13 citations · 20 across the 23 of their papers we have counts for
4 papers · 1 filter
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…
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…
MarsCode Agent: AI-native Automated Bug Fixing
Yizhou Liu, Pengfei Gao, Xinchen Wang +4
Recent advances in large language models (LLMs) have shown significant potential to automate various software development tasks, including code completion, test generation, and bug…