From the 1 of 6 linked papers with an AI index.
6 papers
Knowledge-Guided Synthetic Bug Feedback for LLM-Based Unit Test Generation
Ziheng Wang, Maike Li, Chen Zhi
The paper proposes a framework that converts historical bug mechanisms into synthetic bugs to guide large language models in generating more effective unit tests that can detect re…
EpiDroid: Dependency-Guided Recomposition for Deep State Discovery in Mobile GUI Testing
Jiahui Song, Jiaxin Zhi, Kangjia Zhao +6
The increasing scale and complexity of mobile applications make automated GUI exploration essential for software quality assurance. However, existing methods often neglect state de…
CodeGlance: Understanding Code Reasoning Challenges in LLMs through Multi-Dimensional Feature Analysis
Yunkun Wang, Xuanhe Zhang, Junxiao Han +2
In modern software development, developers frequently need to understand code behavior at a glance -- whether reviewing pull requests, debugging issues, or navigating unfamiliar co…
Empowering RepoQA-Agent based on Reinforcement Learning Driven by Monte-carlo Tree Search
Guochang Li, Yuchen Liu, Zhen Qin +7
Repository-level software engineering tasks require large language models (LLMs) to efficiently navigate and extract information from complex codebases through multi-turn tool inte…
InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration
Yunkun Wang, Yue Zhang, Guochang Li +5
Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose. While existing LLM-based self-repair approaches conduct inte…
ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration
Yunkun Wang, Yue Zhang, Zhen Qin +5
Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhausti…