software engineering research topics 2022

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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.

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.

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 Topics & Ideas: CompSci & IT

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

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

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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Research topics and ideas about data science and big data analytics

Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Information Technology -MSc program

It’s really interesting but how can I have access to the materials to guide me through my work?

That’s my problem also.

Investigating the impacts of software refactoring techniques and tools in blockchain-based developments is in my favour. May i get the proper material about that ?

BLOCKCHAIN TECHNOLOGY

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Software Engineering Research Ideas

I was honored to be given ACM SIGSOFT’s “Influential Educator” award in 2020, but I was also surprised : as far as I can tell, projects like Beautiful Code , Making Software , The Architecture of Open Source Applications , and It Will Never Work in Theory haven’t actually had any impact on how software engineering is taught.

However, I have been collecting random software engineering research ideas from friends and colleagues for more than a decade. I know it’s a weird hobby, but I’ve always believed that studying things practitioners are actually curious about would lead to more fruitful collaboration between academia and industry. Here, therefore, are the questions I’ve been asked since I started taking notes ten years ago. I apologize for not keeping track of who wanted to know, but if you’re working on any of these, please get in touch and I’ll try to track them down.

Does putting documentation in code (e.g., Python’s docstrings) actually work better than keeping the documentation in separate files, and if so, by what measure(s)?

Do doctest -style tests (i.e., tests embedded directly in the code being tested) have any impact long-term usability or maintainability compared to putting tests in separate files?

Which tasks do developers collaborate on most often and which do they do solo most often? (If I’m reading my handwriting correctly, the questioner hypothesized that programmers routinely do bug triage in groups, but usually write new code alone, with other tasks falling in between.)

Are slideshows written using HTML- or Markdown-based tools more text-intensive than those written in PowerPoint? In particular, are slides written in formats that version control understands (text) less likely to use diagrams than slides written with GUI tools?

A lot of code metrics have been developed over the years; are there any for measuring/ranking the difficulty of getting software installed and configured?

How does the percentage of effort devoted to tooling and deployment change as a project grows and/or ages? And how has it changed as we’ve moved from desktop applications to cloud-based applications? (Note: coming back to full-time coding after a decade away, my impression is that we’ve gone from packaging or building an installer taking 10% of effort to cloud deployment infrastructure being 25-30% of effort, but that’s just one data point.)

Has anyone developed a graphical notation for software development processes like this one for game play ?

How do open source projects actually track and manage requirements or user needs? Do they use issues, is it done through discussion threads on email or chat, do people write wiki pages or PEPs , etc.?

Has anyone ever done a quantitative survey of programming books aimed at professionals (i.e., not textbooks) to find out what people in industry care enough to write about or think others care about?

Has anyone ever done a quantitative survey of the data structures used in undergraduate textbooks for courses that aren’t about data structures? I.e., do we know what data structures students are shown in their “other” courses?

Has anyone ever compared a list of things empirical software engineering research has “proven” (ranked by confidence) versus a list of things programmers believe (similarly ranked)?

Has anyone ever done a quantitative survey of how many claims in the top 100 software development books are backed by citations, and of those, how many are still considered valid?

Are there any metrics for code fitness that take process and team into account? (I actually have the source for this one.)

Which of the techniques catalogued in The Discussion Book are programmers familiar with? Which ones do they use informally (i.e., without explicit tool support), and how do they operationalize them?

Is there a graphical notation like UML to show the problems you’re designing around or the special cases you’ve had to take into account rather than the finished solution to the problem (other than complete UML diagrams of the solutions you didn’t implement)?

Ditto for architectural evolution over time: is there an explicit notation for “here’s how the system has changed”, and if so, can it show multiple changes in a single diagram or is it just stepwise?

Pick an application (e.g., Twitter).
Build a work-alike that is deliberately malicious in some way (e.g., designed to radicalize its users).
Have people selected at random use both and then guess which is which.

Has anyone ever summarized the topics covered by ACM Doctoral Dissertation Award winners to see what computer science is actually about? (A subject is defined by what it gives awards for…)

Has anyone ever surveyed developers to find out what the most boring part of their job is?

Is there data anywhere on speakers’ fees at tech conferences broken down by by age, subject, gender, and geography?

Are programmers with greenery or mini-gardens in the office happier and/or more productive than programmers with foosball tables? What about programmers working from home: does the presence of greenery and/or pets make a difference?

How much do software engineering managers know about organizational behavior and/or social psychology? What mistruths and urban myths do they believe?

Has anyone ever compared how long it takes to reach a workable level of understanding of a software system with and without UML diagrams or other graphical notations? More generally, is there any correlation between the amount or quality of different kinds of developer-oriented documentation and time-to-understanding, and if so, which kinds of documentation fare best?

Is it possible to trace the genealogy of the slide decks used in undergrad software engineering classes (i.e., figure out who is adapting lessons originally written by whom)? If so, how does the material change over time?

How do people physically organize coding lessons when using static site generators? For example, do they keep example programs in the same directory or subdirectory as the slides, or keep the slides in one place and the examples in another? And how do they handle incremental evolution of examples, where the first lesson builds a simple version of X, the next lesson changes some parts but leaves others alone, etc.?

Has anyone ever applied security analysis techniques to emerging models of peer review to (for example) anticipate ways in which different kinds of open review might be gamed?

Has anyone ever written a compare-and-contrast feature analysis of tools for building documentation and tutorials? For example, how do Sphinx , Jekyll , and roxygen stack up?

Käfer et al’s paper comparing text and video tutorials for learning new software tools was interesting: has anyone done a follow-up?

Bjarnason et al’s paper on retrospectives was interesting: has anyone looked in more detail at what developers discuss in retrospectives and (crucially) what impact that has?

Has anyone studied adoption over time of changes (read: fixes) to Git’s interface? For example, how widely is git switch actually now being used? And how do adopters find out about it?

Same questions for adoption of new CSS features.

Is ther any correlation between the length of a project’s README file and how widely that software is used? If so, which drives which: does a more detailed README drive adoption or does adoption spur development of a more detailed README ?

Do any programming languages use one syntax for assigning an initial value to a variable and another syntax for updating that value, and if so, does distinguishing the two cases help? (Note: I think the person asking this question initially assumed that Python’s new := operator could only be used to assign an initial value.)

How, when, and why do people move from one open source project to another? For example, do they tend to move from a project to one of its dependencies or one of the projects that depends on it? And do they tend to keep the same role in the new project or use the switch as an opportunity to change roles?

