Paper Info
Title
Improving fault prediction using Bayesian networks for the development of embedded software applications
Abstract
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 (C) 2006 John Wiley & Sons, Ltd.
Year
DOI
Venue
2006
10.1002/stvr.353
SOFTWARE TESTING VERIFICATION & RELIABILITY
Keywords
Field
DocType
Bayesian networks,software quality,fault prediction
Data mining,Computer science,Artificial intelligence,Software development process,Predictive modelling,Fault injection,Variable-order Bayesian network,Embedded software,Bayesian network,Software quality,Reliability engineering,Principal component analysis,Machine learning
Journal
Volume
Issue
ISSN
16.0
3
0960-0833
Citations 
PageRank 
References 
14
0.72
5
Authors
2
Name
Order
Citations
PageRank
Elena Pérez-miñana11296.74
Jean-Jacques Gras2261.83