6 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…
Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling
Trae Research Team, Pengfei Gao, Zhao Tian +12
Software issue resolution is a critical challenge in software engineering and has garnered increasing attention in recent years. With the rapid advancement of large language models…
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…
CodeRepoQA: A Large-scale Benchmark for Software Engineering Question Answering
Ruida Hu, Chao Peng, Jingyi Ren +6
In this work, we introduce CodeRepoQA, a large-scale benchmark specifically designed for evaluating repository-level question-answering capabilities in the field of software engine…
A Real-World Benchmark for Evaluating Fine-Grained Issue Solving Capabilities of Large Language Models
Ruida Hu, Chao Peng, Jingyi Ren +6
Automatically resolving software issues is crucial for software development in practice, impacting the software quality and user experience. The process of resolving real-world iss…
AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions
Xinchen Wang, Pengfei Gao, Xiangxin Meng +4
In software maintenance, bug reproduction is essential for effective fault localization and repair. Manually writing reproduction scripts is a time-consuming task with high require…