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This paper introduces a comprehensive evaluation methodology for LLM-powered agents in software engineering, addressing the limitations of current fragmented evaluation techniques that fail to accurately reflect model capabilities. By focusing on contamination-awareness, in-the-wild behavior assessment, and trajectory-aware benchmarks, the authors create metrics that capture realistic coding contexts and human-aligned behaviors. The key result demonstrates that this new evaluation framework provides a more reliable assessment of agent performance in real-world development environments, ultimately enhancing the integration of LLMs into collaborative coding practices.
A new evaluation framework reveals that current assessments of LLM-powered agents often misrepresent their true capabilities in real-world software development.
Large language models are rapidly moving towards closing the development cycle, transitioning from simple assistive companions to autonomous contributors deeply embedded into collaborative development environments. Despite their accelerated adoption, existing evaluation techniques are limited due to their fragmented nature and distorted projection of true model capabilities, often obtained from hypothetical syntactic scenarios. This research aims to bridge this gap by providing a comprehensive evaluation methodology for LLM-powered agents that is grounded in real-world software development practice. Our evaluation approach focuses on contamination-awareness, in-the-wild agentic behavior assessment, and trajectory-aware benchmarks and metrics capturing realistic coding contexts, human-aligned behavior, and model failure modes.
