Support vector machines for regression and applications to software quality prediction
Jin, Xin, Liu, Zhaodong, Bie, Rongfang, Zhao, Guoxing and Ma, Jixin (2006) Support vector machines for regression and applications to software quality prediction. In: Alexandrov, Vassil N., van Albada, Geert Dick, Sloot, Peter M.A. and Dongarra, Jack, (eds.) Computational Science – ICCS 2006. 6th International Conference, Reading, UK, May 28-31, 2006, Proceedings, Part IV. Lecture Notes in Computer Science (3994). Springer Berlin Heidelberg, Berlin / Heidelberg, Germany, pp. 781-788. ISBN 978-3-540-34385-1 ISSN 0302-9743 (Print), 1611-3349 (Online) (doi:10.1007/11758549_105)Full text not available from this repository.
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.
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