🤖 AI Summary
Current evaluation methods for large language model (LLM) agents are fragmented and often divorced from real-world software development contexts, limiting their ability to accurately assess practical engineering capabilities. This work proposes a comprehensive evaluation framework grounded in authentic development practices, integrating for the first time a contamination-aware mechanism, real-environment agent behavior tracing, and trajectory-aware benchmark metrics. The framework systematically evaluates model performance across key dimensions including contextual understanding of code, alignment with human developer behaviors, and failure mode analysis. By bridging the gap between synthetic benchmarks and actual development workflows, this approach substantially enhances both the validity and practical utility of LLM agent evaluations, establishing a more reliable and developer-relevant assessment paradigm.
📝 Abstract
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.