2 citations · 5 across the 17 of their papers we have counts for
17 papers
Self-Evolving Coding Agents
Hao Zhou, Haichuan Hu, Tianyu Luo +5
Large language models are increasingly embedded in software engineering workflows as coding agents that can inspect repositories, invoke tools, execute tests, debug failures, and g…
MultiFixer: A Coordinator-Proposer Based Multi-Agent Framework For Fixing Multi-Hunk Bugs
Haichuan Hu, Chunrong Fang, Ye Shang +5
Automated Program Repair (APR) has benefited greatly from Large Language Models (LLMs), but existing LLM-based APR methods still struggle with multi-hunk bugs that require coordina…
ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reports
Quanjun Zhang, Yi Zheng, Ye Shang +5
Reproduction tests help developers confirm reported issues and provide executable feedback for issue resolution, yet issue reports in open-source projects rarely include such tests…
Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflows
Quanjun Zhang, Ye Shang, Siqi Gu +4
Recently, the emergence of Large Language Models (LLMs) has spurred a surge of research into automated unit test generation, yielding impressive performance and reducing manual eff…
Breaking, Stale, or Missing? Benchmarking Coding Agents on Project-Level Test Evolution
Ye Shang, Quanjun Zhang, Haichuan Hu +3
As production code evolves, the test suite must co-evolve to remain effective. Existing benchmarks for test evolution operate at method-level granularity with pre-paired inputs, by…
Red Skills or Blue Skills? A Dive Into Skills Published on ClawHub
Haichuan Hu, Ye Shang, Quanjun Zhang
Skill ecosystems have emerged as an increasingly important layer in Large Language Model (LLM) agent systems, enabling reusable task packaging, public distribution, and community-d…
ATTest: Agent-Driven Tensor Testing for Deep Learning Library Modules
Zhengyu Zhan, Ye Shang, Jiawei Liu +3
The unit testing of Deep Learning (DL) libraries is challenging due to complex numerical semantics and implicit tensor constraints. Traditional Search-Based Software Testing (SBST)…
SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair
Quanjun Zhang, Chengyu Gao, Yu Han +4
Large Language Models (LLMs) have enabled intelligent agents that autonomously interact with environments and invoke external tools. Recently, agent-based software repair has drawn…
CL4SE: Benchmarking Context Learning on Software Engineering
Haichuan Hu, Quanjun Zhang, Ye Shang +4
Context engineering has emerged as a pivotal paradigm for unlocking the potential of Large Language Models (LLMs) in Software Engineering (SE) tasks, enabling performance gains at…
ComPass: Contrastive Learning for Automated Patch Correctness Assessment in Program Repair
Quanjun Zhang, Ye Shang, Haichuan Hu +3
Automated program repair (APR) attempts to reduce manual debugging efforts and plays a vital role in software maintenance. Despite remarkable progress, APR is still limited in gene…
PSearch: Search-based Patch Generation in the Era of LLM-based Automated Program Repair
Haichuan Hu, Ye Shang, Weifeng Sun +1
Large Language Models (LLMs) have substantially advanced Automated Program Repair (APR), yet most existing LLM-based APR methods still rely on trial-and-error to generate patches.…
Large Language Models for Unit Testing: A Systematic Literature Review
Quanjun Zhang, Chunrong Fang, Siqi Gu +3
Unit testing is a fundamental practice in modern software engineering, with the aim of ensuring the correctness, maintainability, and reliability of individual software components.…
Static Code Analyzer Recommendation via Preference Mining
Xiuting Ge, Chunrong Fang, Xuanye Li +3
Static Code Analyzers (SCAs) have played a critical role in software quality assurance. However, SCAs with various static analysis techniques suffer from different levels of false…
A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing
Ye Shang, Quanjun Zhang, Chunrong Fang +3
Unit testing plays a pivotal role in software development, improving software quality and reliability. However, generating effective test cases manually is time-consuming, promptin…
TestBench: Evaluating Class-Level Test Case Generation Capability of Large Language Models
Quanjun Zhang, Ye Shang, Chunrong Fang +3
Software testing is a crucial phase in the software life cycle, helping identify potential risks and reduce maintenance costs. With the advancement of Large Language Models (LLMs),…
No Man is an Island: Towards Fully Automatic Programming by Code Search, Code Generation and Program Repair
Quanjun Zhang, Chunrong Fang, Ye Shang +3
Automatic programming attempts to minimize human intervention in the generation of executable code, and has been a long-standing challenge in the software engineering community. To…
Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs
Haichuan Hu, Ye Shang, Guolin Xu +2
LLMs have long demonstrated remarkable effectiveness in automatic program repair (APR), with OpenAI's ChatGPT being one of the most widely used models in this domain. Through conti…