Predicting software defects in varying development lifecycles using Bayesian nets

Authors:
Norman Fenton;Martin Neil;William Marsh;Peter Hearty;David Marquez;Paul Krause;Rajat Mishra
Affiliations:
Department of Computer Science Queen Mary, University of London, Mile End Road, London, UK and Agena Ltd., London, UK;Department of Computer Science Queen Mary, University of London, Mile End Road, London, UK and Agena Ltd., London, UK;Department of Computer Science Queen Mary, University of London, Mile End Road, London, UK;Department of Computer Science Queen Mary, University of London, Mile End Road, London, UK;Department of Computer Science Queen Mary, University of London, Mile End Road, London, UK;Department of Computing, University of Surrey, Guildford, Surrey, UK and Philips Software Centre, Bangalore, India;Philips Software Centre, Bangalore, India
Venue:
Information and Software Technology
Year:
2007

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Abstract

An important decision in software projects is when to stop testing. Decision support tools for this have been built using causal models represented by Bayesian Networks (BNs), incorporating empirical data and expert judgement. Previously, this required a custom BN for each development lifecycle. We describe a more general approach that allows causal models to be applied to any lifecycle. The approach evolved through collaborative projects and captures significant commercial input. For projects within the range of the models, defect predictions are very accurate. This approach enables decision-makers to reason in a way that is not possible with regression-based models.