How often do developers do performance profiling, what do they measure, and how do they measure it?

Has anyone ever created some like Sajaniemi’s roles of variables for refactoring steps or test cases? (Note: the person asking the question is a self-taught programmer who found Gamma et al’s book a bit intimidating, and is looking for beginner-level patterns.)

Has anyone defined a set of design patterns for the roles that columns play in dataframes during a data analysis?

(How) does team size affect the proportion of time spent on planning and the accuracy of plans?

Is there any way to detect altruism in software teams (i.e., how much time developer A spends helping developer B even though B’s problem isn’t officially A’s concern)? If so, is there any correlation between altruism and (for example) staff turnover or the long-term maintainability of the code base?

Is there any correlation between the quality of the error messages in a software system and the quality of the community? (Note: by “quality of the community”, I believe the questioner meant things like “welcoming to newcomers” and “actually enforces its code of conduct”.)

If you collect data from a dozen projects and guess which ones think they’re doing agile and which aren’t, is there anything more than a weak correlation to what process team members tell you they think they’re following? I.e., are different development methodologies distinct rhetorically but not practically?

What are students taught about debugging after their introductory courses? How much of what they’re explicitly taught is domain-specific (e.g., “how to debug a graphics pipeline”)?

Can we assess students’ proficiency with tools by watching screencasts of their work? And can we do it efficiently enough to make it a feasible way to grade how they code (as well as the code they write)?

A lot of people have built computational notebooks based on text formats (like Markdown) or that run in the browser. Has anyone built a computational notebook starting with Microsoft Word or OpenOffice, i.e., embedded runnable code chunks and their output in a rich document?

When people write essay-length explanations about error handling or database internals , how do they decide what’s worth explaining? Is it “I struggled to figure this out and want to save you the pain” or “I’m trying to build my reputation as an expert in this field” or something else?

Has anyone done a study that plots when people get funded on a loose timeline of “building a startup” broken out by founders’ characteristics? I.e., if 0 is “I have an idea” and 100 is fully functioning company, where do most black/brown founders get funded vs. other poc founders vs. white founders?

Has anyone analyzed videos of coding clubs for children or teens to see if girls are treated differently than boys by instructors and by their peers?

How does the distribution of language constructs actually used in large programs vary by language? For example, if we plot percentage of programs that use feature X in a language, ordered by decreasing frequency, how do the curves for different languages compare?

Is it possible to calculate something like a Gini coefficient to see how effectively scientists use computing? If so, is inequality static, decreasing, or increasing? (Note: the questioner felt strongly that the most proficient scientists are getting better at programming but the vast majority haven’t budged in the last three decades, so the gap between “median” and “best” is actually widening.)

If you train a Markov text generator on your software’s documentation, generate some fake man pages, and give users a mix of real and fake pages, can they tell which are which?

How does the number of (active) Slack channels in an organization grow as a function of time or of the number of employees?

How well are software engineering researchers able to summarize each other’s work based solely on the abstracts of their research papers, and how does that compare to researchers in other domains?

Second-line tech support staff often spend a lot of time explaining how things work in general so that they can solve a specific problem. How do they tell how much detail they need to go into?

Is there a notation like CSS selectors for selecting parts of a program to display in tutorials? (Note: I’ve used several systems that relied on specially-formatted comments to slice sections out of programs for display; the questioner was using one of these for the first time and wondering if there was something simpler, more robust, or more general.)

How does the order in which people write code differ from the order in which they explain code in a tutorial and why?

Has anyone built a computational notebook that presents a two-column display with the code on the left and commentary on the right? If so, how does that change what people do or how they do it?

Is it possible to extract entity-relationship diagrams from programs that use Pandas or the tidyverse to show how dataframes are being combined (e.g., to infer foreign key relationships)?

What percentage of time to developers spend debugging and how does that vary by the kind of code they’re working on?

At what point is it more economical to throw away a module and write a replacement instead of refactoring or extending the module to meet new needs?

Are SQL statements written in execution order easier for novices to understand or less likely to be buggy than ones written in standard order? (Note: the questioner was learning SQL after learning to manipulate dataframes with the tidyverse, and found the out-of-order execution of SQL confusing after the in-order execution of tidyverse pipelines.)

What error recovery techniques are used in what languages and applications how often?

What labels do people define for GitHub issues and pull requests, and do they take those labels with them to new projects or re-think each project?

Creating a set of scenarios, each with multiple-choice options.
Having an ethics expert determine the best answer for each.
Then have students and professionals answer the same questions.
Analyzed the results to see how well each group matches the experts’ opinions and whether practitioners are any better than students.

Has anyone ever studied students from the first year to the final year of their program to see what tools they actually start using when. In particular, when (if ever) do they start to use more advanced features of their IDE (e.g., “rename variable in scope”)?

Underrepresented groups often develop “whisper networks” to share essential knowledge (e.g., a young woman joining a company might be taken aside for an off-the-record chat by an older colleague and cautioned about the behavior of certain senior male colleagues). How have these networks changed during the COVID-19 lockdown?

And here are two of my own:

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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 April 17, 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 April 17, 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: 17-Apr-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-Apr-17} }

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

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.

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.

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, the latest work from the sei: an openai collaboration, generative ai, and zero trust, april 10, 2024 • by douglas schmidt (vanderbilt university), 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), get updates on our latest work..

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Title: the general index of software engineering papers.

Abstract: We introduce the General Index of Software Engineering Papers, a dataset of fulltext-indexed papers from the most prominent scientific venues in the field of Software Engineering. The dataset includes both complete bibliographic information and indexed ngrams (sequence of contiguous words after removal of stopwords and non-words, for a total of 577 276 382 unique n-grams in this release) with length 1 to 5 for 44 581 papers retrieved from 34 venues over the 1971-2020 period.The dataset serves use cases in the field of meta-research, allowing to introspect the output of software engineering research even when access to papers or scholarly search engines is not possible (e.g., due to contractual reasons). The dataset also contributes to making such analyses reproducible and independently verifiable, as opposed to what happens when they are conducted using 3rd-party and non-open scholarly indexing services.The dataset is available as a portable Postgres database dump and released as open data.

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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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The growing interest in improving software processes has led organizations to aim for high maturity, where statistical process control (SPC) is required. SPC makes it possible to analyze process behavior, pred...

