Application of Markov Chain Monte Carlo Random Testing to Test Case Prioritization in Regression Testing
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概要
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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.
著者
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Dohi Tadashi
Department Of Information Engineering Graduate School Of Engineering Hiroshima University
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OKAMURA Hiroyuki
Department of Information Engineering, Graduate School of Engineering, Hiroshima University
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Okamura Hiroyuki
Department Of Dental Materials Science School Of Life Dentistry At Tokyo The Nippon Dental Universit
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Zhou Bo
Department Of Animal Genetics Breeding And Reproduction College Of Animal Science And Technology Nanjing Agricultural University
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ZHOU Bo
Department of Computer Science and Engineering, University of California Riverside
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