consensus Algorithm. Ongaro, Ousterhout (2014) .

Designing for Usability: Key Principles and What Designers Think. Gould, Lewis (1985) . Human-Computer Interaction; User Interfaces

  • As We May Think. Bush (1945) .
  • Man-Computer symbiosis. Licklider (1958) .
  • Some Thoughts About the Social Implications of Accessible Computing. David, Fano (1965) .
  • Tutorials for the First-Time Computer User. Al-Awar, Chapanis, Ford (1981) .
  • The star user interface: an overview. Smith, Irby, Kimball (1982) .
  • Design Principles for Human-Computer Interfaces. Norman (1983) .
  • Human-Computer Interaction: Psychology as a Science of Design. Carroll (1997) .

The anatomy of a large-scale hypertextual Web search engine. Brin, Page (1998) . Information Retrieval; World-Wide Web

  • A Statistical Interpretation of Term Specificity in Retrieval. Spärck Jones (1972) .
  • World-Wide Web: Information Universe. Berners-Lee et al (1992) .
  • The PageRank Citation Ranking: Bringing Order to the Web. Page, Brin, Motwani (1998) .

Dynamo, Amazon’s Highly Available Key-value store. DeCandia et al (2007) . Internet Scale Data Systems

  • The Google File System. Ghemawat, Gobioff, Leung (2003) .
  • MapReduce: Simplified Data Processing on Large Clusters. Dean, Ghemawat (2004) .
  • Bigtable: A Distributed Storage System for Structured Data. Chang et al (2006) .
  • ZooKeeper: wait-free coordination for internet scale systems. Hunt et al (2010) .
  • The Hadoop Distributed File System. Shvachko et al (2010) .
  • Kafka: a Distributed Messaging System for Log Processing. Kreps, Narkhede, Rao (2011) .
  • CAP Twelve Years Later: How the "Rules" Have Changed. Brewer (2012) .
  • Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases. Verbitski et al (2017) .

On Designing and Deploying Internet Scale Services. Hamilton (2007) . Operations; Reliability; Fault-tolerance

  • Ironies of Automation. Bainbridge (1983) .
  • Why do computers stop and what can be done about it? Gray (1985) .
  • Recovery Oriented Computing (ROC): Motivation, Definition, Techniques, and Case Studies. Patterson et al (2002) .
  • Crash-Only Software. Candea, Fox (2003) .
  • Building on Quicksand. Helland, Campbell (2009) .

Thinking Methodically about Performance. Gregg (2012) . Performance

  • Performance Anti-Patterns. Smaalders (2006) .
  • Thinking Clearly about Performance. Millsap (2010) .

Bitcoin, A peer-to-peer electronic cash system. Nakamoto (2008) . Crytpocurrencies

  • Ethereum: A Next-Generation Smart Contract and Decentralized Application Platform. Buterin (2014) .

A Few Useful Things to Know About Machine Learning. Domingos (2012) . Machine Learning

