Predicting dependability by testing

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Abstract
In assessing the quality of software, we would like to make engineering judgements similar to those based on statistical quality control. Ideally, we want to support statements like: "The confidence that this program's result at X is correct is p," where X is a particular vector of inputs, and confidence p is obtained from measurements of the software (perhaps involving X). For the theory to be useful, it must be feasible to predict values of p near 1 for many programs, for most values of X.Blum's theory of self-checking/correcting programs has exactly the right character, but it applies to only a few unusual problems. Conventional software reliability theory is widely applicable, but it yields only confidence in a failure intensity, and the measurements required to support a correctness-like failure intensity (say 10-9/demand) are infeasible. Voas's sensitivity theory remedies these problems of reliability theory, but his model is too simple to be very plausible. In this paper we combine these ideas: reliability, sensitivity, and self-checking, to obtain new results on "dependability," plausible predictions of software quality.

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