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Support vector machines for regression and applications to software quality prediction

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)

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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.

Item Type: Book Section
Additional Information: [1] Print ISBN: 978-3-540-34385-1. Online ISBN: 978-3-540-34386-8. Series ISSN: 0302-9743 [2] Copyright Holder: Springer-Verlag Berlin Heidelberg
Uncontrolled Keywords: software metrics, vector machines, software quality, regression, software quality prediction
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Related URLs:
Last Modified: 14 Oct 2016 09:02
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/1026

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