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.

Related papers

• Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning [62.499592503950026]
  Large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments.<n>We propose Agent World Model (AWM), a fully synthetic environment generation pipeline.<n>We scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets.
  arXiv  Detail & Related papers  (2026-02-10T18:55:41Z)
• HerAgent: Rethinking the Automated Environment Deployment via Hierarchical Test Pyramid [15.944450159856602]
  We argue that environment setup success should be evaluated through executable evidence rather than a single binary signal.<n>We propose HerAgent, an automated environment setup approach that incrementally constructs executable environments.
  arXiv  Detail & Related papers  (2026-02-08T08:57:05Z)
• EntWorld: A Holistic Environment and Benchmark for Verifiable Enterprise GUI Agents [12.7922877987936]
  EntWorld is a large-scale benchmark consisting of 1,756 tasks across six representative enterprise domains.<n>We propose a schema-grounded task generation framework that directly reverse-engineers business logic from underlying database schemas.<n>We show that state-of-the-art models achieve 47.61% success rate on EntWorld, substantially lower than the human performance.
  arXiv  Detail & Related papers  (2026-01-25T06:58:15Z)
• ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development [72.4729759618632]
  We introduce ABC-Bench, a benchmark to evaluate agentic backend coding within a realistic, executable workflow.<n>We curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories.<n>Our evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks.
  arXiv  Detail & Related papers  (2026-01-16T08:23:52Z)
• Process-Level Trajectory Evaluation for Environment Configuration in Software Engineering Agents [71.85020581835042]
  Large language model-based agents show promise for software engineering, but environment configuration remains a bottleneck.<n>Existing benchmarks assess only end-to-end build/test success, obscuring where and why agents succeed or fail.<n>We introduce Enconda-bench, which provides process-level trajectory assessment of fine-grained agent capabilities during environment setup-planning.
  arXiv  Detail & Related papers  (2025-10-29T16:59:07Z)
• SCUBA: Salesforce Computer Use Benchmark [63.66753028386581]
  SCUBA is a benchmark designed to evaluate computer-use agents on customer relationship management ( CRM) within the Salesforce platform.<n> SCUBA contains 300 task instances derived from real user interviews, spanning three primary personas, platform administrators, sales representatives, and service agents.<n>We benchmark a diverse set of agents under both zero-shot and demonstration-augmented settings.
  arXiv  Detail & Related papers  (2025-09-30T16:48:49Z)
• GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging [41.754784344572286]
  We release GitTaskBench, a benchmark for evaluating code agents in real-world scenarios.<n>Each task pairs a relevant repository with an automated, human-curated evaluation harness.<n>We also propose the alpha-value metric to quantify the economic benefit of agent performance.
  arXiv  Detail & Related papers  (2025-08-26T12:48:05Z)
• OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks [52.87238755666243]
  We present OmniEAR, a framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks.<n>We model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains.<n>Our systematic evaluation reveals severe performance degradation when models must reason from constraints.
  arXiv  Detail & Related papers  (2025-08-07T17:54:15Z)
• OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety [58.201189860217724]
  We introduce OpenAgentSafety, a comprehensive framework for evaluating agent behavior across eight critical risk categories.<n>Unlike prior work, our framework evaluates agents that interact with real tools, including web browsers, code execution environments, file systems, bash shells, and messaging platforms.<n>It combines rule-based analysis with LLM-as-judge assessments to detect both overt and subtle unsafe behaviors.
  arXiv  Detail & Related papers  (2025-07-08T16:18:54Z)
• SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents [16.08820954102608]
  Large Language Models (LLMs) demonstrate impressive general-purpose reasoning and problem-solving abilities.<n>LLMs struggle with executing complex, long-horizon that demand strict adherence to Standard Operating Procedures.<n>We develop SOP-Bench, a benchmark of over 1,800 tasks across 10 industrial domains.
  arXiv  Detail & Related papers  (2025-06-09T18:20:12Z)
• SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
  SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
  arXiv  Detail & Related papers  (2025-03-11T17:53:02Z)
• Agentless: Demystifying LLM-based Software Engineering Agents [12.19683999553113]
  We build Agentless -- an agentless approach to automatically solve software development problems. Compared to the verbose and complex setup of agent-based approaches, Agentless employs a simplistic three-phase process of localization, repair, and patch validation. Our results on the popular SWE-bench Lite benchmark show that surprisingly the simplistic Agentless is able to achieve both the highest performance and low cost.
  arXiv  Detail & Related papers  (2024-07-01T17:24:45Z)
• WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks? [83.19032025950986]
  We study the use of large language model-based agents for interacting with software via web browsers. WorkArena is a benchmark of 33 tasks based on the widely-used ServiceNow platform. BrowserGym is an environment for the design and evaluation of such agents.
  arXiv  Detail & Related papers  (2024-03-12T14:58:45Z)
• AgentBench: Evaluating LLMs as Agents [88.45506148281379]
  Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. We present AgentBench, a benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
  arXiv  Detail & Related papers  (2023-08-07T16:08:11Z)
• WebArena: A Realistic Web Environment for Building Autonomous Agents [92.3291458543633]
  We build an environment for language-guided agents that is highly realistic and reproducible. We focus on agents that perform tasks on the web, and create an environment with fully functional websites from four common domains. We release a set of benchmark tasks focusing on evaluating the functional correctness of task completions.
  arXiv  Detail & Related papers  (2023-07-25T22:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.