PCART: Automated Repair of Python API Parameter Compatibility Issues
<|reference_start|>PCART: Automated Repair of Python API Parameter Compatibility Issues: In modern software development, Python third-party libraries have become crucial, particularly due to their widespread use in fields such as deep learning and scientific computing. However, the parameters of APIs in third-party libraries often change during evolution, causing compatibility issues for client applications that depend on specific versions. Due to Python's flexible parameter-passing mechanism, different methods of parameter passing can result in different API compatibility. Currently, no tool is capable of automatically detecting and repairing Python API parameter compatibility issues. To fill this gap, we propose PCART, the first to implement a fully automated process from API extraction, code instrumentation, and API mapping establishment, to compatibility assessment, and finally to repair and validation, for solving various types of Python API parameter compatibility issues, i.e., parameter addition, removal, renaming, reordering of parameters, as well as the conversion of positional parameters to keyword parameters. We construct a large-scale benchmark PCBENCH, including 47,478 test cases mutated from 844 parameter-changed APIs of 33 popular Python libraries, to evaluate PCART. The evaluation results show that PCART is effective yet efficient, significantly outperforming existing tools (MLCatchUp and Relancer) and the large language model ChatGPT-4, achieving an F-measure of 96.49% in detecting API parameter compatibility issues and a repair accuracy of 91.36%. The evaluation on 14 real-world Python projects from GitHub further demonstrates that PCART has good practicality. We believe PCART can help programmers reduce the time spent on maintaining Python API updates and facilitate automated Python API compatibility issue repair.<|reference_end|>
@article{zhang2024pcart:,
    title={PCART: Automated Repair of Python API Parameter Compatibility Issues},
    author={Shuai Zhang, Guanping Xiao, Jun Wang, Huashan Lei, Yepang Liu, Zheng
  Zheng},
    journal={arXiv preprint arXiv:2406.03839},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03839},
    primaryClass={cs.SE}
}