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Commit0: Library Generation from Scratch

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arxiv 2412.01769 v1 pith:RCCKXJI2 submitted 2024-12-02 cs.SE cs.AI

Wenting Zhao , Nan Jiang , Celine Lee , Justin T Chiu , Claire Cardie , Matthias Gallé , Alexander M Rush This is my paper
classification cs.SEcs.AI
keywords agentscodecommit0testsunitfeedbackgenerateinteractive
verification ladder T0 review T1 audit T2 compute T3 formal
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With the goal of benchmarking generative systems beyond expert software development ability, we introduce Commit0, a benchmark that challenges AI agents to write libraries from scratch. Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests, with the goal of producing an implementation of this API accordingly. The implementation is validated through running these unit tests. As a benchmark, Commit0 is designed to move beyond static one-shot code generation towards agents that must process long-form natural language specifications, adapt to multi-stage feedback, and generate code with complex dependencies. Commit0 also offers an interactive environment where models receive static analysis and execution feedback on the code they generate. Our experiments demonstrate that while current agents can pass some unit tests, none can yet fully reproduce full libraries. Results also show that interactive feedback is quite useful for models to generate code that passes more unit tests, validating the benchmarks that facilitate its use.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner

    cs.CL 2025-06 conditional novelty 7.0 of 10

    SWE-Flow synthesizes incremental, test-driven development tasks from real GitHub projects and shows that fine-tuning Qwen2.5-Coder-32B-Instruct on them improves performance on the resulting SWE-Flow-Bench benchmark.

  2. Effective Strategies for Asynchronous Software Engineering Agents

    cs.CL 2026-03 conditional novelty 6.0 of 10

    CAID, a manager-driven multi-agent system using git worktrees, commits, and merges, improves long-horizon SWE success by roughly 14–27 absolute points over single-agent baselines.

  3. How can we assess human-agent interactions? Case studies in software agent design

    cs.AI 2025-10 conditional novelty 6.0 of 10

    PULSE combines sparse human ratings with prediction-powered inference to cut confidence intervals by ~40% and shows LLM choice matters more than scaffolding for user satisfaction.

  4. Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A guidance-augmented RLVR framework improves software engineering agent pass@1 on SWE-Bench Verified from 9.4% to 22.4%, with guidance being the critical component.

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