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 substantially shapes how SE agents interact with repositories, execute actions, and validate solutions. However, the role of harness design rem...
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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