DebugTA: An LLM-Based Agent for Simplifying Debugging and Teaching in Programming Education

Lingyue Fu, Haowei Yuan, Datong Chen, Xinyi Dai, Qingyao Li, Weinan Zhang, Weiwen Liu, Yong Yu

Keywords: Debugging and Teaching, LLM Agents, Programming Education

Abstract: In programming education, Debugging and Teaching (DT) task is a common scenario where students receive assistance in correcting their erroneous code. The task involves multiple inputs, including erroneous code, error messages, reference solutions, and the question description, with the goal of generating modification suggestions to the erroneous code. However, two key challenges hinder the effectiveness of existing approaches. Firstly, the complexity and heterogeneity of inputs inherent in DT tasks significantly elevate the reasoning challenges faced by LLMs. Second, existing approaches often fail to fully leverage the availability of standard code in DT tasks, forcing models to rely solely on complex multi-step reasoning, which limits the potential of LLMs in addressing DT tasks effectively. To address these challenges, we propose DebugTA, a novel LLM-based debugging and teaching agent. DebugTA is equipped with a set of specialized tools, including a standard code retrieval tool, a variable substitution tool for generating an aligned reference code, and an external compiler interface for real-time code analysis and validation. Guided by explicit pedagogical and debugging principles, DebugTA acts as an agent that decomposes a complex task into sequential LLM interactions, each utilizing distinct tools for specific subtasks, thereby simplifying the logical reasoning at each step and reducing overall reasoning complexity. Furthermore, DebugTA utilizes tool calls to align the standard code with the erroneous code as much as possible, allowing the LLM to focus on logic errors within the erroneous code and improving the accuracy of the generated suggestions. To rigorously assess the quality of modification suggestions, we introduce a student simulator-teacher interaction paradigm. Experimental results on three real-world code datasets demonstrate that DebugTA consistently improves teaching effectiveness while significantly reducing computational costs.

Area: Generative and Agentic AI (GAAI)

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Submission Number: 337