  • Statistical Modeling: The Two Cultures. Breiman (2001) .
  • The Unreasonable Effectiveness of Data. Halevy, Norvig, Pereira (2009) .
  • ImageNet Classification with Deep Convolutional Neural Networks. Krizhevsky, Sutskever, Hinton (2012) .
  • Playing Atari with Deep Reinforcement Learning. Mnih et al (2013) .
  • Generative Adversarial Nets. Goodfellow et al (2014) .
  • Deep Learning. LeCun, Bengio, Hinton (2015) .
  • Attention Is All You Need. Vaswani et al (2017) .
  • Von Neumann's First Computer Program. Knuth (1970) .
  • Computing Machinery and Intelligence. Turing (1950) .
  • A Method for the Construction of Minimum-Redundancy Codes. Huffman (1952) .
  • Engineering a Sort Function. Bentley, McIlroy (1993) .
  • A Design Methodology for Reliable Software Systems. Liskov (1972) .
  • Programming with Abstract Data Types. Liskov, Zilles (1974) .
  • Why Functional Programming Matters. Hughes (1990) .
  • An Incremental Approach to Compiler Construction. Ghuloum (2006) .
  • No Silver Bullet: Essence and Accidents of Software Engineering. Brooks (1987) .
  • Communicating sequential processes. Hoare (1978) .
  • The UNIX Time- Sharing System. Ritchie, Thompson (1974) .
  • A Relational Model of Data for Large Shared Data Banks. Codd (1970) .
  • A Protocol for Packet Network Intercommunication. Cerf, Kahn (1974) .
  • New Directions in Cryptography. Diffie, Hellman (1976) .
  • Time, Clocks, and the Ordering of Events in a Distributed System. Lamport (1978) .
  • Designing for Usability: Key Principles and What Designers Think. Gould, Lewis (1985) .
  • The anatomy of a large-scale hypertextual Web search engine. Brin, Page (1998) .
  • Dynamo, Amazon’s Highly Available Key-value store. DeCandia et al (2007) .
  • On Designing and Deploying Internet Scale Services. Hamilton (2007) .
  • Thinking Methodically about Performance. Gregg (2012) .
  • Bitcoin, A peer-to-peer electronic cash system. Nakamoto (2008) .
  • A Few Useful Things to Know About Machine Learning. Domingos (2012) .

This list was inspired by (and draws from) several books and paper collections:

  • Papers We Love
  • Ideas That Created the Future
  • The Innovators
  • The morning paper
  • Distributed systems for fun and profit
  • Readings in Database Systems (the Red Book)
  • Fermat's Library
  • Classics in Human-Computer Interaction
  • Awesome Compilers
  • Distributed Consensus Reading List
  • The Decade of Deep Learning

A few interesting resources about reading papers from Papers We Love and elsewhere:

  • Should I read papers?
  • How to Read an Academic Article
  • How to Read a Paper. Keshav (2007) .
  • Efficient Reading of Papers in Science and Technology. Hanson (1999) .
  • On ICSE’s “Most Influential Papers”. Parnas (1995) .

Selection criteria

  • The idea is not to include every interesting paper that I come across but rather to keep a representative list that's possible to read from start to finish with a similar level of effort as reading a technical book from cover to cover.
  • I tried to include one paper per each major topic and author. Since in the process I found a lot of noteworthy alternatives, related or follow-up papers and I wanted to keep track of those as well, I included them as sublist items.
  • The papers shouldn't be too long. For the same reasons as the previous item, I try to avoid papers longer than 20 or 30 pages.
  • They should be self-contained and readable enough to be approachable by the casual technical reader.
  • They should be freely available online.
  • Examples of this are classic works by Von Neumann, Turing and Shannon.
  • That being said, where possible I preferred the original paper on each subject over modern updates or survey papers.
  • Similarly, I tended to skip more theoretical papers, those focusing on mathematical foundations for Computer Science, electronic aspects of hardware, etc.
  • I sorted the list by a mix of relatedness of topics and a vague chronological relevance, such that it makes sense to read it in the suggested order. For example, historical and seminal topics go first, contemporary internet-era developments last, networking precedes distributed systems, etc.

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Latest Thesis and Research Topics in Software Engineering

Unique software engineering research topics for students.

more software engineers are needed as a result of the growing reliance on technology in both personal and professional spheres of life. Software engineering research topics are essential for solving complicated issues, increasing productivity, and fostering innovation. While software engineering is so important, it is equally difficult for students to get their degree in Software engineering.

Being said that many students struggle to keep up academically because software engineering is one of the most desired degrees. The final year thesis or dissertation is the most challenging assignment; many students are on the edge of losing their minds over it. While writing a thesis is one duty, coming up with an original and creative software engineering research topic is the first and most challenging step. Students with their assignments and activities don’t have enough time or energy to build a topic that is exactly right for them, finding a topic that is feasible and corresponds with your interests requires a lot of effort.

However this issue can be resolved as our PhD experts can provide you with well researched software engineering dissertation topics . We have plenty of topics for you to choose from mentioned below, and even if you don’t find anything according to your interests here you can simply contact us and request your topics according to your requirements and our experts will get you a tailored software engineering thesis topic.

