DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale

• URL: http://arxiv.org/abs/2501.13699v1
• Date: Thu, 23 Jan 2025 14:27:11 GMT
• Title: DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale
• Authors: Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang,
• Abstract summary: DI-BENCH is a large-scale benchmark and evaluation framework designed to assess Large Language Models' capability on dependency inference.<n>The benchmark features 581 repositories with testing environments across Python, C#, Rust, and JavaScript.<n>Extensive experiments with textual and execution-based metrics reveal that the current best-performing model achieves only a 42.9% execution pass rate.
• Score: 39.92722886613929
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models have advanced automated software development, however, it remains a challenge to correctly infer dependencies, namely, identifying the internal components and external packages required for a repository to successfully run. Existing studies highlight that dependency-related issues cause over 40\% of observed runtime errors on the generated repository. To address this, we introduce DI-BENCH, a large-scale benchmark and evaluation framework specifically designed to assess LLMs' capability on dependency inference. The benchmark features 581 repositories with testing environments across Python, C#, Rust, and JavaScript. Extensive experiments with textual and execution-based metrics reveal that the current best-performing model achieves only a 42.9% execution pass rate, indicating significant room for improvement. DI-BENCH establishes a new viewpoint for evaluating LLM performance on repositories, paving the way for more robust end-to-end software synthesis.

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