An approach for applying Test-Driven Development (TDD) in the development of randomized algorithms

TDD is a technique traditionally applied in applications with deterministic algorithms, in which the input and the expected result are known. However, the application of TDD with randomized algorithms have bee...

Supporting governance of mobile application developers from mining and analyzing technical questions in stack overflow

There is a need to improve the direct communication between large organizations that maintain mobile platforms (e.g. Apple, Google, and Microsoft) and third-party developers to solve technical questions that e...

Working software over comprehensive documentation – Rationales of agile teams for artefacts usage

Agile software development (ASD) promotes working software over comprehensive documentation. Still, recent research has shown agile teams to use quite a number of artefacts. Whereas some artefacts may be adopt...

Development as a journey: factors supporting the adoption and use of software frameworks

From the point of view of the software framework owner, attracting new and supporting existing application developers is crucial for the long-term success of the framework. This mixed-methods study explores th...

Applying user-centered techniques to analyze and design a mobile application

Techniques that help in understanding and designing user needs are increasingly being used in Software Engineering to improve the acceptance of applications. Among these techniques we can cite personas, scenar...

A measurement model to analyze the effect of agile enterprise architecture on geographically distributed agile development

Efficient and effective communication (active communication) among stakeholders is thought to be central to agile development. However, in geographically distributed agile development (GDAD) environments, it c...

A survey of search-based refactoring for software maintenance

This survey reviews published materials related to the specific area of Search-Based Software Engineering that concerns software maintenance and, in particular, refactoring. The survey aims to give a comprehen...

Guest editorial foreword for the special issue on automated software testing: trends and evidence

Similarity testing for role-based access control systems.

Access control systems demand rigorous verification and validation approaches, otherwise, they can end up with security breaches. Finite state machines based testing has been successfully applied to RBAC syste...

An algorithm for combinatorial interaction testing: definitions and rigorous evaluations

Combinatorial Interaction Testing (CIT) approaches have drawn attention of the software testing community to generate sets of smaller, efficient, and effective test cases where they have been successful in det...

How diverse is your team? Investigating gender and nationality diversity in GitHub teams

Building an effective team of developers is a complex task faced by both software companies and open source communities. The problem of forming a “dream”

Investigating factors that affect the human perception on god class detection: an analysis based on a family of four controlled experiments

Evaluation of design problems in object oriented systems, which we call code smells, is mostly a human-based task. Several studies have investigated the impact of code smells in practice. Studies focusing on h...

On the evaluation of code smells and detection tools

Code smells refer to any symptom in the source code of a program that possibly indicates a deeper problem, hindering software maintenance and evolution. Detection of code smells is challenging for developers a...

On the influence of program constructs on bug localization effectiveness

Software projects often reach hundreds or thousands of files. Therefore, manually searching for code elements that should be changed to fix a failure is a difficult task. Static bug localization techniques pro...

DyeVC: an approach for monitoring and visualizing distributed repositories

Software development using distributed version control systems has become more frequent recently. Such systems bring more flexibility, but also greater complexity to manage and monitor multiple existing reposi...

A genetic algorithm based framework for software effort prediction

Several prediction models have been proposed in the literature using different techniques obtaining different results in different contexts. The need for accurate effort predictions for projects is one of the ...

Elaboration of software requirements documents by means of patterns instantiation

Studies show that problems associated with the requirements specifications are widely recognized for affecting software quality and impacting effectiveness of its development process. The reuse of knowledge ob...

ArchReco: a software tool to assist software design based on context aware recommendations of design patterns

This work describes the design, development and evaluation of a software Prototype, named ArchReco, an educational tool that employs two types of Context-aware Recommendations of Design Patterns, to support us...

On multi-language software development, cross-language links and accompanying tools: a survey of professional software developers

Non-trivial software systems are written using multiple (programming) languages, which are connected by cross-language links. The existence of such links may lead to various problems during software developmen...

SoftCoDeR approach: promoting Software Engineering Academia-Industry partnership using CMD, DSR and ESE

The Academia-Industry partnership has been increasingly encouraged in the software development field. The main focus of the initiatives is driven by the collaborative work where the scientific research work me...

Issues on developing interoperable cloud applications: definitions, concepts, approaches, requirements, characteristics and evaluation models

Among research opportunities in software engineering for cloud computing model, interoperability stands out. We found that the dynamic nature of cloud technologies and the battle for market domination make clo...

Game development software engineering process life cycle: a systematic review

Software game is a kind of application that is used not only for entertainment, but also for serious purposes that can be applicable to different domains such as education, business, and health care. Multidisc...

Correlating automatic static analysis and mutation testing: towards incremental strategies

Traditionally, mutation testing is used as test set generation and/or test evaluation criteria once it is considered a good fault model. This paper uses mutation testing for evaluating an automated static anal...

A multi-objective test data generation approach for mutation testing of feature models

Mutation approaches have been recently applied for feature testing of Software Product Lines (SPLs). The idea is to select products, associated to mutation operators that describe possible faults in the Featur...

An extended global software engineering taxonomy

In Global Software Engineering (GSE), the need for a common terminology and knowledge classification has been identified to facilitate the sharing and combination of knowledge by GSE researchers and practition...

A systematic process for obtaining the behavior of context-sensitive systems

Context-sensitive systems use contextual information in order to adapt to the user’s current needs or requirements failure. Therefore, they need to dynamically adapt their behavior. It is of paramount importan...

Distinguishing extended finite state machine configurations using predicate abstraction

Extended Finite State Machines (EFSMs) provide a powerful model for the derivation of functional tests for software systems and protocols. Many EFSM based testing problems, such as mutation testing, fault diag...

Extending statecharts to model system interactions

Statecharts are diagrams comprised of visual elements that can improve the modeling of reactive system behaviors. They extend conventional state diagrams with the notions of hierarchy, concurrency and communic...

On the relationship of code-anomaly agglomerations and architectural problems

Several projects have been discontinued in the history of the software industry due to the presence of software architecture problems. The identification of such problems in source code is often required in re...

An approach based on feature models and quality criteria for adapting component-based systems

Feature modeling has been widely used in domain engineering for the development and configuration of software product lines. A feature model represents the set of possible products or configurations to apply i...

Patch rejection in Firefox: negative reviews, backouts, and issue reopening

Writing patches to fix bugs or implement new features is an important software development task, as it contributes to raise the quality of a software system. Not all patches are accepted in the first attempt, ...