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An analysis of the undertaking of good outcome factors and difficulties in software engineering projects:, how “the research guardian” can help you a lot.

Our top thesis writing experts are available 24/7 to assist you the right university projects. Whether its critical literature reviews to complete your PhD. or Master Levels thesis.

Automated software testing and quality control:

The study aims to improve programming testing and quality control through the execution of mechanized testing methods.

Objectives:

  • To efficiently detect software defeat and ensure complete test coverage, create an automated testing framework.
  • To determine which automated testing frameworks and tools are best suited to software development.
  • To analyze key metrics, and contrast them with the manual testing method to investigate the effects.

Impact of DevOps practices on software development:

The study aims to examine how DevOps practices affect software development productivity and efficiency.

  • To encourage cross-functional teams to collaborate, share information, and jointly advanced the development process.
  • To automate testing procedures like unit root tests, integration tests, and regression tests.
  • To change the activities for quality assurance and testing in the development process.

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Role of upgrading software security to enhance protection:

The aim of upgrading programming security through weakness identification and enhancing protection from possible breach

  • To find security flaws and weaknesses early on, employ, methods like vulnerability scanning, code reviews, and penetration testing.
  • To reduce the likelihood of being exploited, establish a procedure for resolving vulnerabilities as soon as possible.
  • To provide extensive security awareness and training programs, an organization can foster a security-conscious culture.

Adoption and effectiveness of continuous development:

The study aims to identify how effectively software engineering can be used for continuous development along with integration practices

  • To determine the benefit of implementing continuous deployment practices in numbers.
  • To evaluate the effect of computerizing the arrangement cycle, including code joining, testing, and delivery to the executive.
  • To analyze the impact of continuous integration practices on software development lifecycle enhancement.
  • To analyze how team communication and collaboration are affected by adopting software engineering practices and continuous development.

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Planning and assess client-driven approaches in software programming:

The study aims to plan and assess client driven approaches to programing necessities and designing.

  • To identify the beneficial client-driven approaches necessary for programming and designing.
  • To ensure the successful implementation of these approaches in an organization.
  • To investigate the outcomes of these approaches in the success or failure of an organization.

Analyzing software metrics and their applications:

The study aims to analyze software metrics and their application to predictive software quality assurance.

  • To evaluate a comprehensive set of software metrics that can shed light on software product quality.
  • To create predictive models that make use of the software metrics that have been identified to predict potential risk and quality issues.
  • To compare the predictions made by the predictive models to actual software quality outcomes.

Applying Block chain Innovation:

The study aims to investigate how the distinctive characteristics of Block chain technology can be used to enhance software development and deployment process

  • To assess the potential use cases and advantages of coordinating block chain innovation into the product advancement lifecycle.
  • To investigate the application of block chain for transparent deployment histories, and decentralized package management.
  • To influence block chain’s straightforwardness to work with reviewing and consistence process in programming advancement.

Investigation of augmented and Virtual Reality into Software Engineering Methods and Tools:

The study aims to deeply analyse the integration of Augmented and Virtual Reality into Software Engineering Methods and tools to enhance the efficiency

  • To measure the impact of the integration of AR and VR technologies on software engineering
  • To examine the practical and technical obstacles to incorporate to incorporating augmented reality and virtual reality into existing software engineering techniques and tools.
  • To analyze existing frameworks and solution that make it possible to integrate AR and VR Software.

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  • Computer Science

Software and Systems Engineering

Research on software and systems engineering studies the application of engineering to the design, development, assurance, management, and maintenance of software-reliant systems over their life cycles.  at vanderbilt, this research encompasses multiple topics including cyber-security, data-centricity, human-computer interaction, human-systems interaction, middleware frameworks, mobile cloud computing applications and systems, model-integrated computing, and software patterns ..

