FastFixer: An Efficient and Effective Approach for Repairing Programming Assignments
Fang Liu, Zhenwei Liu, Qianhui Zhao, Jing Jiang, Li Zhang, Ge Li, Zian, Sun, Zhongqi Li, Yuchi Ma

TL;DR
FastFixer is a novel approach that enhances the accuracy and speed of automated programming assignment repair using a specialized fine-tuning strategy and inference acceleration, significantly outperforming existing methods.
Contribution
It introduces a repair-oriented fine-tuning strategy and a tailored inference acceleration method to improve both the effectiveness and efficiency of LLM-based program repair.
Findings
20.46% improvement in assignment fixing accuracy
16.67 times faster inference speed
Effective handling of complex programming bugs
Abstract
Providing personalized and timely feedback for student's programming assignments is useful for programming education. Automated program repair (APR) techniques have been used to fix the bugs in programming assignments, where the Large Language Models (LLMs) based approaches have shown promising results. Given the growing complexity of identifying and fixing bugs in advanced programming assignments, current fine-tuning strategies for APR are inadequate in guiding the LLM to identify bugs and make accurate edits during the generative repair process. Furthermore, the autoregressive decoding approach employed by the LLM could potentially impede the efficiency of the repair, thereby hindering the ability to provide timely feedback. To tackle these challenges, we propose FastFixer, an efficient and effective approach for programming assignment repair. To assist the LLM in accurately…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Cloud Computing and Resource Management · Advanced Data Storage Technologies
