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Enhancing Performance of Random Testing through Markov Chain Monte Carlo Methods

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3 Author(s)
Bo Zhou ; University of California, Riverside ; Hiroyuki Okamura ; Tadashi Dohi

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.

Published in:

IEEE Transactions on Computers  (Volume:62 ,  Issue: 1 )