Investigating probabilistic sampling approaches for large-scale surveys in software engineering

Establishing representative samples for Software Engineering surveys is still considered a challenge. Specialized literature often presents limitations on interpreting surveys’ results, mainly due to the use o...

Characterising the state of the practice in software testing through a TMMi-based process

The software testing phase, despite its importance, is usually compromised by the lack of planning and resources in industry. This can risk the quality of the derived products. The identification of mandatory ...

Self-adaptation by coordination-targeted reconfigurations

A software system is self-adaptive when it is able to dynamically and autonomously respond to changes detected either in its internal components or in its deployment environment. This response is expected to ensu...

Templates for textual use cases of software product lines: results from a systematic mapping study and a controlled experiment

Use case templates can be used to describe functional requirements of a Software Product Line. However, to the best of our knowledge, no efforts have been made to collect and summarize these existing templates...

F3T: a tool to support the F3 approach on the development and reuse of frameworks

Frameworks are used to enhance the quality of applications and the productivity of the development process, since applications may be designed and implemented by reusing framework classes. However, frameworks ...

NextBug: a Bugzilla extension for recommending similar bugs

Due to the characteristics of the maintenance process followed in open source systems, developers are usually overwhelmed with a great amount of bugs. For instance, in 2012, approximately 7,600 bugs/month were...

Assessing the benefits of search-based approaches when designing self-adaptive systems: a controlled experiment

The well-orchestrated use of distilled experience, domain-specific knowledge, and well-informed trade-off decisions is imperative if we are to design effective architectures for complex software-intensive syst...

Revealing influence of model structure and test case profile on the prioritization of test cases in the context of model-based testing

Test case prioritization techniques aim at defining an order of test cases that favor the achievement of a goal during test execution, such as revealing failures as earlier as possible. A number of techniques ...

A metrics suite for JUnit test code: a multiple case study on open source software

The code of JUnit test cases is commonly used to characterize software testing effort. Different metrics have been proposed in literature to measure various perspectives of the size of JUnit test cases. Unfort...

Designing fault-tolerant SOA based on design diversity

Over recent years, software developers have been evaluating the benefits of both Service-Oriented Architecture (SOA) and software fault tolerance techniques based on design diversity. This is achieved by creat...

Method-level code clone detection through LWH (Light Weight Hybrid) approach

Many researchers have investigated different techniques to automatically detect duplicate code in programs exceeding thousand lines of code. These techniques have limitations in finding either the structural o...

The problem of conceptualization in god class detection: agreement, strategies and decision drivers

The concept of code smells is widespread in Software Engineering. Despite the empirical studies addressing the topic, the set of context-dependent issues that impacts the human perception of what is a code sme...

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Software Engineering Research, Management and Applications

© 2022
Roger Lee 0

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Presents recent research in Software Engineering, Management, and Applications
Is edited outcome of the 20th IEEE/ACIS SERA 2021 conference held May 25-27, 2022, Las Vegas, USA
Written by experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 1053)

Included in the following conference series:

SERA: International Conference on Software Engineering Research and Applications

Conference proceedings info: SERA 2022.

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Table of contents (12 chapters)

Front matter, examining the factors that influence customers’ intention to use smartwatches in malaysia using utaut2 model.

Norazryana Mat Dawi, Ha Jin Hwang, Ahmad Jusoh, Haeng Kon Kim

Generating Adversarial Robust Defensive CAPTCHA (GARD-CAPTCHA) in Convolutional Neural Networks

Pu Tian, Weixian Liao, Turhan Kimbrough, Erik Blasch, Wei Yu

A Deep Learning Approach for Lantana Camara Weed Detection and Localization in the Natural Environment

Wie Kiang Hi, Santoso Wibowo

Modeling Concretizations in Software Design

Alexey Tazin, Shan Lu, Yanji Chen, Mieczyslaw M. Kokar, Jeff Smith

A Practical Style Guide and Templates Repository for Writing Effective Use Cases

Bingyang Wei, Lin Deng, Yi Wang

Label Correction of Sound Data with Label Noise Using Self Organizing Map

Pildong Hwang, Yanggon Kim

Evaluation Method of Enterprise Cybersecurity

Meng Zhang, Yue Zhou, Che Li, Shuang Li, Jianhua Wu, Chao Yan

A Multi-model Multi-task Learning System for Hurricane Genesis Prediction

Martin Pineda, Qianlong Wang, Weixian Liao, Michael McGuire, Wei Yu

Development of Autonomous Driving Adaptive Simulation System Using Deep Learning Process Model

Symphorien Karl Yoki Donzia, Haeng-Kon Kim

An OCL Implementation for Model-Driven Engineering of C++

R. Maschotta, N. Silatsa, T. Jungebloud, M. Hammer, A. Zimmermann

Improving Students’ Readiness Toward the Labor Market Through Customized Learning

Majed Almotairi, Hamdan Ziyad Alabsi, Yahya Alqahtani, Mohammed Abdulkareem Alyami, Majed M. Aljazaeri, Yeong-Tae Song

Assessing Software Fault Risk with Machine Learning

Naveen Ashish, Greg Barish, Steven Minton

Back Matter

Computational Intelligence
Software Engineering
Software Management

About this book

This edited book presents scientific results of the 20th IEEE/ACIS International Conference on Software Engineering Research, Management, and Applications (SERA2022) held on May 25, 2022, in Las Vegas, USA. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review.From this second round of review, 12 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science.

Editors and Affiliations

Bibliographic information.

