Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30917
Title: Predicting object-oriented software maintainability using multivariate adaptive regression splines
Authors: Zhou, Y
Leung, H 
Keywords: Maintainability
Multiple adaptive regression splines
Object-oriented
Prediction
Issue Date: 2007
Publisher: Elsevier
Source: Journal of systems and software, 2007, v. 80, no. 8, p. 1349-1361 How to cite?
Journal: Journal of systems and software 
Abstract: Accurate software metrics-based maintainability prediction can not only enable developers to better identify the determinants of software quality and thus help them improve design or coding, it can also provide managers with useful information to help them plan the use of valuable resources. In this paper, we employ a novel exploratory modeling technique, multiple adaptive regression splines (MARS), to build software maintainability prediction models using the metric data collected from two different object-oriented systems. The prediction accuracy of the MARS models are evaluated and compared using multivariate linear regression models, artificial neural network models, regression tree models, and support vector models. The results suggest that for one system MARS can predict maintainability more accurately than the other four typical modeling techniques, and that for the other system MARS is as accurate as the best modeling technique.
URI: http://hdl.handle.net/10397/30917
ISSN: 0164-1212
DOI: 10.1016/j.jss.2006.10.049
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