5 papers
An Empirical Study on LLM-based Agents for Automated Bug Fixing
Xiangxin Meng, Zexiong Ma, Pengfei Gao +1
Large language models (LLMs) and LLM-based Agents have been applied to fix bugs automatically, demonstrating the capability in addressing software defects by engaging in developmen…
Tool-integrated Reinforcement Learning for Repo Deep Search
Zexiong Ma, Chao Peng, Qunhong Zeng +3
Issue localization, the process of identifying code locations that need modification to resolve software issues, is a critical yet challenging task in software development. The sem…
SoRFT: Issue Resolving with Subtask-oriented Reinforced Fine-Tuning
Zexiong Ma, Chao Peng, Pengfei Gao +3
Mainstream issue-resolving frameworks predominantly rely on commercial models, leading to high costs and privacy concerns. Existing training approaches for issue resolving struggle…
Repository Structure-Aware Training Makes SLMs Better Issue Resolver
Zexiong Ma, Shengnan An, Zeqi Lin +2
Language models have been applied to various software development tasks, but the performance varies according to the scale of the models. Large Language Models (LLMs) outperform Sm…
Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation
Zexiong Ma, Shengnan An, Zeqi Lin +3
Large language models (LLMs) are susceptible to generating hallucinated information, despite the integration of retrieval-augmented generation (RAG). Parallel context extension (PC…