Book Title : Software Engineering Research, Management and Applications

Editors : Roger Lee

Series Title : Studies in Computational Intelligence

DOI : https://doi.org/10.1007/978-3-031-09145-2

Publisher : Springer Cham

eBook Packages : Intelligent Technologies and Robotics , Intelligent Technologies and Robotics (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Hardcover ISBN : 978-3-031-09144-5 Published: 22 September 2022

Softcover ISBN : 978-3-031-09147-6 Published: 22 September 2023

eBook ISBN : 978-3-031-09145-2 Published: 21 September 2022

Series ISSN : 1860-949X

Series E-ISSN : 1860-9503

Edition Number : 1

Number of Pages : XIII, 204

Number of Illustrations : 20 b/w illustrations, 54 illustrations in colour

Topics : Computational Intelligence , Software Engineering/Programming and Operating Systems , Artificial Intelligence

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Research Topics in Software Engineering

software engineering research topics 2022

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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Ieee spectrum, follow ieee spectrum, support ieee spectrum, enjoy more free content and benefits by creating an account, saving articles to read later requires an ieee spectrum account, the institute content is only available for members, downloading full pdf issues is exclusive for ieee members, downloading this e-book is exclusive for ieee members, access to spectrum 's digital edition is exclusive for ieee members, following topics is a feature exclusive for ieee members, adding your response to an article requires an ieee spectrum account, create an account to access more content and features on ieee spectrum , including the ability to save articles to read later, download spectrum collections, and participate in conversations with readers and editors. for more exclusive content and features, consider joining ieee ., join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, join the world’s largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of ieee spectrum’s articles, archives, pdf downloads, and other benefits. learn more →, access thousands of articles — completely free, create an account and get exclusive content and features: save articles, download collections, and talk to tech insiders — all free for full access and benefits, join ieee as a paying member., 12 exciting engineering milestones to look for in 2022, an electric aircraft race, a new dark-matter detector, and a permanent chinese space station await, psyche’s deep-space lasers.

In August, NASA will launch the Psyche mission , sending a deep-space orbiter to a weird metal asteroid orbiting between Mars and Jupiter. While the probe’s main purpose is to study Psyche’s origins, it will also carry an experiment that could inform the future of deep-space communications. The Deep Space Optical Communications (DSOC) experiment will test whether lasers can transmit signals beyond lunar orbit. Optical signals, such as those used in undersea fiber-optic cables, can carry more data than radio signals can, but their use in space has been hampered by difficulties in aiming the beams accurately over long distances. DSOC will use a 4-watt infrared laser with a wavelength of 1,550 nanometers (the same used in many optical fibers) to send optical signals at multiple distances during Psyche’s outward journey to the asteroid.

The Great Electric Plane Race

For the first time in almost a century, the U.S.-based National Aeronautic Association (NAA) will host a cross-country aircraft race . Unlike the national air races of the 1920s, however, the Pulitzer Electric Aircraft Race, scheduled for 19 May, will include only electric-propulsion aircraft. Both fixed-wing craft and helicopters are eligible. The competition will be limited to 25 contestants, and each aircraft must have an onboard pilot. The course will start in Omaha and end four days later in Manteo, N.C., near the site of the Wright brothers’ first flight. The NAA has stated that the goal of the cross-country, multiday race is to force competitors to confront logistical problems that still plague electric aircraft, like range, battery charging, reliability, and speed.

6-Gigahertz Wi-Fi Goes Mainstream

Wi-Fi is getting a boost with 1,200 megahertz of new spectrum in the 6-gigahertz band, adding a third spectrum band to the more familiar 2.4 GHz and 5 GHz. The new band is called Wi-Fi 6E because it extends Wi-Fi’s capabilities into the 6-GHz band. As a rule, higher radio frequencies have higher data capacity, but a shorter range. With its higher frequencies, 6-GHz Wi-Fi is expected to find use in heavy traffic environments like offices and public hotspots. The Wi-Fi Alliance introduced a Wi-Fi 6E certification program in January 2021, and the first trickle of 6E routers appeared by the end of the year. In 2022, expect to see a bonanza of Wi-Fi 6E–enabled smartphones.

3-Nanometer Chips Arrive

Taiwan Semiconductor Manufacturing Co. (TSMC) plans to begin producing 3-nanometer semiconductor chips in the second half of 2022 . Right now, 5-nm chips are the standard. TSMC will make its 3-nm chips using a tried-and-true semiconductor structure called the FinFET (short for “fin field-effect transistor”). Meanwhile, Samsung and Intel are moving to a different technique for 3 nm called nanosheet. (TSMC is eventually planning to abandon FinFETs .) At one point, TSMC’s sole 3-nm chip customer for 2022 was Apple , for the latter’s iPhone 14, but supply-chain issues have made it less certain that TSMC will be able to produce enough chips—which promise more design flexibility —to fulfill even that order.

Seoul Joins the Metaverse

After Facebook (now Meta ) announced it was hell-bent on making the metaverse real, a host of other tech companies followed suit. Definitions differ, but the basic idea of the metaverse involves merging virtual reality and augmented reality with actual reality. Also jumping on the metaverse bandwagon is the government of the South Korean capital, Seoul, which plans to develop a “metaverse platform” by the end of 2022. To build this first public metaverse, Seoul will invest 3.9 billion won (US $3.3 million). The platform will offer public services and cultural events , beginning with the Metaverse 120 Center, a virtual-reality portal for citizens to address concerns that previously required a trip to city hall. Other planned projects include virtual exhibition halls for school courses and a digital representation of Deoksu Palace . The city expects the project to be complete by 2026.

IBM’s Condors Take Flight

In 2022, IBM will debut a new quantum processor—its biggest yet—as a stepping-stone to a 1,000-qubit processor by the end of 2023 . This year’s iteration will contain 433 qubits, three times as much as the company’s 127-qubit Eagle processor, which was launched last year. Following the bird theme, the 433- and 1,000-qubit processors will be named Condor. There have been quantum computers with many more qubits; D-Wave Systems, for example, announced a 5,000-qubit computer in 2020. However, D-Wave’s computers are specialized machines for optimization problems. IBM’s Condors aim to be the largest general-purpose quantum processors.

New Dark-Matter Detector

The Forward Search Experiment (FASER) at CERN is slated to switch on in July 2022. The exact date depends on when the Large Hadron Collider is set to renew proton-proton collisions after three years of upgrades and maintenance. FASER will begin a hunt for dark matter and other particles that interact extremely weakly with “normal” matter. CERN, the fundamental physics research center near Geneva, has four main detectors attached to its Large Hadron Collider, but they aren’t well-suited to detecting dark matter. FASER won’t attempt to detect the particles directly; instead, it will search for the more strongly interacting Standard Model particles created when dark matter interacts with something else. The new detector was constructed while the collider was shut down from 2018 to 2021. Located 480 meters “downstream” of the ATLAS detector, FASER will also hunt for neutrinos produced in huge quantities by particle collisions in the LHC loop. The other CERN detectors have so far failed to detect such neutrinos.

