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 grounded in real-world software development practice.
Our evaluation approach focuses on the following aspects:
- Contamination-awareness: Ensuring the evaluation process is not affected by external interferences.
- In-the-wild agentic behavior assessment: Evaluating agents' performance in actual working environments.
- Trajectory-aware benchmarks and metrics: Capturing relevant indicators of realistic coding contexts, human-aligned behavior, and model failure modes.
Through these methods, we aim to provide developers with more reliable evaluation tools for agents, promoting the application of large language models in software engineering.
Blogger's Review: This article presents an evaluation method for large language models in software development, emphasizing the importance of real-world contexts and addressing the shortcomings of traditional evaluation approaches. As technology advances, accurately assessing model capabilities will be crucial for driving development forward.