Robotics, Artificial Intelligence and Computer Graphics

ANIRUDDHA (ANDY) GOKHALE

Associate Professor of Computer Science and Computer Engineering

Aniruddha Gokhale

GABOR KARSAI

Professor of Electrical Engineering, Computer Science and Computer Engineering Associate Director of the Institute of Software Integrated Systems

Gabor Karsai - Software and Systems Engineering

This research is being supported by the National Science Foundation and various government research programs. The project is focusing on cyber-physical systems (CPS) where resilience and dependability are critical due to the interactions with the physical environment. The key to building more sophisticated and complex yet more resilient and dependable systems is clearly design automation and engineering tools. The project uses and develops model-integrated computing techniques, including tools for the model-driven design, analysis, verification, implementation, operation, and maintenance of resilient CPS.

DOUGLAS SCHMIDT

Doug Schmidt - Software and Systems Engineering

Dr. Douglas C. Schmidt's research covers a wide range of software-related topics, including patterns, optimization techniques, and empirical analyses of object-oriented middleware frameworks that facilitate the development of mobile cloud computing applications running over data networks. Schmidt has more than 20 years of experience leading the development of ACE, TAO , CIAO and CoSMIC, which are widely used, open-source middleware frameworks and model-driven engineering tools that implement patterns and product-line architectures for open systems. The middleware platforms and modeling tools developed by Schmidt and his colleagues at ISIS constitute some of the most successful examples of software R&D ever transitioned from research to industry.

JULES WHITE

Assistant Professor of Computer Science

Jules White - Software and Systems Engineering

He was previously a faculty member in Electrical and Computer Engineering at Virginia Tech and won the Outstanding New Assistant Professor Award at Virginia Tech. His research has won 4 Best Paper Awards. He has also published over 95 papers. His research has been licensed and transitioned to industry where it has received over $13.5 million in venture backing and won an Innovation Award at CES 2013, attended by over 150,000 people, was a finalist for the Technical Achievement at Award at SXSW Interactive, and was a top 3 for mobile in the Accelerator Awards at SXSW 2013. His research is conducted through the Mobile Application computinG, optimizatoN, and secUrity Methods (MAGNUM) Group at Vanderbilt University, which he directs ( http://magnum.io ).

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Robotics Software Engineering

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About this Research Topic

Increasingly, challenging domains employ robotic applications. Yet, Robotics still is one of the most challenging domains for software engineering. Deploying robotics applications requires integrating solutions from experts in various domains, including navigation, path planning, manipulation, localization, human-robot interaction, etc. Integration of modules contributed by respective domain experts is one of the key challenges in engineering software-centric systems, yet only one of the cross-cutting software concerns crucial to robotics. As robots often operate in dynamic, partially observable environments additional challenges include adaptability, robustness, safety, and security. The goal of this Research Topic is to bring together researchers with practitioners to identify new frontiers in robotics software engineering, discuss challenges raised by real-world applications, and transfer the latest insights from research to industry. This Research Topic welcomes contributions from both academic and industrial participants, thus fostering active synergy between the two communities. We seek contributions addressing, but not limited to, the following topics related to robotics software engineering: • Analysis of challenges in robotic software engineering • Architectures that lead to reusable robotic software • Challenges for defining and integrating domain-specific languages for the design of robotic systems • Continuous integration and deployment in robotics • Identification and analysis of design principles promoting quality of service (e.g., performance, energy efficiency) • Engineering the collaboration of multiple (heterogeneous) robots • Machine learning for safety-critical robotic systems • Metrics to measure non-functional properties (e.g., robustness, availability, etc.) and their application in robotic software • Best practices in engineering robotic software • Variability, modularity, and reusability in robotic software • Validation and verification of robotic software • Processes and tools supporting the engineering and development of robotic systems • State-of-the-art research projects, innovative ideas, and field-based studies in robotic software engineering • Lessons learned in the engineering and deployment of large-scale, real-world integrated robot Dr Hoffmann is affiliated with XITASO GmbH IT & Software Solutions. All other Topic Editors declare no competing interests with regard to the Research Topic subject. This Research Topic is linked with the 5th international workshop on Robotics Software Engineering (RoSE 2023) colocated with the 45th International Conference on Software Engineering (ICSE 2023). Any contributions presented at the workshop must be extended to contain 30% original content. However, this Research Topic welcomes relevant contributions that were not presented at the workshop.

