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Software Project and Quality Modelling Using Bayesian Networks

Software Project and Quality Modelling Using Bayesian Networks

Norman Fenton (University of London, UK), Peter Hearty (University of London, UK), Martin Neil (University of London, UK), and Lukasz Radlinski (University of London, UK and University of Szczecin, Poland)
Copyright: © 2010 |Pages: 25
DOI: 10.4018/978-1-60566-758-4.ch001
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Abstract

This chapter provides an introduction to the use of Bayesian Network (BN) models in Software Engineering. A short overview of the theory of BNs is included, together with an explanation of why BNs are ideally suited to dealing with the characteristics and shortcomings of typical software development environments. This theory is supplemented and illustrated using real world models that illustrate the advantages of BNs in dealing with uncertainty, causal reasoning and learning in the presence of limited data.
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Background

Before we can describe BN software project models, it is worthwhile examining the problems that such models are trying to address and why it is that traditional approaches have proved so difficult. Then, by introducing the basics of BN theory, we will see how BN models address these shortcomings.

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