A MARKOV-CHAIN MODEL FOR STATISTICAL SOFTWARE TESTING

Statistical testing of software establishes a basis for statistical in ference about a software system's expected field quality. This paper d escribes a method for statistical testing based on a Markov chain mode l of software usage. The significance of the Markov chain is twofold. First, it allows test input sequences to be generated from multiple pr obability distributions, making it more general than many existing tec hniques. Analytical results associated with Markov chains facilitate i nformative analysis of the sequences before they are generated, indica ting how the test is likely to unfold. Second, the test input sequence s generated from the chain and applied to the software are themselves a stochastic model and are used to create a second Markov chain to enc apsulate the history of the test, including any observed failure infor mation. The influence of the failures is assessed through analytical c omputations on this chain. We also derive a stopping criterion for the testing process based on a comparison of the sequence generating prop erties of the two chains.