Understanding Performance Concerns in the API Documentation of Data Science Libraries

Tao，Yida1,3; Jiang，Jiefang1,3; Liu，Yepang2,3; Xu，Zhiwu1,3; Qin，Shengchao1,3

10.1145/3324884.3416543

2020-09-01

1938-4300

978-1-7281-7281-1

Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering, ASE 2020

895-906

21-25 Sept. 2020

Melbourne, VIC, Australia

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.

API documentation data science empirical study performance

其他

英语

EI ; CPCI-S

WOS:000651313500075

20210309773487

Application programming interfaces (API) ; Libraries ; Software engineering

Computer Software, Data Handling and Applications:723 ; Computer Programming:723.1 ; Libraries:903.4.1

2-s2.0-85099237019

Scopus

1.College of Computer Science and Software,Engineering Shenzhen University,China
2.Southern University of Science and Technology,Department of Computer Science and Engineering,China
3.School of Computing,Engr. Digital Technologies,Teesside University,United Kingdom

Tao，Yida,Jiang，Jiefang,Liu，Yepang,et al. Understanding Performance Concerns in the API Documentation of Data Science Libraries[C],2020:895-906.