Keywords : Robotics, Software engineering, Robot, Operating System, ROS, Software architecture, Testing, Program analysis, Software design, Model-driven engineering

Important Note : All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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XP 2024 | Bolzano, Italy

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Call for Submissions – Research Workshops

Ai for agile software engineering (ai4ase), 25th international conference on agile software development june 4-7, 2024 • bolzano, italy.

  • Call for Submissions
  • 25 Years of XP – Special Track
  • Research Papers Track
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  • Legacy, Architecture Modernisation and Technical Debt
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Important Dates

  • Workshop paper submissions: April 5, 2024
  • Workshop paper notifications: April 30, 2024
  • Camera-ready versions of accepted papers: TBD (post-conference date)

* All dates are defined as the end of the day anywhere on Earth (AoE). Please note that some dates may change.

Call and Topics of Interest

The workshop seeks to develop knowledge and build a network related to the impact that AI-enabled solutions (tools, models, methods, etc.) will have on Agile software engineering practices.

The workshop will be a venue and a platform to bring together researchers and practitioners to share ideas and insights, and to spur collaboration for further development and learning.

The workshop will consist of three segments:

  •  Introductions and keynotes highlighting recent advancements, serving as an inspiration for the workshop’s themes. Presenters include selected proposal authors and invited experts.
  • Guided discussions or Open Space forums, adapted based on audience size, focusing on main themes. Key points and conclusions will be documented for post-workshop summaries.
  • Developing a Research Agenda, inviting participants to contribute towards defining how AI impacts agile software engineering practices and research.

Within this scope, the workshop will cover themes including (but not limited to):

  • AI Integration in Agile Development (e.g., how Generative AI tools such as Copilot and GPT fit into existing practices)
  • Data-Driven Decision-Making in Agile (AI needs data, how can data from systems aid in agile decision making, e.g., on team level, in agile transformation)
  • AI in Agile Project Management (e.g., how can AI augment, or aid scrum masters, product owners and product managers)
  • Ethical and Legal Considerations of AI in Software Engineering (e.g., concerns related to IP, AI energy footprint)
  • Collaborative Tools and Techniques in Agile Development (e.g., use of collaboration tools, practices for sharing, and coordinating in the age of AI)
  • Future Trends in AI and Agile Methodologies (e.g., new methods for SE, prompt engineering vis-a-vis coding, agile prompt engineering methods) High quality submissions to the workshop can potentially be invited to a special issue on AI4SE in IST.

Submission Guidelines

We invite contributions in the form of papers and/or presentations.

Papers can be up to 8 pages. 

All submissions will be peer-reviewed. 

Topics should align with the workshop themes, emphasizing novel research, case studies, practical experiences, or new and exciting ideas. 

Submissions should provide value to both academic and/or industry participants.

There is no limit on the number of submissions an author may submit, but authors are advised to focus on quality rather than quantity.

All submissions must conform to the LNBIP formatting and submission instructions. Read the instructions for authors here.

If the submission is accepted, at least one of the authors needs to attend the conference, as the workshop will run in person.

All submissions must be done electronically via EasyChair by the defined deadline.

The XP 2024 workshops conference proceedings will be published by Springer in the Lecture Notes in Business Information Processing (LNBIP) as Open Access.

Submissions will be screened on rigor and relevance and then evaluated by program committee members based on soundness, significance, novelty, verifiability, and presentation quality.

For more information, please contact the Workshop Chair, Dr. Astri Barbala ( email ).

research software engineering topics

Program Committee Members (TBC)

  • Prof. Darja Smite (Blekinge Institute of Technology, SE) 
  • Prof. Torgeir Dingsøyr (Norwegian University of Science, NO) 
  • Prof. Daniel Russo (Aalborg University, DE) 
  • Dr. Eriks Klotins (Blekinge Institute of Technology, SE) 
  • Prof. Robert Feldt (University of Gothenburg, SE) 
  • Dr. Ken Power (Software Engineering Director, Motional)

If you have any questions or comments about a specific workshop, please contact the organizers  above.

