A comparison of techniques for developing predictive models of software metrics

Gray, A
MacDonell, SG
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Journal Article
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The use of regression analysis to derive predictive equations for software metrics has recently been complemented by increasing numbers of studies using non-traditional methods, such as neural networks, fuzzy logic models, case-based reasoning systems, and regression trees. There has also been an increasing level of sophistication in the regression-based techniques used, including robust regression methods, factor analysis, and more effective validation procedures. This paper examines the implications of using these methods and provides some recommendations as to when they may be appropriate. A comparison of the various techniques is also made in terms of their modelling capabilities with specific reference to software metrics.

Metrics , Analysis techniques , Predictive models , Multilayer FeedForward Networks , Neural Networks , Fuzzy-Systems , Universal Approximators , Local Minima , Regression , Examples , Squares , Validation , Management
Information and Software Technology, vol.39(6), pp.425 - 437.
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Copyright © 1998 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version)