6 papers
Learning Globally Reusable Skills for Coding Agents
Chen Yang, Jiashuo Tian, Ziqi Wang +3
Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining. However, existing approaches typically treat skill evoluti…
STELLAR: Structure-guided LLM Assertion Retrieval and Generation for Formal Verification
Saeid Rajabi, Chengmo Yang, Satwik Patnaik
Formal Verification (FV) relies on high-quality SystemVerilog Assertions (SVAs), but the manual writing process is slow and error-prone. Existing LLM-based approaches either genera…
Clarifying Semantics of In-Context Examples for Unit Test Generation
Chen Yang, Lin Yang, Ziqi Wang +3
Recent advances in large language models (LLMs) have enabled promising performance in unit test generation through in-context learning (ICL). However, the quality of in-context exa…
Reflective Unit Test Generation for Precise Type Error Detection with Large Language Models
Chen Yang, Ziqi Wang, Yanjie Jiang +4
Type errors in Python often lead to runtime failures, posing significant challenges to software reliability and developer productivity. Existing static analysis tools aim to detect…
Advancing Code Coverage: Incorporating Program Analysis with Large Language Models
Chen Yang, Junjie Chen, Bin Lin +2
Automatic test generation plays a critical role in software quality assurance. While the recent advances in Search-Based Software Testing (SBST) and Large Language Models (LLMs) ha…
A Survey of Reinforcement Learning for Software Engineering
Dong Wang, Hanmo You, Lingwei Zhu +6
Reinforcement Learning (RL) has emerged as a powerful paradigm for sequential decision-making and has attracted growing interest across various domains, particularly following the…