Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2018

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.

Keywords

Performance bugs, Software mining, Software testing

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Data Science and Engineering

Publication

ASE 2018: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, Montpellier, France, September 3-7

First Page

17

Last Page

28

ISBN

9781450359375

Identifier

10.1145/3238147.3238204

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3238147.3238204

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