Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods
IEEE Transactions on Software Engineering
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Bayesian network (BN) models can be used to predict the level of fault injection in the different phases of the software development process. This paper describes the techniques used by Motorola to develop BN fault prediction models using product, usage and process information. It also includes a procedure created to validate and calibrate the BN models. Statistical techniques are applied that provide a measurement of the quality of the predictions made by the models, and directions on how the models could be improved. The validation method described in this paper considers two alternative ways to produce an improved network. The first is based mainly on modifying the ranges of the existing nodes, adding interdependencies between them, and varying the weight values associated with each of the nodes that are used as inputs to the intermediate nodes of the BN model. The second possibility uses linear regression and principal component analysis to build the intermediate and output nodes of the network. The paper closes with some encouraging results, and outlines a number of unresolved questions. Copyright © 2006 John Wiley & Sons, Ltd.A version of this paper was originally presented at UKTest 2005: The Third U.K. Workshop on Software Testing Research, held at the University of Sheffield, U.K., 5–6 September 2005. It is reproduced here in revised and extended form with the permission of the Workshop organizers