14 papers
Beyond the Model: Demystifying Harness Effects in Software Engineering Agents
Haichuan Hu, Quanjun Zhang, Shengcheng Yu +5
Large Language Model (LLM)-based agents are increasingly used for software engineering tasks, yet their performance is not determined by the base model alone. The agent harness sub…
RESCUE-BENCH: Towards Relation-Aware Multi-Party Emotional Support Conversation Systems
Haichuan Hu, Yang Xiao, Mingni Tang +7
Existing emotional support conversation systems mainly focus on one-on-one seeker-supporter interactions and individual emotional states, leaving interpersonal relations in multi-p…
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…
On the Effectiveness of Code Representation in Deep Learning-Based Automated Patch Correctness Assessment
Quanjun Zhang, Haichuan Hu, Chunrong Fang +5
Automated program repair (APR) attempts to generate correct patches and has drawn wide attention from both academia and industry in the past decades. However, APR is continuously s…
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…
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…
Repair-R1: Better Test Before Repair
Haichuan Hu, Xiaochen Xie, Quanjun Zhang
APR (Automated Program Repair) aims to automatically locate program defects, generate patches and validate the repairs. Existing techniques for APR are often combined with LLMs (La…
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.…
LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation
Haichuan Hu, Congqing He, Xiaochen Xie +1
Retrieval-Augmented Generation (RAG) has become a primary technique for mitigating hallucinations in large language models (LLMs). However, incomplete knowledge extraction and insu…