6 papers
Empowering Autonomous Debugging Agents with Efficient Dynamic Analysis
Jiahong Xiang, Xiaoyang Xu, Xiaopan Chu +2
Autonomous agents for automated program repair represent a promising frontier in software engineering, yet their effectiveness is often hindered by reliance on post-mortem, coarse-…
Agentic Harness for Real-World Compilers
Yingwei Zheng, Cong Li, Shaohua Li +2
Compilers are critical to modern computing, yet fixing compiler bugs is difficult. While recent large language model (LLM) advancements enable automated bug repair, compiler bugs p…
Evaluating and Improving Automated Repository-Level Rust Issue Resolution with LLM-based Agents
Jiahong Xiang, Wenxiao He, Xihua Wang +2
The Rust programming language presents a steep learning curve and significant coding challenges, making the automation of issue resolution essential for its broader adoption. Recen…
SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
Jingxuan Xu, Ken Deng, Weihao Li +36
Evaluating large language models (LLMs) for software engineering has been limited by narrow task coverage, language bias, and insufficient alignment with real-world developer workf…
Tumbling Down the Rabbit Hole: How do Assisting Exploration Strategies Facilitate Grey-box Fuzzing?
Mingyuan Wu, Jiahong Xiang, Kunqiu Chen +4
Many assisting exploration strategies have been proposed to assist grey-box fuzzers in exploring program states guarded by tight and complex branch conditions such as equality cons…
How Far Can We Go with Practical Function-Level Program Repair?
Jiahong Xiang, Xiaoyang Xu, Fanchu Kong +4
Recently, multiple Automated Program Repair (APR) techniques based on Large Language Models (LLMs) have been proposed to enhance the repair performance. While these techniques main…