Reliable and Developer-Aligned Evaluation of Agents for Software Engineering
For researchers and practitioners evaluating LLM agents in software engineering, this work offers a more grounded evaluation framework, though it is incremental as it builds on existing concepts.
The paper addresses the lack of reliable evaluation for LLM-powered software engineering agents by proposing a methodology that is contamination-aware, assesses in-the-wild agentic behavior, and uses trajectory-aware benchmarks. The approach aims to provide a more realistic and developer-aligned assessment of agent capabilities.
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.