Abstract
Parametric software effort estimation models rely on the availability of historical project databases from which estimation models are derived. In the case of large project databases with data coming from heterogeneous sources, a single mathematical model cannot properly capture the diverse nature of the projects under consideration. Clustering algorithms can be used to segment the project database, obtaining several segmented models. In this paper, a new tool is presented, Recursive Clustering Tool, which implements the EM algorithm to cluster the projects, and allows use different regression curves to fit the different segmented models. This different approaches will be compared to each other and with respect to the parametric model that is not segmented. The results allows conclude that depending on the arrangement and characteristics of the given clusters, one regression approach or another must be used,and in general, the segmented model improve the unsegmented one.
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Cuadrado-Gallego, J.J., Garre, M., Rejas, R.J., Sicilia, MÁ. (2008). Analysis of Software Functional Size Databases. In: Cuadrado-Gallego, J.J., Braungarten, R., Dumke, R.R., Abran, A. (eds) Software Process and Product Measurement. Mensura IWSM 2007 2007. Lecture Notes in Computer Science, vol 4895. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85553-8_16
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DOI: https://doi.org/10.1007/978-3-540-85553-8_16
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