Abstract
Software project managers commonly use various metrics to assist in the design, maintaining and implementation of large software systems. The ability to predict the quality of a software object can be viewed as a classification problem, where software metrics are the features and expert quality rankings the class labels. In this paper we propose a Gaussian Mixture Model (GMM) based method for software quality classification and use Locality Preserving Projection (LPP) to improve the classification performance. GMM is a generative model which defines the overall data set as a combination of several different Gaussian distributions. LPP is a dimensionality deduction algorithm which can preserve the distance between samples while projecting data to lower dimension. Empirical results on benchmark dataset show that the two methods are effective.
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Jin, X., Liu, Y., Ren, J., Xu, A., Bie, R. (2006). Locality Preserving Projection on Source Code Metrics for Improved Software Maintainability. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_92
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DOI: https://doi.org/10.1007/11941439_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
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