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Reducing False Positives in Static Bug Detection with LLMs: An Empirical Study in Industry
Xueying Du, Jiayi Feng, Yi Zou +6
Static analysis tools (SATs) are widely adopted in both academia and industry for improving software quality, yet their practical use is often hindered by high false positive rates…
EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents
Yaoqi Guo, Ying Xiao, Jie M. Zhang +4
Software engineering (SE) agents powered by large language models are increasingly adopted in practice, yet they often incur substantial monetary cost. We introduce EET, an experie…
Extracting Conceptual Knowledge to Locate Software Issues
Ying Wang, Wenjun Mao, Chong Wang +5
Issue localization, which identifies faulty code elements such as files or functions, is critical for effective bug fixing. While recent LLM-based and LLM-agent-based approaches im…
Minimizing False Positives in Static Bug Detection via LLM-Enhanced Path Feasibility Analysis
Xueying Du, Kai Yu, Chong Wang +6
Static bug analyzers play a crucial role in ensuring software quality. However, existing analyzers for bug detection in large codebases often suffer from high false positive rates.…
Code Copycat Conundrum: Demystifying Repetition in LLM-based Code Generation
Mingwei Liu, Juntao Li, Ying Wang +9
Despite recent advances in Large Language Models (LLMs) for code generation, the quality of LLM-generated code still faces significant challenges. One significant issue is code rep…
Show Me Why It's Correct: Saving 1/3 of Debugging Time in Program Repair with Interactive Runtime Comparison
Ruixin Wang, Zhongkai Zhao, Le Fang +4
Automated Program Repair (APR) holds the promise of alleviating the burden of debugging and fixing software bugs. Despite this, developers still need to manually inspect each patch…