LLM code generators solve most introductory programming assignments but rarely produce fully correct solutions to advanced CS4 and CS5 assignments, often identifying the algorithm while missing problem-specific constraints.
Insights from the Frontline: GenAI Utilization Among Software Engineering Students
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abstract
Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students.
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Evaluating Code Generation of LLMs in Advanced Computer Science Problems
LLM code generators solve most introductory programming assignments but rarely produce fully correct solutions to advanced CS4 and CS5 assignments, often identifying the algorithm while missing problem-specific constraints.