Learner-Tailored Program Repair: A Solution Generator with Iterative Edit-Driven Retrieval Enhancement

• URL: http://arxiv.org/abs/2601.08545v2
• Date: Sun, 18 Jan 2026 15:44:30 GMT
• Title: Learner-Tailored Program Repair: A Solution Generator with Iterative Edit-Driven Retrieval Enhancement
• Authors: Zhenlong Dai, Zhuoluo Zhao, Hengning Wang, Xiu Tang, Sai Wu, Chang Yao, Zhipeng Gao, Jingyuan Chen,
• Abstract summary: We propose a framework to enhance program repair while offering the bug descriptions for the buggy code.<n>In the first stage, we utilize a solution retrieval framework to construct a solution retrieval database.<n>In the second stage, we propose a solution-guided program repair method, which fixes the code and provides explanations.
• Score: 33.04212496723856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the development of large language models (LLMs) in the field of programming, intelligent programming coaching systems have gained widespread attention. However, most research focuses on repairing the buggy code of programming learners without providing the underlying causes of the bugs. To address this gap, we introduce a novel task, namely LRP (Learner-Tailored Program Repair). We then propose a novel and effective framework, LSGEN (Learner-Tailored Solution Generator), to enhance program repair while offering the bug descriptions for the buggy code. In the first stage, we utilize a repair solution retrieval framework to construct a solution retrieval database and then employ an edit-driven code retrieval approach to retrieve valuable solutions, guiding LLMs in identifying and fixing the bugs in buggy code. In the second stage, we propose a solution-guided program repair method, which fixes the code and provides explanations under the guidance of retrieval solutions. Moreover, we propose an Iterative Retrieval Enhancement method that utilizes evaluation results of the generated code to iteratively optimize the retrieval direction and explore more suitable repair strategies, improving performance in practical programming coaching scenarios. The experimental results show that our approach outperforms a set of baselines by a large margin, validating the effectiveness of our framework for the newly proposed LPR task.

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