Pong Turns 50

Atari changed the course of video games when it released its first game, Pong, in 1972. While not the first video game—or even the first to be presented in an upright, arcade-style cabinet—Pong was the first to be commercially successful. The game was developed by engineer Allan Alcorn and originally assigned to him as a test after he was hired, before he began working on actual projects. However, executives at Atari saw potential in Pong’s simple game play and decided to develop it into a real product. Unlike the countless video games that came after it, the original Pong did not use any code or microprocessors. Instead, it was built from a television and transistor-transistor logic.

The Green Hydrogen Boom

Utility company Energias de Portugal (EDP), based in Lisbon, is on track to begin operating a 3-megawatt green hydrogen plant in Brazil by the end of the year. Green hydrogen is hydrogen produced in sustainable ways, using solar or wind-powered electrolyzers to split water molecules into hydrogen and oxygen. According to the International Energy Agency, only 0.1 percent of hydrogen is produced this way. The plant will replace an existing coal-fired plant and generate hydrogen—which can be used in fuel cells—using solar photovoltaics. EDP’s roughly US $7.9 million pilot program is just the tip of the green hydrogen iceberg. Enegix Energy has announced plans for a $5.4 billion green hydrogen plant in the same Brazilian state, Ceará, where the EDP plant is being built. The green hydrogen market is predicted to generate a revenue of nearly $10 billion by 2028 , according to a November 2021 report by Research Dive.

A Permanent Space Station for China

China is scheduled to complete its Tiangong (“Heavenly Palace”) space station in 2022. The station, China’s first long-term space habitat, was preceded by the Tiangong-1 and Tiangong-2 stations, which orbited from 2011 to 2018 and 2016 to 2019, respectively. The new station’s core module, the Tianhe, was launched in April 2021. A further 10 missions by the end of 2022 will deliver other components and modules, with construction to be completed in orbit. The final station will have two laboratory modules in addition to the core module. Tiangong will orbit at roughly the same altitude as the International Space Station but will be only about one-fifth the mass of the ISS.

A Cool Form of Energy Storage

Cryogenic energy-storage company Highview Power will begin operations at its Carrington plant near Manchester, England, this year. Cryogenic energy storage is a long-term method of storing electricity by cooling air until it liquefies (about –196 °C). Crucially, the air is cooled when electricity is cheaper—at night, for example—and then stored until electricity demand peaks. The liquid air is then allowed to boil back into a gas, which drives a turbine to generate electricity. The 50-megawatt/250-megawatt-hour Carrington plant will be Highview Power’s first commercial plant using its cryogenic storage technology, dubbed CRYOBattery. Highview Power has said it plans to build a similar plant in Vermont, although it has not specified a timeline yet.

Carbon-Neutral Cryptocurrency?

Seattle-based startup Nori is set to offer a cryptocurrency for carbon removal . Nori will mint 500 million tokens of its Ethereum-based currency (called NORI). Individuals and companies can purchase and trade NORI, and eventually exchange any NORI they own for an equal number of carbon credits. Each carbon credit represents a tonne of carbon dioxide that has already been removed from the atmosphere and stored in the ground. When exchanged in this way, a NORI is retired, making it impossible for owners to try to “double count” carbon credits and therefore seem like they’re offsetting more carbon than they actually have. The startup has acknowledged that Ethereum and other blockchain-based technologies consume an enormous amount of energy, so the carbon it sequesters could conceivably originate in cryptocurrency mining. However, 2022 will also see Ethereum scheduled to switch to a much more energy-efficient method of verifying its blockchain , called proof-of-stake, which Nori will take advantage of when it launches.

Google's Quantum Tech Milestone Excites Scientists and Spurs ... ›
10 Exciting Engineering Milestones to Look for in 2021 - IEEE ... ›
Top Tech 2022: A Special Report - IEEE Spectrum ›
Tech Leaders on 5G, Robots, and the Future of Work - IEEE Spectrum ›
11 Intriguing Engineering Milestones to Look for in 2023 - IEEE Spectrum ›

Michael Koziol is an associate editor at IEEE Spectrum where he covers everything telecommunications. He graduated from Seattle University with bachelor's degrees in English and physics, and earned his master's degree in science journalism from New York University.

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Software Engineering Project Ideas (2024)

Are you an Engineering student looking for software engineering project Ideas and topics to develop for your final year compliance for 2024?

Then here’s what you need. I have here the best project ideas for software engineering which are best and ideal to develop for 2024.

There’s also a bonus list of Software Engineering Projects that you can see through to choose your desired project.

They were thoroughly made and researched to assure you that they are helpful and applicable for the current situation on every field of work.

New ideas and topics are formulated through the growing technology and software engineer project ideas .

These ideas were latest and are based to the current need of our surroundings.

They were also made sure to be applicable nowadays so you wont be left behind. So without further ado, let us discuss the TOP 5 capstone project ideas software engineering Students for 2024.

We have chosen the TOP 5 Final Year Projects for Software Engineering Students this 2024 and provided the bonus list.

The bonus list is provided for you to choose your best choice of project that suits your capabilities.

These software engineering projects for students bonus list are valued and studied well that’s why all of them has their own potential.

TOP 5 Final Year Projects for Software Engineering Students for 2024

Time needed: 5 minutes

Now I present to the top 5 chosen Final Year Software Engineering Project topics and ideas that will surely be applicable in big establishments and for our current situations. They could also help a lot of people nowadays.

Because sensitive data is at risk, including high-range security into imaging and communication systems is critical. The digital photos are encrypted using the AES technique, and only the sender and receiver can be seen.

For verification, the suggested system uses the location of geography. If it detects an abnormal pattern, the user will have to repeat the verification process.

The system categorizes and organizes the information in the e-book. Customers can utilize the e-learning website to look for books and learn so that paperwork is reduced. Admin changes the system whenever a new book/video arrives.

This project will be utilized to create a cloud-based bus pass system. So that consumers can quickly check the status of buses and the availability of tickets.

The E consultation system intends to provide an atmosphere in which patients can consult doctors, send photographs (for skin diseases/beauty-related issues), communicate with doctors, inform them about their problems, and explore possible solutions.

Now if you don’t like the chosen top 5 Final Year Projects as your Software Engineering topics and ideas this 2022, you could still look for another on the bonus list provided below.

Bonus Lists of Final Year Projects For Software Engineering Students for 2024

Final year projects for software engineering students for 2024.

Here is the list of new topics and ideas for final-year software Engineering Projects applicable to all Engineering Students in 2024.

Each of the projects Listed is ideal and applicable to your field. And the ideas applied were thoroughly formulated and assured.

