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J.A. Whittaker, M.G. Thomason, "A Markov Chain Model for Statistical Software Testing," IEEE Transactions on Software Engineering, vol. 20, no. 10, pp. 812824, October, 1994.  
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@article{ 10.1109/32.328991, author = {J.A. Whittaker and M.G. Thomason}, title = {A Markov Chain Model for Statistical Software Testing}, journal ={IEEE Transactions on Software Engineering}, volume = {20}, number = {10}, issn = {00985589}, year = {1994}, pages = {812824}, doi = {http://doi.ieeecomputersociety.org/10.1109/32.328991}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Software Engineering TI  A Markov Chain Model for Statistical Software Testing IS  10 SN  00985589 SP812 EP824 EPD  812824 A1  J.A. Whittaker, A1  M.G. Thomason, PY  1994 KW  Markov processes; program testing; software quality; probability; Markov chain model; statistical software testing; statistical inference; software field quality; software usage; test input sequences; multiple probability distributions; stochastic model; software failures; testing process; sequence generating properties VL  20 JA  IEEE Transactions on Software Engineering ER   
Statistical testing of software establishes a basis for statistical inference about a software system's expected field quality. This paper describes a method for statistical testing based on a Markov chain model of software usage. The significance of the Markov chain is twofold. First, it allows test input sequences to be generated from multiple probability distributions, making it more general than many existing techniques. Analytical results associated with Markov chains facilitate informative analysis of the sequences before they are generated, indicating how the test is likely to unfold. Second, the test input sequences generated from the chain and applied to the software are themselves a stochastic model and are used to create a second Markov chain to encapsulate the history of the test, including any observed failure information. The influence of the failures is assessed through analytical computations on this chain. We also derive a stopping criterion for the testing process based on a comparison of the sequence generating properties of the two chains.
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