HypoCompass: Large-Language-Model-based Tutor for Hypothesis Construction in Debugging for Novices

With the prevalence of imperfect but capable LLMs in software development, it becomes increasingly important for developers to cultivate debugging skills -- to form hypotheses about the source of error in both their own codes and codes produced by their AI pair programmers. Despite the necessity, hypothesis construction in debugging is rarely taught due to a lack of explicit instruction. In this work, we explore whether LLMs can be used to train novices on hypothesis construction, by designing a theoretically motivated, LLM-augmented tutor -- HypoCompass. HypoCompass relies on LLMs for generating rich training materials guided by learning principles and presents them in a learning-by-teaching environment, where LLMs act as students who write bugs and attempt to fix them, and human novices focus on debugging in the role of a Teaching Assistant. Evaluations show that HypoCompass consistently generates high-quality training materials, and brings significant learning gain: In a pre-to-post test setup, 10 novices improved their performances by 17%, with a reduced completion time of 13%.