Active Chat Monitoring and Suspicious Chat Detection over the Internet
Advanced Mobile Store
Advanced Reliable Real Estate Portal
AI Desktop Partner
AI Multi Agent Shopping System
Airport Network Flight Scheduler
An Adaptive Social Media Recommendation System
Artificial Intelligence Dietician
Attack Source Tracing Project
Automated Attendance System
Automated College Timetable Generator
Automated Payroll With GPS Tracking And Image Capture
Automatic Answer Checker
Banking Bot Project
Barbershop Service Booking App
Bikers Portal
Biomedical Data Mining For Web Page Relevance Checking
Bus Pass with Barcode Card scan
Bus Pass with webcam Scan
Camera Motion Sensing Project
Canteen Automation System
Car Sales And Inventory Store Project
Cargo Booking Software
Cloud Based Bus Pass System
Cloud Based Career Guidance System
Cloud Based Local Train Ticketing System
Cloud Based Online Blood Bank System
Cloud computing for Rural banking
Collective Face Detection Project
College automation project
College Enquiry Chat Bot
College Social Networking Web Project
Corporate Dashboard Project
Credit Card Fraud Detection
Criminal Investigation Tracker with Suspect Prediction
Cursor Movement By Hand Gesture Project
Customer Behavior Prediction Using Web Usage Mining
Data Mining For Automated Personality Classification
Dementia Virtual Memory App
Detect Irregular moving objects and tracking based on color and shape in real-time
Detecting Data Leaks
Detecting E Banking Phishing Websites Using Associative Classification
Detecting Edges Using Image Processor
Diagnostic Centre Client Coordination System
Distributed Dealership Network Analyzer and Sales Monitor
Doctor Appointment Booking & Live Chat App
Document checker and Corrector Project
Driver Card With QR Code Identification
E Commerce Product Rating Based On Customer Review Mining
E Healthcare – Online Consultation And Medical Subscription
Education Assignment Dashboard
Efficient Doctor Patient Portal
E-Learning Platform using Cloud Computing
Emergency Ambulance Booking App
Employee attendance System By QR Scan
Employee Hourly Attendance By Barcode Scan
Engineering College Automation and Scheduling System
ERP System For Institutes
Extended AES with Custom Configurable Encryption
Face Recognition Attendance System
Facial Expression Recognition
Fake Product Review Monitoring And Removal For Genuine Online Product Reviews Using Opinion Mining
Farming Assistance Web Service
Fingerprint Based ATM System
Fingerprint Voting System Project
Fitness App With Workout Diet & Motivation
Graphical Password By Image Segmentation
Graphical Password Strategy
Gym Trainer & Progress Tracker App
Human Speed Detection Project
Image Encryption For Secure Internet Transfer
Image Encryption Using AES Algorithm
Image Encryption Using Triple DES
Image Mining Project
Image Steganography With 3 Way Encryption
Improved Data Leakage Detection
Intelligent PC Location Tracking System
Intelligent Tourist System Project
Internet Based Live Courier Tracking And Delivery System
iPad Restaurant Application
LED display generator project
Look Based Media Player
Matrimonial Portal Project
Media player Project
Medical Search Engine Project
Military Access Using Card Scanning With OTP
MLM Project
Mobile Attendance System Project
Mobile Banking Project
Mobile Network Stability
Mobile Networks Load Balancing
Mobile Quiz Through Wi-Fi Project
Mobile(location based) Advertisement System
Monitoring Suspicious Discussions On Online Forums Using Data Mining
Movie Success Prediction Using Data Mining
Multi Coverage Broadcast
Multi Website Advertisement Handling System
Network Based Stock Price System
On Demand Remote PC Monitoring system Through Internet
Online AI Shopping With M-Wallet System
Online Bookstore System On Cloud Infrastructure
Online Diagnostic Lab Reporting System
Online E-book Maker Project
Online Election System Project
Online Fashion Stylist Website
Online Herbs Shopping Project
Online Loan Application & Verification System
Online Mobile Recharge Portal Project
Online PDF to Text Converter & Language Translator Python
Online Printed T-Shirt Designing
Online Visiting Card Creation Project
Opinion Mining For Comment Sentiment Analysis
Opinion Mining For Restaurant Reviews
Opinion Mining For Social Networking Site
PC Configuration Retrieval System on Online Server
Public Photography Contest With Live Voting
Question paper generator system
Railway Tracking and Arrival Time Prediction
Real Estate Search Based On Data Mining
Remote Java 2 .net Communication Application
Remote User Recognition And Access Provision
Retail Store Inventory & POS Checkout App
RFID Based Automatic Traffic Violation Ticketing
Secure ATM Using Card Scanning Plus OTP
Secure Data Transfer Over Internet Using Image Steganography
Secure Electronic Fund Transfer Over Internet Using DES
Secure Lab Access Using Card Scanner Plus Face Recognition
Secure Remote Communication Using DES Algorithm
Sending a secure message over a network to a remote site
Sentiment Analysis for Product Rating
Sentiment Based Movie Rating System
Smart Health Consulting Project
Smart Health consulting system
Smart Health Prediction Using Data Mining
Software Piracy Protection Project
Space Shooter Combat Game Python
SQL Injection Prevention Project
Storage/Energy efficient Cloud Computing
Students Grievance Redressal Cell Python
Tab Based Library Book Availability & Location Finder On Wi-Fi
Civil System Project
Three Level Image Password Authentication
Three Level Password Authentication System
Topic Detection Using Keyword Clustering
Tour Recommender App Using Collaborative Filtering
Unique User Identification Across Multiple Social Networks
User Web Access Records Mining For Business Intelligence
Vehicle Tracking Using Driver Mobile GPS Tracking
Video Surveillance Project
Waste Food Management & Donation App
Weather Forecasting Using Data Mining
Web Agent For Learning Content Updating
Web Content Trust Rating Prediction Using Evidence Theory
Web Data Mining To Detect Online Spread Of Terrorism
Web Mining For Suspicious Keyword Prominence
Web Server Log Analysis System
Web Server to Client communication for web usage data analysis
Webpage Ranking Search Engine With SEO Suggestions
Website Evaluation Using Opinion Mining
Wheelchair Guidance & Assistance App
Wi-Fi Shopping Guide Project
Wireless Data Handling And Management
Wireless Indoor Positioning System

Final Year Software Engineering Projects Topics and Ideas using Android for 2024

Now here’s another bonus list of Software Engineering Project ideas and topics using Android. So if you’re fond of programming and doing Android then this Final Year Software Engineering Topics and Ideas are best for you.