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HKUST Prof. LAU Kei May Elected to the US National Academy of Engineering | The Hong Kong University of Science and Technology

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HKUST Prof. LAU Kei May Elected to the US National Academy of Engineering

Prof. Lau Kei May

Prof. Lau Kei May is elected to the US National Academy of Engineering in recognition of “her contributions to photonics and electronics based on III-V semiconductors on silicon”.

The Hong Kong University of Science and Technology (HKUST) Prof. LAU Kei May, Professor Emerita of the Department of Electronic and Computer Engineering and Research Professor of the Division of Emerging Interdisciplinary Areas, has been elected as a member of the US National Academy of Engineering (NAE), one of the highest professional distinctions accorded to an engineer, the organization points out.

Prof. Lau was elected to the NAE in recognition of “her contributions to photonics and electronics based on III-V semiconductors on silicon”. She is the only Hong Kong scholar newly elected to the NAE this year, among a total of 114 new members and 21 international members. As one of the few world-renowned researchers in the electronics and optoelectronics technology sector, her research breakthroughs achieved throughout her career make her one of the few NAE members from Hong Kong, highlighting the world-class research led by her at HKUST earning accolades from esteemed peers worldwide. 

The NAE's membership honors those who have made outstanding contributions to “engineering research, practice or education - including significant contributions to the engineering literature”, and to the “pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing / implementing innovative approaches to engineering education.”

“I am deeply honored to have been elected to the US National Academy of Engineering,” said Prof. Lau. “This prestigious membership not only acknowledges my contributions to semiconductor materials and device but also opens more doors to connect researchers from my HKUST lab with esteemed leaders in the engineering profession, to push the boundaries of innovation and make a lasting impact on the field. HKUST’s unwavering commitment to encourage and empower researchers has played a pivotal role in my achievements during the past two decades. I am particularly grateful that many of my students and researchers are now carrying on the torch at universities, research institutions, and industry around the world.”

President Prof. Nancy IP was delighted with the news, “We extend our warmest congratulations to Prof. Lau on her election to the US National Academy of Engineering. This prestigious recognition is a testament to her outstanding contributions to the fields of photonics and electronics, as well as her exceptional dedication to research and innovation. Prof. Lau's groundbreaking research and her role as a trailblazer in her fields have brought immense pride to HKUST.  We hope her remarkable accomplishments will ignite a spark of inspiration for the young generation in Hong Kong to pursue excellence in science and engineering.” 

Prof. Lau was born and raised in Hong Kong, with her K-12 education at the Pui Ching Primary School and Middle School. She received her bachelor’s and master’s degrees in physics from the University of Minnesota, and a PhD degree in electrical engineering from Rice University. Her HKUST journey began in 1998 when she came to the University as a visiting professor while serving as a faculty member at the University of Massachusetts/Amherst. She joined HKUST’s regular faculty two years later and established the Photonics Technology Center. 

Over her tenure at HKUST spanning more than twenty years, Prof. Lau has dedicated to pursuing innovation and breakthroughs. She has worked on LED research and has revolutionized display technologies as her team developed and kick-started commercialization of monolithic micro-LED micro-displays that attracted tremendous attention in recent years as a promising technology to establish user interface for wearable/portable electronic devices such as augmented reality (AR) and virtual reality (VR) displays, portable projectors, and smart phones. 

Prof. Lau is also well-known for her work on integrating high-performance III-V compound devices onto industry-standard silicon substrates using Metalorganic Chemical Vapor Deposition (MOCVD). Her work has not only represented a significant milestone in the development of technologies critical to present-day energy-efficient and high-performance photonics and electronics, but also had a prominent impact on current high-speed and low power optoelectronic devices for ubiquitous internet communication.

