EffiBench: Benchmarking the Efficiency of Automatically Generated Code
<|reference_start|>EffiBench: Benchmarking the Efficiency of Automatically Generated Code: Code generation models have increasingly become integral to aiding software development. Although current research has thoroughly examined the correctness of the code produced by code generation models, a vital aspect that plays a pivotal role in green computing and sustainability efforts has often been neglected. This paper presents EffiBench, a benchmark with 1,000 efficiency-critical coding problems to assess the efficiency of code generated by code generation models. EffiBench contains a diverse set of LeetCode coding problems. Each problem is paired with an executable human-written canonical solution, which obtains the SOTA efficiency on the LeetCode solution leaderboard. With EffiBench, we empirically examine the ability of 42 large language models (35 open-source and 7 closed-source) to generate efficient code. Our evaluation results demonstrate that the efficiency of the code generated by LLMs is generally worse than the efficiency of human-written canonical solutions. For example, GPT-4 generated code has an average \textbf{3.12} times execution time that of the human-written canonical solutions. In the most extreme cases, the execution time and total memory usage of GPT-4 generated code are \textbf{13.89} and \textbf{43.92} times that of the canonical solutions. The source code of EffiBench is released on https://github.com/huangd1999/EffiBench. We also provide the LeaderBoard at https://huggingface.co/spaces/EffiBench/effibench-leaderboard.<|reference_end|>
@article{huang2024effibench:,
    title={EffiBench: Benchmarking the Efficiency of Automatically Generated Code},
    author={Dong Huang, Yuhao Qing, Weiyi Shang, Heming Cui, Jie M.Zhang},
    journal={arXiv preprint arXiv:2402.02037},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02037},
    primaryClass={cs.SE cs.CL}
}