The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - Jan. (2013 vol.62)
pp: 186-192
Bo Zhou , University of California, Riverside
Hiroyuki Okamura , Hiroshima University, Higashi-Hiroshima
Tadashi Dohi , Hiroshima University, Higashi-Hiroshima
ABSTRACT
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
REFERENCES
[1] V.D. Agrawal, “When to Use Random Testing,” IEEE Trans. Computers, vol. 27, no. 11, pp. 1054-1055, Nov. 1978.
[2] G.J. Myers, The Art of Software Testing. John Wiley & Sons, 1979.
[3] T.Y. Chen, H. Leung, and I.K. Mak, “Adaptive Random Testing,” Proc. Ninth Asian Computing Science Conf., pp. 320-329, 2004.
[4] T.Y. Chen, D.H. Huang, T.H. Tse, and Z. Yang, “An Innovative Approach to Tackling the Boundary Effect in Adaptive Random Testing,” Proc. Hawaii Int'l Conf. System Sciences, p. 262a, 2007.
[5] T.Y. Chen, F.C. Kuo, R.G. Merkel, and S.P. Ng, “Mirror Adaptive Random Testing,” Information and Software Technology, vol. 46, no. 15, pp. 1001-1010, 2004.
[6] K.P. Chan, T.Y. Chen, and D. Towey, “Forgetting Test Cases,” Proc. 30th Ann. Int'l Computer Software and Applications Conf., pp. 485-494, 2006.
[7] B. Zhou, H. Okamura, and T. Dohi, “Markov Chain Monte Carlo Random Testing,” Advances in Computer Science and Information Technology, pp. 447-456, Springer-Verlag, 2010.
[8] T.Y. Chen, T.H. Tse, and Y.T. Yu, “Proportional Sampling Strategy: A Compendium and Some Insights,” J. Systems and Software, vol. 58, no. 1, pp. 65-81, 2001.
[9] F.T. Chan, T.Y. Chen, I.K. Mak, and Y.T. Yu, “Proportional Sampling Strategy: Guidelines for Software Testing Practitioners,” Information and Software Technology, vol. 38, no. 12, pp. 775-782, 1996.
[10] K.P. Chan, T.Y. Chen, and D. Towey, “Normalized Restricted Random Testing,” Proc. Eighth Ada-Europe Int'l Conf. Reliable Software Technologies, pp. 368-381, 2003.
[11] S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, Nov. 1984.
[12] T.Y. Chen, F.C. Kuo, and H. Liu, “Application of a Failure Driven Test Profile in Random Testing,” IEEE Trans. Reliability, vol. 58, no. 1, pp. 179-192, Mar. 2009.
[13] S. Chib and E. Greenberg, “Understanding the Metropolis-Hastings Algorithm,” Am. Statistician, vol. 49, no. 4, pp. 327-335, 1995.
[14] S.M. Ross, Introduction to Probability Models, ninth ed. Academic Press, 2006.
[15] I. Ciupa, A. Leitner, M. Oriol, and B. Meyer, “ARTOO: Adaptive Random Testing for Object-Oriented Software,” Proc. 30th Int'l Conf. Software Eng., pp. 71-80, 2008.
[16] T.Y. Chen and Y.T. Yu, “On the Expected Number of Failures Detected by Subdomain Testing and Random Testing,” IEEE Trans. Software Eng., vol. 22, no. 2, pp. 109-119, Feb. 1996.
[17] GSL, “GNU Scientific Library,” http://www.gnu.org/softwaregsl, 2011.
[18] K.N. King and J. Offutt, “A Fortran Language System for Mutation-Based Software Testing,” Software: Practice and Experience, vol. 21, no. 7, pp. 686-718, 1991.
[19] T.Y. Chen and R. Merkel, “An Upper Bound on Software Testing Effectiveness,” ACM Trans. Software Eng. and Methodology, vol. 17, pp. 16:1-16:27, 2008.
30 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool