Support vector machines for regression and applications to software quality prediction

Authors:
Xin Jin;Zhaodong Liu;Rongfang Bie;Guoxing Zhao;Jixin Ma
Affiliations:
College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;School of Mathematical Sciences, Beijing Normal University, Beijing, P.R. China;School of Computing and Mathematical Science, The University of Greenwich, London, U.K
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Year:
2006

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Abstract

Software metrics are the key tool in software quality management. In this paper, we propose to use support vector machines for regression applied to software metrics to predict software quality. In experiments we compare this method with other regression techniques such as Multivariate Linear Regression, Conjunctive Rule and Locally Weighted Regression. Results on benchmark dataset MIS, using mean absolute error, and correlation coefficient as regression performance measures, indicate that support vector machines regression is a promising technique for software quality prediction. In addition, our investigation of PCA based metrics extraction shows that using the first few Principal Components (PC) we can still get relatively good performance.