Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Wed 13 Nov 2019 17:00 - 17:10 at Hillcrest - Performance Chair(s): Tim Menzies

As data volume and complexity grow at an unprecedented rate, the performance of data analytics programs is becoming a major concern for developers. We observed that developers sometimes use alternative data analytics APIs to improve program runtime performance while preserving functional equivalence. However, little is known on the characteristics and performance attributes of alternative data analytics APIs. In this paper, we propose a novel approach to extract alternative implementations that invoke different data analytics APIs to solve the same tasks. A key appeal of our approach is that it exploits the comparative structures in Stack Overflow discussions to discover programming alternatives. We show that our approach is promising, as 86% of the extracted code pairs were validated as true alternative implementations. In over 20% of these pairs, the faster implementation was reported to achieve a 10x or more speedup over its slower alternative. We hope that our study offers a new perspective of API recommendation and motivates future research on optimizing data analytics programs.

Wed 13 Nov

Displayed time zone: Tijuana, Baja California change

16:00 - 17:50
PerformanceResearch Papers / Demonstrations at Hillcrest
Chair(s): Tim Menzies North Carolina State University
16:00
20m
Talk
Accurate Modeling of Performance Histories for Evolving Software Systems
Research Papers
Stefan Mühlbauer Bauhaus-University Weimar, Sven Apel Saarland University, Norbert Siegmund Bauhaus-University Weimar
Pre-print
16:20
20m
Talk
An Industrial Experience Report on Performance-Aware Refactoring on a Database-centric Web Application
Research Papers
Boyuan Chen York University, Zhen Ming (Jack) Jiang York University, Paul Matos Copywell Inc., Michael Lacaria Copywell Inc.
Authorizer link Pre-print
16:40
20m
Talk
An Experience Report of Generating Load Tests Using Log-recovered Workloads at Varying Granularities of User Behaviour
Research Papers
Jinfu Chen Jiangsu University, Weiyi Shang Concordia University, Canada, Ahmed E. Hassan Queen's University, Yong Wang Alibaba Group, Jiangbin Lin Alibaba Group
Pre-print
17:00
10m
Talk
How Do API Selections Affect the Runtime Performance of Data Analytics Tasks?
Research Papers
Yida Tao Shenzhen University, Shan Tang Shenzhen University, Yepang Liu Southern University of Science and Technology, Zhiwu Xu Shenzhen University, Shengchao Qin University of Teesside
17:10
10m
Talk
Demystifying Application Performance Management Libraries for Android
Research Papers
Yutian Tang The Hong Kong Polytechnic University, Xian Zhan The Hong Kong Polytechnic University, Hao Zhou The Hong Kong Polytechnic University, Xiapu Luo The Hong Kong Polytechnic University, Zhou Xu Wuhan University, Yajin Zhou Zhejiang University, Qiben Yan Michigan State University
17:20
10m
Demonstration
PeASS: A Tool for Identifying Performance Changes at Code Level
Demonstrations
David Georg Reichelt Universität Leipzig, Stefan Kühne Universität Leipzig, Wilhelm Hasselbring Kiel University
Pre-print Media Attached File Attached
17:30
20m
Talk
ReduKtor: How We Stopped Worrying About Bugs in Kotlin Compiler
Research Papers
Daniil Stepanov Saint Petersburg Polytechnic University, Marat Akhin Saint Petersburg Polytechnic University / JetBrains Research, Mikhail Belyaev Saint Petersburg Polytechnic University
Pre-print