EVENTS

Machine learning (ML) models trained on code are increasingly integrated into various software engineering tasks. While they generally demonstrate promising performance, many aspects of their capabilities remain unclear. Specifically, there is a lack of understanding regarding what these models learn, why they learn it, how they operate, and when they produce erroneous outputs.

In this talk, I will present findings from a series of studies that (i) examine the abilities of these models to complement human developers, (ii) explore the syntax and representation learning capabilities of ML models designed for software maintenance tasks, and (iii) investigate the patterns of bugs these models exhibit. Additionally, I will discuss a novel self-refinement approach aimed at enhancing the reliability of code generated by Large Language Models (LLMs). This method focuses on reducing the occurrence of bugs before execution, autonomously and without the need for human intervention or predefined test cases.

Bio:
Foutse Khomh is a Full Professor of Software Engineering at Polytechnique Montréal,  a Canada Research Chair Tier 1 on Trustworthy Intelligent Software Systems, a Canada CIFAR AI Chair on Trustworthy Machine Learning Software Systems, an NSERC Arthur B. McDonald Fellow, an Honoris Genius Prize Laureate, and an FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications. He received a Ph.D. in Software Engineering from the University of Montreal in 2011, with the Award of Excellence. He also received a CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize for 2019. His research interests include software maintenance and evolution, machine learning systems engineering, cloud engineering, and dependable and trustworthy ML/AI. His work has received four ten-year Most Influential Paper (MIP) Awards, six Best/Distinguished Paper Awards at major conferences, and two Best Journal Paper of the Year Awards. He initiated and co-organized the Software Engineering for Machine Learning Applications (SEMLA) symposium and the RELENG (Release Engineering) workshop series. He is co-founder of the NSERC CREATE SE4AI: A Training Program on the Development, Deployment, and Servicing of Artificial Intelligence-based Software Systems and one of the Principal Investigators of the DEpendable Explainable Learning (DEEL) project. He is also a co-founder of Quebec's initiative on Trustworthy AI (Confiance IA Quebec) and Scientific co-director of the Institut de Valorisation des Données (IVADO). He is on the editorial board of multiple international software engineering journals (e.g., IEEE Software, EMSE, SQJ, JSEP) and is a Senior Member of IEEE.

Institution

• CISPA Helmholtz Center for Information Security