SetupBench: Assessing Software Engineering Agents' Ability to Bootstrap Development Environments

• URL: http://arxiv.org/abs/2507.09063v1
• Date: Fri, 11 Jul 2025 22:45:07 GMT
• Title: SetupBench: Assessing Software Engineering Agents' Ability to Bootstrap Development Environments
• Authors: Avi Arora, Jinu Jang, Roshanak Zilouchian Moghaddam,
• Abstract summary: We introduce setupbench, a benchmark that isolates the environment-bootstrap skill.<n>Our tasks span seven language ecosystems, five database engines, and multi-service orchestration scenarios.<n>We find low success rates across task categories, with particular challenges in repository setup (38.9-57.4%) and local database configuration (20.0-53.3%)
• Score: 2.184775414778289
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Modern Large Language Model (LLM) agents promise end to end assistance with real-world software tasks, yet existing benchmarks evaluate LLM agents almost exclusively in pre-baked environments where every dependency is pre-installed. To fill this gap, we introduce SetupBench, a 93 instance benchmark that isolates the environment-bootstrap skill: starting from a bare Linux sandbox, an agent must install packages, resolve dependency conflicts, initialize databases, and configure background services. Our tasks span seven language ecosystems, five database engines, and multi-service orchestration scenarios, each accompanies by a natural language problem statement and a deterministic success command. Through evaluation of OpenHands, a state-of-the-art coding agent, we find low success rates across task categories, with particular challenges in repository setup (38.9-57.4%) and local database configuration (20.0-53.3%). Our analysis reveals systematic failure modes including incomplete development tooling installation, hallucinated task constraints, and non-persistent environment modifications that break agent-human collaboration workflows. We identify substantial inefficiencies in agent exploration strategies, with 38-89% of actions being unnecessary compared to optimal human behavior. These findings highlight gaps in current agents' practical environment-bootstrap capabilities. By targeting this critical yet under-evaluated capability, SetupBench provides a rigorous yard-stick for the next generation of software developer agents aiming to solve end to end real-wold tasks.

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