A systematic empirical study of harness effects in SE agents shows that harness effectiveness depends jointly on model capability and task type, and highlights harness design as a first-class factor in SE-agent performance and provides insights for building more effective and efficient coding agents.
Hai-Chuan Hu, Quan-Jun Zhang, Sheng-Cheng Yu et al.· 2 citations
This analysis covers 2,857 report-test-patch triplets from Defects4J and SWT-Bench using two widely adopted instruction-tuned LLMs from distinct model families and finds that both models exhibit systematic optimism relative to humans and only modest rank agreement, motivating bias-aware evaluation.
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang et al.· arXiv.org· 0 citations
ReProAgent is a multi-stage agent framework for reproduction test generation from issue reports that decomposes the task into four agent stages: bug localization, root cause analysis, test planning, and test generation, and generalizes across multiple backbone LLMs.
Quanjun Zhang, Yi Zheng, Ye Shang et al.· 1 citation
TestAgent is proposed, an LLM-based test generation approach that addresses the above limitations by emulating human testing practices via a multi-agent collaboration mechanism and equips TestAgent with a set of tool APIs that can be invoked dynamically in an on-demand and adaptive manner.
Quanjun Zhang, Ye Shang, Siqi Gu et al.· arXiv.org· 0 citations
This survey aims to clarify the conceptual boundaries of self-evolving coding agents and provide a foundation for designing more adaptive, reliable, and software-aware agentic systems.
Hao Zhou, Hai-Chuan Hu, Tianyu Luo et al.· 0 citations
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