Reliable and Developer-Aligned Evaluation of Agents for Software Engineering
Razvan Mihai Popescu
Why It Matters
What makes this one worth your time
Understanding and improving the evaluation of LLM agents in software engineering can lead to more effective and reliable tools for developers.
A new evaluation methodology for LLM agents in software engineering is proposed to better reflect real-world practices.
Summary
The paper proposes a comprehensive evaluation methodology for large language model-powered agents in software engineering, focusing on real-world practices and addressing limitations of existing evaluation techniques.
Key contributions
- Proposes a comprehensive evaluation methodology for LLM agents in software engineering.
- Focuses on contamination-awareness and trajectory-aware benchmarks.
Notable insights
- The emphasis on contamination-awareness and trajectory-aware benchmarks suggests a nuanced approach to evaluating agent behavior in realistic scenarios.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.06713v1 Announce Type: cross 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.