Abstract
Software performance is important for ensuring the quality of software products. Performance bugs, defined as programming errors that cause significant performance degradation, can lead to slow systems and poor user experience. While there has been some research on automated performance testing such as test case generation, the main idea is to select workload values to increase the program execution times. These techniques often assume the initial test cases have the right combination of input parameters and focus on evolving values of certain input parameters. However, such an assumption may not hold for highly configurable real-word applications, in which the combinations of input parameters can be very large. In this paper, we manually analyze 300 bug reports from three large open source projects - Apache HTTP Server, MySQL, and Mozilla Firefox. We found that 1) exposing performance bugs often requires combinations of multiple input parameters, and 2) certain input parameters are frequently involved in exposing performance bugs. Guided by these findings, we designed and evaluated an automated approach, PerfLearner, to extract execution commands and input parameters from descriptions of performance bug reports and use them to generate test frames for guiding actual performance test case generation.
Original language | English |
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Title of host publication | ASE 2018 - Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering |
Editors | Christian Kastner, Marianne Huchard, Gordon Fraser |
Pages | 17-28 |
Number of pages | 12 |
ISBN (Electronic) | 9781450359375 |
DOIs | |
State | Published - Sep 3 2018 |
Event | 33rd IEEE/ACM International Conference on Automated Software Engineering, ASE 2018 - Montpellier, France Duration: Sep 3 2018 → Sep 7 2018 |
Publication series
Name | ASE 2018 - Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering |
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Conference
Conference | 33rd IEEE/ACM International Conference on Automated Software Engineering, ASE 2018 |
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Country/Territory | France |
City | Montpellier |
Period | 9/3/18 → 9/7/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computing Machinery.
Keywords
- Performance bugs
- Software mining
- Software testing
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Human-Computer Interaction
- Software