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....

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Published in:2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) pp. 895 - 906
Main Authors: Tao, Yida, Jiang, Jiefang, Liu, Yepang, Xu, Zhiwu, Qin, Shengchao
Format: Conference Proceeding
Language:English
Published: ACM 01.09.2020
Subjects:
API documentation
Data science
Documentation
empirical study
Libraries
Maintenance engineering
Search problems
Software development management
Software engineering
ISSN:2643-1572
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Abstract 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.
AbstractList 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.
Author Qin, Shengchao
Xu, Zhiwu
Liu, Yepang
Jiang, Jiefang
Tao, Yida
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  email: s.qin@tees.ac.uk
  organization: School of Computing, Engr. & Digital Technologies, Teesside University, UK College of Computer Sci. & Software Engr., Shenzhen University,China
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Snippet The development of efficient data science applications is often impeded by unbearably long execution time and rapid RAM exhaustion. Since API documentation is...
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SubjectTerms API documentation
Data science
Documentation
empirical study
Libraries
Maintenance engineering
Search problems
Software development management
Software engineering
Title Understanding Performance Concerns in the API Documentation of Data Science Libraries
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