Applying Rule Induction in Software Prediction

Applying Rule Induction in Software Prediction

Bhekisipho Twala (Brunel University, UK), Michelle Cartwright (Brunel University, UK) and Martin Shepperd (Brunel University, UK)
Copyright: © 2007 |Pages: 22
DOI: 10.4018/978-1-59140-941-1.ch011
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Abstract

Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of software engineering problems including software prediction, for example, cost and defect processes. An important advantage of machine learning over statistical analysis as a modelling technique lies in the fact that the interpretation of production rules is more straightforward and intelligible to human beings than, say, principal components and patterns with numbers that represent their meaning. The main focus of this chapter is upon rule induction (RI): providing some background and key issues on RI and further examining how RI has been utilised to handle uncertainties in data. Application of RI in prediction and other software engineering tasks is considered. The chapter concludes by identifying future research work when applying rule induction in software prediction. Such future research work might also help solve new problems related to rule induction and prediction.

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