Understanding Performance Concerns in the API Documentation of Data Science Libraries
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 suggest 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 found in performance-related discussions. 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.
Thu 24 SepDisplayed time zone: (UTC) Coordinated Universal Time change
02:20 - 03:20
|Understanding Performance Concerns in the API Documentation of Data Science Libraries|
|On the Effectiveness of Unified Debugging: An Extensive Study on 16 Program Repair Systems|
|Automated Third-party Library Detection for Android Applications: Are We There Yet?Experience|
Xian Zhan The Hong Kong Polytechnic University, Lingling Fan Nanyang Technological University, Singapore, Tianming Liu Monash University, Australia, Sen Chen Nanyang Technological University, Singapore, Li Li Monash University, Australia, Haoyu Wang Beijing University of Posts and Telecommunications, China, Yifei Xu Southern University of Science and Technology, Xiapu Luo The Hong Kong Polytechnic University, Yang Liu Nanyang Technological University, Singapore