A procedure for modeling software usage with the finite state, discrete parameter Markov chain is described. It involves rigorous analysis of the specification before design and coding begin. Many benefits emerge from this process, including the ability to synthesize a macro level usage distribution from a micro level understanding of how the software will be used. This usage distribution becomes the basis for a statistical test of the software, which is fundamental to the Cleanroom development process. Some analytical results known for Markov chains that have meaningful implications and interpretations for the software development process are described.

In statistical testing, a model is developed to characterize the population of uses of the software, and the model is used to generate a statistically correct sample of all uses of the software. A software ‘usage model’ characterizes the population of intended uses of the software in the intended environment. Statistical testing based on a software usage model ensures that the failures that will occur most frequently in operational use will be found early in the testing cycle. The usage model is based on the software specification. The model can be developed in parallel with the software, thus shortening the elapsed time required to develop the deliver software. Usage modeling has been demonstrated to be an activity that improves the specification, gives an analytical description of the specification, quantifies the testing costs and, with statistical testing, provides a basis from which inferences of software reliability may be made. This paper describes the justification for statistical testing of software using a usage model, describes procedures for developing and using a usage model and discusses several usage modeling issues and recent advances in usage model applications.

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