HapRepair: Learn to Repair OpenHarmony Apps

Zhihao Lin, Mingyi Zhou, Wei Ma, Chi Chen, Yun Yang, Jun Wang, Chunming Hu, Li Li

Abstract: Software defect detection and repair are essential software engineering tasks that mitigate potential risks in the early development stages. Large Language Models (LLMs) have demonstrated significant capabilities in software defect detection and repair. However, it is hard for LLMs to handle the new programming languages such as ArkTS (which is predominantly used in the OpenHarmony platform) due to training data shortage. Additionally, LLM-based multi-defect repair suffers from the limitation of the context window of LLMs. These issues significantly affect the performance of LLM-based defect repair in new programming languages. To address the above challenges, we propose HapRepair, a defect repair framework that integrates static analysis tools with retrieval-augmented generation (RAG) to improve the effectiveness of the defect repair. Specifically, we integrate CodeLinter into our iterative defect repair framework for defect detection, which is the basis of defect repair, and utilize RAG together with ArkAnalyzer to improve the quality of our repair solutions. To overcome the context window limitation of LLMs, we propose the Surrounding Context Extractor and the Context Combination Tool. Experiment results show that HapRepair effectively repairs defects in OpenHarmony Apps, demonstrating high reliability and efficiency in addressing code issues, achieving a defect repair rate of about 99% on the test set, compared to only about 37% when directly using LLMs for defect repair based on the defect information. Our approach demonstrates a robust solution for defect repair on new programming languages that have limited code corpus.

External IDs:dblp:conf/sigsoft/LinZMC00H025