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Legislation and public sentiment throughout the world have promoted fairness metrics, explainability, and interpretability as prescriptions for the responsible development of ethical artificial intelligence systems. Despite the importance…
Background: Research software plays an important role in solving real-life problems, empowering scientific innovations, and handling emergency situations. Therefore, the correctness and trustworthiness of research software are of absolute…
Deep Recommender Systems (DRS) are increasingly dependent on a large number of feature fields for more precise recommendations. Effective feature selection methods are consequently becoming critical for further enhancing the accuracy and…
Context: Empirical Software Engineering (ESE) drives innovation in SE through qualitative and quantitative studies. However, concerns about the correct application of empirical methodologies have existed since the 2006 Dagstuhl seminar on…
Computer science (CS) education needs to evolve to support software and artificial intelligence (AI) systems engineering, and it needs to happen now -- precisely because the core intellectual contributions of CS have never been more…
Software plays a central role in scientific discovery. Improving how we develop and use software for research can have both broad and deep impacts on a spectrum of challenges and opportunities society faces today. The emergence of Research…
Software (SW) development is a labor intensive activity. Modern software projects generally have to deal with producing and managing large and complex software products. Developing such software has become an extremely challenging job not…
Background: The development of scientific software applications is far from trivial, due to the constant increase in the necessary complexity of these applications, their increasing size, and their need for intensive maintenance and reuse.…
A large number of computational scientific research projects make use of open source software packages. However, the development process of such tools frequently differs from conventional software development; partly because of the nature…
Sustainable Software Engineering (SSE) is slowly becoming an industry need for reasons including reputation enhancement, improved profits and more efficient practices. However, SSE has many definitions, and this is a challenge for…
How should software engineering be adapted for Computational Science (CS)? If we understood that, then we could better support software sustainability, verifiability, reproducibility, comprehension, and usability for CS community. For…
Dyslexia is a common learning disorder that primarily impairs an individual's reading and writing abilities. In adults, dyslexia can affect both professional and personal lives, often leading to mental challenges and difficulties acquiring…
Continuous Software Engineering (CSE) is widely adopted in the industry, integrating practices such as Continuous Integration and Continuous Deployment (CI/CD). Beyond technical aspects, CSE also encompasses business activities like…
As computer systems become more and more complex, software and tools lag more and more behind. This is especially true for scientific software that often demands high performance, and thus needs to take advantage of parallelisms, memory…
Software systems impact society at different levels as they pervasively solve real-world problems. Modern software systems are often so sophisticated that their complexity exceeds the limits of human comprehension. These systems must…
The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists. While collaboration can always be challenging, ML introduces…
More and more enterprises recently intend to deploy data management systems in the cloud. Due to the professionalism of software development, it has still been difficult for non-programmers to develop this kind of systems, even a small one.…
Software development comprises complex tasks which are performed by humans. It involves problem solving, domain understanding and communication skills as well as knowledge of a broad variety of technologies, architectures, and solution…
Background: Sustainable software engineering (SSE) means creating software in a way that meets present needs without undermining our collective capacity to meet our future needs. It is typically conceptualized as several intersecting…
Large Language Models (LLMs) have become widely used for Software Engineering (SE) tasks, spanning from function-level code generation to complex repository-level workflows. However, the high latency of autoregressive inference remains a…