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Improving fault prediction using Bayesian networks for the development of embedded software applications
Author(s) -
PérezMiñana Elena,
Gras JeanJacques
Publication year - 2006
Publication title -
software testing, verification and reliability
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 49
eISSN - 1099-1689
pISSN - 0960-0833
DOI - 10.1002/stvr.353
Subject(s) - computer science , process (computing) , bayesian network , data mining , fault (geology) , software , principal component analysis , reliability engineering , bayesian probability , software quality , component (thermodynamics) , interdependence , machine learning , software development , artificial intelligence , engineering , seismology , law , political science , programming language , geology , operating system , physics , thermodynamics
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.