Title: Process-Level Trajectory Evaluation for Environment Configuration in Software Engineering Agents
Primary area: datasets and benchmarks
Keywords: Software Engineering, Agent, Large Language Model

Abstract: Large language model-based agents show promise for software engineering, but environment configuration remains a bottleneck due to heavy manual effort and scarce large-scale, high-quality datasets.
Existing benchmarks assess only end-to-end build/test success, obscuring where and why agents succeed or fail.
We introduce the Environment Configuration Diagnosis Benchmark, EnConda-bench, which provides process-level trajectory assessment of fine-grained agent capabilities during environment setup-planning, perception-driven error diagnosis, feedback-driven repair, and action to execute the final environment configuration. 
Our task instances are automatically constructed by injecting realistic README errors and are validated in Docker for scalable, high-quality evaluation.
EnConda-bench combines process-level analysis with end-to-end executability to enable capability assessments beyond aggregate success rates.
Evaluations across state-of-the-art LLMs and agent frameworks show that while agents can localize errors, they struggle to translate feedback into effective corrections, limiting end-to-end performance.
To our knowledge, EnConda-bench is the first framework to provide process-level internal capability assessment for environment configuration, offering actionable insights for improving software engineering agents.