Cultivating Software Quality Improvement in the Classroom: An Experience with ChatGPT

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 paper, our aim is to shed light on our experience in teaching the use of Programming Mistake Detector (PMD) to cultivate a bugfix culture and leverage LLMs to improve software quality in educational settings. This paper discusses the results of an experiment involving 102 submissions that carried out a code review activity of 1,230 rules. Our quantitative and qualitative analysis reveals that a set of PMD quality issues influences the acceptance or rejection of the issues, and design-related categories that take longer to resolve. Although 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 LLMs and promote software quality in education.