She has also garnered numerous awards and distinctions over the years. She is a recipient of the IPRM award, IET J. J. Thomson Medal for Electronics, Optica Nick Holonyak Jr. Award, IEEE Photonics Society Aron Kressel Award, US National Science Foundation (NSF) Faculty Award for Women Scientists and Engineers, and Hong Kong Croucher Senior Research Fellowship. Prof. Lau is also a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Optica, and the Hong Kong Academy of Engineering Sciences (HKAES).

The National Academy of Engineering is a non-profit institution founded in 1964 which provides engineering leadership in service to the United States. It currently has 2,310 peer-elected members and 332 international members, who are among the world's most accomplished engineers in business, academia and government.

About The Hong Kong University of Science and Technology  The Hong Kong University of Science and Technology (HKUST) ( https://hkust.edu.hk/ ) is a world-class research intensive university that focuses on science, engineering and business as well as humanities and social science. HKUST offers an international campus, and a holistic and interdisciplinary pedagogy to nurture well-rounded graduates with global vision, a strong entrepreneurial spirit and innovative thinking. Over 80% of our research work were rated “Internationally excellent” or “world leading” in the Research Assessment Exercise 2020 of Hong Kong’s University Grants Committee. We were ranked 2nd in Times Higher Education’s Young University Rankings 2023, and our graduates were ranked 29th worldwide and among the best from universities from Asia in Global Employability University Ranking 2023. As of September 2023, HKUST members have founded 1,747 active start-ups, including 9 Unicorns and 13 exits (IPO or M&A), generating economic impact worth over HK$ 400 billion. InvestHK cited QS World University Rankings by Subject 2021 to demonstrate the performance of five world’s top 100 local universities in several innovation-centric areas, among which HKUST ranked top in four engineering and materials science subjects.  

For media enquiries, please contact: Anita Lam Tel: 2358 6313 Email:  [email protected]

Janice Tsang Tel: 3469 2512 Email:  [email protected]

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Seismic Performance of Isolated Bridges Under Beyond Design Basis Shaking, PEER Report 2024-02

Seismically isolated highway bridges are expected to provide limited service under a safety evaluation-level ground shaking with minimal to moderate damage. The behavior under shaking beyond design considerations, corresponding to a large return period seismic hazard, is not well understood and could induce significant damage. In these rare events, the seismic isolation system can be subjected to displacement demands beyond its design capacity, resulting in failure of the bearings, exceeding the clearance and pounding against the abutment backwalls, or damage propagating to other primary structural components. To better understand the seismic performance of simple highway bridges subjected to earthquakes beyond design considerations, this study simulates the response of a prototype bridge structure and examines the lateral displacement demands, the transfer of forces to the substructure, and potential failure modes of seismically isolated bridges. Advanced modeling approaches are considered to capture bearing characteristics, such as hardening at large strains, and a pounding macro-element to capture the effects of impact. Results show that for beyond design shaking, the bearings can reach the maximum shear strain capacity, significant residual deformation of the abutment can result from pounding, and the columns can experience moderate damage. The progression of damage is identified in an effort toward the development of models suitable for assessing the overall seismic risk, repairability, and downtime of seismically isolated bridges.

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    This seminar is an opportunity to become familiar with current research in software engineering and more generally with the methods and challenges of scientific research. ... The papers will span topics in both theory and practice, including papers on program verification, program analysis, testing, programming language design, and development ...

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    The papers will span topics in both theory and practice, including papers on program verification, program analysis, testing, programming language design, and development tools. Zoom Link Please join our sessions using this Zoom link . Schedule Graduate seminar on the latest research trends in the field of software engineering.

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  20. Papers for Software Engineers

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  22. Software and Systems Engineering

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  23. Robotics Software Engineering

    As robots often operate in dynamic, partially observable environments additional challenges include adaptability, robustness, safety, and security. The goal of this Research Topic is to bring together researchers with practitioners to identify new frontiers in robotics software engineering, discuss challenges raised by real-world applications ...

  24. Gartner Emerging Technologies and Trends Impact Radar for 2024

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  25. AI for Agile Software Engineering (AI4ASE)

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  28. Seismic Performance of Isolated Bridges Under Beyond Design Basis

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