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Test-Suite Reduction Using Genetic Algorithm

  • Xue-ying Ma
  • Bin-kui Sheng
  • Cheng-qing Ye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3756)

Abstract

As the software is modified and new test cases are added to the test-suite, the size of the test-suite grows and the cost of regression testing increases. In order to decrease the cost of regression testing, researchers have researched on the use of test-suite reduction techniques, which identify a subset of test cases that provides the same coverage of the software, according to some criterion, as the original test-suite. This paper investigates the use of an evolutionary approach, called genetic algorithms, for test-suite reduction. The algorithm builds the initial population based on test history, calculates the fitness value using coverage and cost information, and then selectively breeds the successive generations using genetic operations. This generational process is repeated until a minimized test-suite is founded. The results of studies show that, genetic algorithms can significantly reduce the size of the test-suite and the cost of regression testing, and achieves good cost-effectiveness.

Keywords

Test-suite reduction Regression testing Genetic algorithm Gene modeling Cost-effectiveness 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xue-ying Ma
    • 1
    • 2
  • Bin-kui Sheng
    • 2
  • Cheng-qing Ye
    • 1
  1. 1.College of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
  2. 2.Dept. of Information ManagementZhejiang University of Finance & EconomicsHangzhouP. R. China

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