Logic programs are a powerful approach for solving NP-Hard problems. However, their declarative nature poses significant challenges in debugging. Unlike procedural paradigms, which allow for step-by-step inspection of program state, logic programs require reasoning about logical statements for fault localization. This complexity is especially significant in learning environments due to students’ inexperience.
We introduce FormHe, a novel tool that integrates logic-based techniques with Large Language Models (LLMs) to detect and correct issues in Answer Set Programming submissions. FormHe consists of two main components: a fault localization module and a program repair module. First, the fault localization module identifies specific faulty statements in need of modification. Next, FormHe applies program mutation techniques and leverages LLMs to repair the flawed code. The resulting repairs are then used to generate hints that guide students in correcting their programs.
Our experiments with real buggy programs submitted by students show that FormHe accurately detects faults in 94% of cases and successfully repairs 58% of incorrect submissions.