Android PC Chatting & Image Sharing System
Android AI Diet Consultant
Android Anti-Virus Application
Android Based Furniture Shopping
Android Based Parking Booking System
Android Based Universal Ticketing Project
Android Based Visual Product Identification For The Blind
Android Blood Bank
Android Blood Donation & Blood Bank Finder
Android Bluetooth Chat
Android College Connect Chat App
Android Customer Relationship Management System
Android Employee Tracker
Android File finder and Sorting
Android Local Train Ticketing Project
Android location alarm
Android Merchant Application Using QR
Android Messenger App
Android Offloading Computation Over Cloud
Android Patient Tracker
Android Personal Safety App
Android Task Monitoring
Android Tourist Guide Project
Android Vehicle Tracking Application
Android Voting System
Automated Canteen Ordering System using Android
Bus Pass Android Project
Grocery Shopping Android
Herb & Grocery Shopping Android App
Hotel Management Android Project
Online Driver Hiring Android App

By the way, we will continue to update the listed topics and ideas for final-year software engineering projects using Android for 2024 above so you can find more project ideas that can be used in the future.

Aside from this list of best final year projects for software engineering student 2024 , I will also give you a list of final year projects for students and engineers with source code .

We have also final year projects for computer science Engineering with source code

Student Management System Project In Django With Source Code
Billing System In PHP With Source Code
Loan Management System Project In PHP With Source Code
College Management System Project in Django with Source Code

You may also visit if you are a BSIT Student and looking for the best Thesis title Proposal for IT/CS students you can click it here.

You can also have some articles might help you doing your document to support your Software Engineering project topics for final years :

How to Make An Effective Thesis or Capstone Document
Writing A Good Research Title For Thesis or Capstone Project
Chapter 1(Research Description) Capstone Project Guidelines and Sample
Chapter 2 in Thesis Writing for IT/CS Students (with Sample)

We also recommended Books, Course, Compiler, etc.

Python Compiler Online and Offline
Free Python Certification | Which and How to Get Best Python Certification
Best Python IDE for Windows, Linux, Mac OS
List of Python Interpreters | Guide to Best Python Interpreter Online

Finally, the list of final year Software Engineering projects for students and engineers study includes programming , design , analysis , and theory .

Engineering also comes to involve coming up with and development of different application-based code.

In addition, these final year projects for Software Engineering Students for 2024 list will be enforced by a variety of tools like Java, VB.NET Application, Databases, Oracle, and the likes.

Note: If you have any questions or suggestions about the list of the best final year project ideas and topics for software engineering students, then please feel free to contact us at our contact page or leaving a comment below. Also, if you have ideas related to this topic please let us know.

3 thoughts on “Software Engineering Project Ideas (2024)”

Thanks for the great post you posted. I like the content which was mentioned above. If any of the final year students are looking for the software engineering projects

Thanks for the great post you posted. I like the content. thankyou.

In today’s ever-evolving technological landscape, mobile applications have become an integral part of our daily lives. With the increasing demand for innovative and user-friendly apps, Android App Development has become a popular choice for businesses and individuals alike. The possibilities for creating engaging and lucrative projects are endless. In this blog, we will explore some exciting Smart Android Project ideas for the year 2023. So, grab your thinking caps and let’s dive into the world of Android App Development!

software engineering research topics 2022

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Top 10 Software Engineer Research Topics for 2024

What are Software Engineer Research Topics?

Top Software Engineer Research Topics

2. Natural Language Processing

3. Applications of Data Mining in Software Engineering

4. Data Modeling

5. Verification and Validation

6. Software Project Management

7. Software Quality

8. Ontology

9. Software Models

Why is Software Engineering Required?

2. Scalability

3. Large Software

4. Dynamic Nature

5. Better Quality Management

Frequently Asked Questions (FAQs)

Eshaan Pandey

Upcoming Programming Batches & Dates

Software Engineering Institute

Monday, November 14, 2022

Research Topics & Ideas: CompSci & IT

Overview: CompSci Research Topics

Topics/Ideas: Algorithms & Data Structures

Topics & Ideas: Artificial Intelligence (AI)

Topics & Ideas: Networking

Topics & Ideas: Database Systems

Topics & Ideas: Human-Computer Interaction

Topics & Ideas: Information Security

Topics & Ideas: Software Engineering

CompSci & IT Dissertations/Theses

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Software Engineering Research Ideas

Software Engineering Institute

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

Anita Carleton

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Software Engineering

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Computer Science > Software Engineering

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References & Citations

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software engineering Recently Published Documents

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

Opportunities and Challenges in Code Search Tools

Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines

Towards an Anatomy of Software Craftsmanship

On the Reproducibility and Replicability of Deep Learning in Software Engineering

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

Software—A New Open Access Journal on Software Engineering

Improving bioinformatics software quality through incorporation of software engineering practices

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

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

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Metric-centered and technology-independent architectural views for software comprehension

Back to the future: origins and directions of the “Agile Manifesto” – views of the originators

Investigating the effectiveness of peer code review in distributed software development based on objective and subjective data

On the benefits and challenges of using kanban in software engineering: a structured synthesis study

Challenges on applying genetic improvement in JavaScript using a high-performance computer

Actor’s social complexity: a proposal for managing the iStar model

Investigating measures for applying statistical process control in software organizations

An approach for applying Test-Driven Development (TDD) in the development of randomized algorithms

Supporting governance of mobile application developers from mining and analyzing technical questions in stack overflow

Working software over comprehensive documentation – Rationales of agile teams for artefacts usage

Development as a journey: factors supporting the adoption and use of software frameworks

Applying user-centered techniques to analyze and design a mobile application

A measurement model to analyze the effect of agile enterprise architecture on geographically distributed agile development

A survey of search-based refactoring for software maintenance

Guest editorial foreword for the special issue on automated software testing: trends and evidence

An algorithm for combinatorial interaction testing: definitions and rigorous evaluations

How diverse is your team? Investigating gender and nationality diversity in GitHub teams

Investigating factors that affect the human perception on god class detection: an analysis based on a family of four controlled experiments

On the evaluation of code smells and detection tools

On the influence of program constructs on bug localization effectiveness

DyeVC: an approach for monitoring and visualizing distributed repositories