Bayesian-network Con rmation of Software Testing Uncertainties

In this paper, we claim that software development will do well by explicit modeling of its uncertainties using existing uncertainty modeling techniques. This is accomplished initially by stating the Maxim of Uncertainty in Software Engineering (MUSE), followed by a detailed presentation of uncertainty in software testing. We then propose that a speci c technique, known as Bayesian Belief Networks, be used to model software testing uncertainties. We demonstrate the use of Bayesian networks to con rm beliefs in the validity of software artifacts and relations in an elevator control system. We describe a prototype implementation that allows for such \software belief networks" to be de ned and updated. We conclude with a discussion of issues, concerns, and future prospects for modeling software uncertainties.