Application of Markov Chain Monte Carlo Random Testing to Test Case Prioritization in Regression Testing

Bo ZHOU  Hiroyuki OKAMURA  Tadashi DOHI  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.9   pp.2219-2226
Publication Date: 2012/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.2219
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Software Reliability Engineering)
regression testing,  test case prioritization,  random testing,  Markov chain Monte Carlo,  

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This paper proposes the test case prioritization in regression testing. The large size of a test suite to be executed in regression testing often causes large amount of testing cost. It is important to reduce the size of test cases according to prioritized test sequence. In this paper, we apply the Markov chain Monte Carlo random testing (MCMC-RT) scheme, which is a promising approach to effectively generate test cases in the framework of random testing. To apply MCMC-RT to the test case prioritization, we consider the coverage-based distance and develop the algorithm of the MCMC-RT test case prioritization using the coverage-based distance. Furthermore, the MCMC-RT test case prioritization technique is consistently comparable to coverage-based adaptive random testing (ART) prioritization techniques and involves much less time cost.