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ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Thu 24 Sep 2020 02:20 - 02:40 at Koala - Empirical Software Engineering (2) Chair(s): Julia Rubin

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 Sep
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02:20 - 03:20: Empirical Software Engineering (2)Research Papers at Koala
Chair(s): Julia RubinUniversity of British Columbia, Canada
02:20 - 02:40
Understanding Performance Concerns in the API Documentation of Data Science Libraries
Research Papers
Yida TaoShenzhen University, Jiefang JiangShenzhen University, Yepang LiuSouthern University of Science and Technology, Zhiwu XuShenzhen University, Shengchao QinUniversity of Teesside
02:40 - 03:00
On the Effectiveness of Unified Debugging: An Extensive Study on 16 Program Repair Systems
Research Papers
Samuel BentonThe University of Texas at Dallas, Xia LiKennesaw State University, Yiling LouPeking University, China, Lingming ZhangUniversity of Illinois at Urbana-Champaign, USA
03:00 - 03:20
Automated Third-party Library Detection for Android Applications: Are We There Yet?Experience
Research Papers
Zhan XianThe Hong Kong Polytechnic University, Lingling FanNanyang Technological University, Singapore, Tianming LiuMonash University, Australia, Sen ChenNanyang Technological University, Singapore, Li LiMonash University, Australia, Haoyu WangBeijing University of Posts and Telecommunications, China, Yifei XuSouthern University of Science and Technology, Xiapu LuoThe Hong Kong Polytechnic University, Yang LiuNanyang Technological University, Singapore