activity
20232026
most citedAdvancing Code Coverage: Incorporating Program Analysis with Large Language Models

7 citations · 19 across the 9 of their papers we have counts for

collaborators

9 papers

cs.SE2026

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…

cs.SE2026

Characterizing and Mitigating False-Positive Bug Reports in the Linux Kernel

Jiashuo Tian, Dong Wang, Chen Yang +3

False-positive bug reports represent a significant yet underexplored challenge in the development and maintenance of the Linux kernel. They occur when correct system behavior is mi…

cs.SE2025★ 1 cited

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…

cs.SE2025★ 1 cited

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…

cs.SE2025★ 2 cited

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…

cs.SE2024★ 5 cited

Dependency-Aware Code Naturalness

Chen Yang, Junjie Chen, Jiajun Jiang +1

Code naturalness, which captures repetitiveness and predictability in programming languages, has proven valuable for various code-related tasks in software engineering. However, pr…

cs.SE2024★ 2 cited

On the Evaluation of Large Language Models in Unit Test Generation

Lin Yang, Chen Yang, Shutao Gao +8

Unit testing is an essential activity in software development for verifying the correctness of software components. However, manually writing unit tests is challenging and time-con…

cs.SE2024★ 7 cited

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…

cs.SE2023★ 1 cited

COCO: Testing Code Generation Systems via Concretized Instructions

Ming Yan, Junjie Chen, Jie M. Zhang +3

Code generation systems have been extensively developed in recent years to generate source code based on natural language instructions. However, despite their advancements, these s…