Reliable and Developer-Aligned Evaluation of Agents for Software Engineering

2026-07-09 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, medium

Summary

A research methodology for reliably evaluating Large Language Model (LLM)-powered agents in software engineering is proposed, addressing limitations of current fragmented and hypothetical evaluation techniques. Presented at FSE '26 in July 2026, this approach emphasizes contamination-awareness, in-the-wild agentic behavior assessment, and trajectory-aware benchmarks. It aims to capture realistic coding contexts, human-aligned behavior, and model failure modes, moving beyond unreliable NLP metrics like BLEU or ROUGE and flawed benchmarks like HumanEval. The research plan includes a systematic review of 279 peer-reviewed papers on 26 coding tasks to establish an evaluation roadmap. It also involves "in-the-wild" evaluation of agents such as OpenAI Codex and GitHub Copilot in open-source repositories, analyzing their impact on codebase maintainability. Finally, it introduces a multilingual, contamination-aware benchmark for end-to-end issue resolution in evolving software repositories.

Key takeaway

For Machine Learning Engineers or Research Scientists evaluating LLM-powered agents in software engineering, recognize that current benchmarks often provide misleading performance metrics. You should prioritize evaluation methodologies that are contamination-aware, assess agent behavior in real-world development environments, and utilize trajectory-aware benchmarks. This ensures your agent assessments reflect true capabilities and human-aligned utility, moving beyond synthetic scenarios and unreliable NLP metrics.

Key insights

Current LLM agent evaluations are unreliable; a new methodology is needed for real-world software engineering contexts.

Principles

• Evaluation must be contamination-aware.
• Assess agent behavior in real-world settings.
• Benchmarks need trajectory-awareness for evolving code.

Method

The proposed methodology involves a systematic literature review, "in-the-wild" assessment of agentic behavior in open-source repositories, and designing change-responsive, contamination-aware benchmarks for issue resolution.

In practice

• Audit datasets for contamination.
• Analyze agent behavior in open-source repos.
• Design benchmarks for evolving requirements.

Topics

• Large Language Models
• Autonomous AI Agents
• Software Engineering
• LLM Evaluation
• Benchmarking
• Data Contamination

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.