SetupBench: Assessing Software Engineering Agents' Ability to Bootstrap Development Environments
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913938939052032 |
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| author | Arora, Avi Jang, Jinu Moghaddam, Roshanak Zilouchian |
| author_facet | Arora, Avi Jang, Jinu Moghaddam, Roshanak Zilouchian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09063 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SetupBench: Assessing Software Engineering Agents' Ability to Bootstrap Development Environments Arora, Avi Jang, Jinu Moghaddam, Roshanak Zilouchian Software Engineering Artificial Intelligence Machine Learning 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. |
| title | SetupBench: Assessing Software Engineering Agents' Ability to Bootstrap Development Environments |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.09063 |