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  • SetupBench: Assessing Software Engineering Agents Ability to Bootstrap . . .
    These agents run code inside secure sandboxes with fixed toolchains of widely used languages, packages, and dependencies, leaving many task-specific aspects of environment setup to the agent itself Environment setup and dependency management represents a critical yet overlooked capability: recent empirical studies consistently place installation, dependency resolution, and build configuration
  • EnvBench: A Benchmark for Automated Environment Setup | AI Research . . .
    Can AI finally handle the headache of setting up your coding environment for any project? EnvBench: A Benchmark for Automated Environment Setup Published 3 18 2025 by Aleksandra Eliseeva, Alexander Kovrigin, Ilia Kholkin, Egor Bogomolov, Yaroslav Zharov
  • GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks . . .
    Beyond scratch coding, exploiting large-scale code repositories (e g , GitHub) for practical tasks is vital in real-world software development, yet current benchmarks rarely evaluate code agents in such authentic, workflow-driven scenarios To bridge this gap, we introduce GitTaskBench, a benchmark designed to systematically assess this capability via 54 realistic tasks across 7 modalities and
  • ABSTRACT arXiv:2503. 14443v1 [cs. LG] 18 Mar 2025
    In this work, we focus on another repository-level task that programmers face regularly— environment setup, i e , configuring the system to work with an arbitrary software project, for in-stance, a freshly cloned GitHub repository It usually entails installing the dependencies but might include arbitrary project-specific steps, such as installing additional system packages, setting the
  • Automated Benchmark Generation for Repository-Level Coding Tasks
    Code Agent development is an extremely active research area, where a reliable performance metric is critical for tracking progress and guiding new developments This demand is underscored by the meteoric rise in popularity of SWE-Bench This benchmark challenges code agents to generate patches addressing GitHub issues given the full repository as context The correctness of generated patches
  • Process-Level Trajectory Evaluation for Environment Configuration in . . .
    Agent Methods Early agent attempts to automate environment setup relied on specific heuristics that infer dependencies from source code, offering determinism but falling short on system packages, version pinning, and platform heterogeneity [Gruber and Fraser, 2023, Zhang et al , 2024, Yang et al , 2025]
  • Automated Benchmark Generation for Repository-Level Coding Tasks
    To achieve the automatic generation of challenging and realistic repository-level coding benchmarks, this work proposes an LLM-driven method, SETUPAGENT, to automate the extraction of valid information from complex real-world repositories, ensuring the correct setup of the environment for perfectly reproducing issues encountered in practice
  • ResearchEnvBench: Benchmarking Agents on Environment Synthesis for . . .
    The Pyramid of Runtime Verification: We propose a rigorous evaluation protocol that moves beyond static analysis We formalize environment setup as a hierarchical capability ladder, requiring agents to pass a sequence of checks ranging from dependency integrity to multi-GPU distributed data parallel (DDP) execution
  • SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution . . .
    Building on these capabilities, multi-agent systems, where specialized agents collaborate on subtasks such as repository navigation, bug localization, patch generation, and verification, have evolved rapidly to address long-horizon challenges in SE, outpacing single-agent architectures in scalability and performance as of 2025
  • Automated Benchmark Generation for Repository-Level Coding Tasks
    Comparing these datasets to SWE-Bench with respect to their characteristics and code agent performance, we find significant distributional differences, including lower issue description quality and detail level, higher fix complexity, and most importantly up to 40% lower agent success rates View arXiv page View PDF Add to collection





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