Mohamed Mkaouer on Cultivating Software Quality Improvement in the Classroom

Large Language Models (LLMs), like ChatGPT, have gained widespread popularity and usage in various software engineering tasks, including programming, testing, code review, and program comprehension. However, their effectiveness in improving software quality in the classroom remains uncertain. In this talk, we aim to shed light on our experience in teaching the use of ChatGPT to cultivate a bugfix culture and leverage LLMs to improve software quality in educational settings. This work discusses the results of an experiment involving student submissions that carried out a code review activity of using multiple tools and technologies. Our quantitative and qualitative analyses reveal that while students acknowledge the potential of using ChatGPT during code review, some skepticism persists. We envision our findings to enable educators to support students with code review strategies to raise students’ awareness about LLM and promote software quality in the classroom.
Bio: Mohamed Wiem Mkaouer is an Associate Professor in the College of Innovation & Technology at the University of Michigan-Flint. Dr. Mkaouer is the graduate director of the Master’s in Software Engineering and the Master’s in Artificial Intelligence. Dr. Mkaouer’s research interests are at the intersection of Software Engineering and Artificial Intelligence. It includes software quality assurance, and systems refactoring, and he has co-authored over 140 peer-reviewed papers, including works appearing in top venues like TSE, TOSEM, EMSE, ICSE, FSE, CHI, and ASE. Dr. Mkaouer has been PI/Co-PI on $1.5 million in externally funded projects. He is the recipient of 5 best-paper / presentation awards, and he is the recipient of the Rochester Institute of Technology 2020 GCCIS best-emerging scholar award.