4 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-…
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…
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…
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…