DevBench: A Comprehensive Benchmark for Software Development
<|reference_start|>DevBench: A Comprehensive Benchmark for Software Development: Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities. However, existing benchmarks predominantly focused on simplified or isolated aspects of programming, such as single-file code generation or repository issue debugging, falling short of measuring the full spectrum of challenges raised by real-world programming activities. To this end, we propose DevBench, a comprehensive benchmark that evaluates LLMs across various stages of the software development lifecycle, including software design, environment setup, implementation, acceptance testing, and unit testing. DevBench features a wide range of programming languages and domains, high-quality data collection, and carefully designed and verified metrics for each task. Empirical studies show that current LLMs, including GPT-4-Turbo, fail to solve the challenges presented within DevBench. Analyses reveal that models struggle with understanding the complex structures in the repository, managing the compilation process, and grasping advanced programming concepts. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications. Our benchmark is available at https://github.com/open-compass/DevBench<|reference_end|>
@article{li2024devbench:,
    title={DevBench: A Comprehensive Benchmark for Software Development},
    author={Bowen Li, Wenhan Wu, Ziwei Tang, Lin Shi, John Yang, Jinyang Li,
  Shunyu Yao, Chen Qian, Binyuan Hui, Qicheng Zhang, Zhiyin Yu, He Du, Ping
  Yang, Dahua Lin, Chao Peng, Kai Chen},
    journal={arXiv preprint arXiv:2403.08604},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08604},
    primaryClass={cs.CL cs.SE}
}