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Enhancing Performance of Random Testing through Markov Chain Monte Carlo Methods
Jan. 2013 (vol. 62 no. 1)
pp. 186-192
Bo Zhou, University of California, Riverside
Hiroyuki Okamura, Hiroshima University, Higashi-Hiroshima
Tadashi Dohi, Hiroshima University, Higashi-Hiroshima
In this paper, we propose a probabilistic approach to finding failure-causing inputs based on Bayesian estimation. According to our probabilistic insights of software testing, the test case generation algorithms are developed by Markov chain Monte Carlo (MCMC) methods. Dissimilar to existing random testing schemes such as adaptive random testing, our approach can also utilize the prior knowledge on software testing. In experiments, we compare effectiveness of our MCMC-based random testing with both ordinary random testing and adaptive random testing in real program sources. These results indicate the possibility that MCMC-based random testing can drastically improve the effectiveness of software testing.
Index Terms:
Subspace constraints,Markov processes,Software testing,Correlation,Proposals,Software,Markov chain Monte Carlo,Software testing,random testing,adaptive random testing,Bayes statistics
Citation:
Bo Zhou, Hiroyuki Okamura, Tadashi Dohi, "Enhancing Performance of Random Testing through Markov Chain Monte Carlo Methods," IEEE Transactions on Computers, vol. 62, no. 1, pp. 186-192, Jan. 2013, doi:10.1109/TC.2011.208
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