一句话：提出面向编程作业修复的微调策略与推理加速方法，提升LLM修复效果与速度。
该论文针对编程教育中为学生编程作业提供个性化、及时反馈的需求，研究基于大语言模型（LLM）的自动程序修复（APR）。现有面向APR的微调策略在复杂编程作业中难以引导LLM准确识别缺陷并做出正确编辑，且自回归解码效率低，影响反馈及时性。为此，作者提出FastFixer：首先设计一种面向修复的微调策略，增强LLM对生成补丁及其相关上下文的注意力；其次提出专用于程序修复任务的推理加速方法，以加快补丁生成。评估表明，与最先进基线相比，FastFixer在作业修复上整体提升20.46%，且相比自回归解码实现16.67倍推理加速。
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 identifying and repairing bugs, we first propose a novel repair-oriented fine-tuning strategy, aiming to enhance the LLM’s attention towards learning how to generate the necessary patch and its associated context. Furthermore, to speed up the patch generation, we propose an inference acceleration approach that is specifically tailored for the program repair task. The evaluation results demonstrate that FastFixer obtains an overall improvement of 20.46% in assignment fixing when compared to the state-of-the-art baseline. Considering the repair efficiency, FastFixer achieves a remarkable inference speedup of 16.67× compared to the autoregressive decoding algorithm.CCS CONCEPTS• Software and its engineering; • Computing methodologies → Artificial intelligence;
@inproceedings{0032LZ00S0LM24,
title = {FastFixer: An Efficient and Effective Approach for Repairing Programming Assignments},
author = {Fang Liu and Zhenwei Liu and Qianhui Zhao and Jing Jiang and Li Zhang and Zian Sun and Ge Li and Zhongqi Li and Yuchi Ma},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695062}
}