一句话：实证研究数据科学库 API 文档中的性能关注，揭示官方与社区文档高度互补。
该研究关注数据科学应用开发中执行时间过长和内存快速耗尽的问题，并考察流行数据科学库的 API 文档如何记录性能关注点。作者对官方文档进行定量与定性分析，同时比较 Stack Overflow、GitHub 等社区文档。定量结果显示，许多数据科学 API 在性能相关语境中被文档化，但相关文档维护不频繁。定性分析发现，社区文档与官方文档在 API 覆盖范围、知识分布和性能内容细节上高度互补。实践者可据此采用更有针对性的检索策略，研究者则可结合官方与社区文档，更全面地理解数据科学开发中的性能问题。
The development of efficient data science applications is often impeded by unbearably long execution time and rapid RAM exhaustion. Since API documentation is the primary information source for troubleshooting, we investigate how performance concerns are documented in popular data science libraries. Our quantitative results reveal the prevalence of data science APIs that are documented in performance-related context and the infrequent maintenance activities on such documentation. Our qualitative analyses further reveal that crowd documentation like Stack Overflow and GitHub are highly complementary to official documentation in terms of the API coverage, the knowledge distribution, as well as the specific information conveyed through performance-related content. Data science practitioners could benefit from our findings by learning a more targeted search strategy for resolving performance issues. Researchers can be more assured of the advantages of integrating both the official and the crowd documentation to achieve a holistic view on the performance concerns in data science development.
@inproceedings{paperbot5970,
title = {Understanding performance concerns in the API documentation of data science libraries},
author = {Yida Tao and Jiefang Jiang and Yepang Liu and Zhiwu Xu and Shengchao Qin},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3416543}
}