Ponencia JISBD/JCIS/PROLE 2021 – Miércoles, 22 de septiembre de 2021 (15:00-16:30)
Ponente: Prof. Lionel C. Briand
Lionel C. Briand is professor of software engineering and has shared appointments between (1) The University of Ottawa, Canada and (2) The SnT centre for Security, Reliability, and Trust, University of Luxembourg. Lionel was elevated to the grades of IEEE Fellow and ACM Fellow. He was the recipient of the IEEE Computer Society Harlan Mills award in 2012 and also received an ERC Advanced grant in 2016. More recently, he was awarded a Canada Research Chair (Tier 1) on “Intelligent Software Dependability and Compliance”. His research interests include: software testing and verification, model-driven software development, applications of AI in software engineering, and empirical software engineering.
Artificial Intelligence and Software Engineering: Past, Present and Future
There is a long history of applications of various Artificial Intelligence (AI) technologies in software engineering. From machine learning, evolutionary computing, to Natural Language Processing, AI has played an increasingly important role in making software engineering more predictable and automatable. This rising impact stems from increasingly powerful AI technologies, easy access to enormous computing power, and the availability of large amounts of software data in readily available development repositories. This talk will provide a reflection over 25 years of experience in applying and tailoring AI techniques to address software engineering problems at scale. Recent developments and future research directions will also be outlined.
Ponencia METODOS (JISBD) – Viernes, 24 de septiembre de 2021 (10:30-11:30) – Diapositivas
Ponente: Prof. Robert Feldt
Robert Feldt is a professor of Software Engineering at Chalmers University of Technology, Sweden, and at Blekinge Institute of Technology, Sweden. He has broad research interests spanning from human factors to hardcore automation and statistics, and work on testing and quality, requirements engineering, as well as human-centred (behavioural) software engineering. Dr Feldt was an early contributor to search-based software engineering and has recently argued for increased application of psychology and social science to understand and improve software engineering. Most of his research is empirical and conducted in close collaboration with industry partners in Sweden, Europe and Asia, but he also leads more basic research. Dr Feldt received a PhD in Computer Engineering from the Chalmers University of Technology in 2002, studied Psychology at Gothenburg University in the ’90s and has also worked as an IT and software consultant for more than 30 years. He is passionate about empirical research and methods and changing organisations through technical innovation, but with the humans in focus. He is co-Editor in Chief of the EMSE journal and on the editorial boards of two other journals.
Empirical Software Engineering as a Science: A Manifesto
The Empirical Software Engineering (ESE) community has made great progress in the last 20 years and expanded the field considerably both in scope, volume as well as quality. Nowadays, we have established conferences as well as journals focused on the area, and a majority of the papers published in the top SE conferences are empirical. However, while more established scientific fields such as Physics, Biology and Psychology have clear identities, specific schools of thought, and explicated research methods, I argue this is less so in ESE.
In this talk, I propose an updated manifesto for empirical software engineering and discuss some challenges and ways we might overcome them. This, I hope, can contribute to a more clear sense of identity and act as a vision. In particular, I discuss the negative effects of our love for novelty and how it affects publication bias and can be a challenge to uncover truths. I also summarize the ongoing debate among statisticians about how to move beyond p-values and provide some ideas for how to improve empirical studies using qualitative methods. I will conclude with concrete call-for-actions so that we can be an even stronger science in the future.