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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cuadrado, J.J., Sicilia, M.A., Garre, M., Rodrguez, D.: An empirical study of process-related attributes in segmented software cost-estimation relationships. Journal of Systems and Software 79(3), 351–361 (2006)
Garre, M., Cuadrado, J.J., Sicilia, M.A.: Recursive segmentation of software projects for the estimation of development effort. In: Proceedings of the ADIS 2004 Workshop on Decision Support in Software Engineering, CEUR Workshop proceedings, vol. 120 (2004)
Garre, M., Cuadrado, J.J., Sicilia, M.A., Charro, M., Rodrguez, D.: Segmented Parametric Software Estimation Models: Using the EM algorithm with the ISBSG 8 database. In: Information Technology Interfaces (2005)
McLachlan, G., Krishnan, T.: The EM Algorithm and Extensions. Wiley series in probability and statistics. John Wiley & Sons, Chichester (1997)
McLachlan, G., Peel, D.: Finite Mixture Model. Wiley, New York (2000)
Boehm, B., Abts, C., Chulani, S.: Software Development Cost Estimation approaches – a survey. USC Center for Software Engineering Technical Report # USC-CSE-2000-505
Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)
Stensrud, E., Foss, T., Kitchenham, B., Myrtveit, I.: An Empirical Validation of the Relationship Between the Magnitude of Relative Error and Project Size. In: Proceedings of the Eighth IEEE Symposium on Software Metrics (2002)
Oligny, S., Bourque, P., Abran, A., Fournier, B.: Exploring the relation between effort and duration in software engineering projects. In: Proceedings of the World Computer Congress, pp. 175–178 (2000)
Parametric Estimating Initiative. Parametric Estimating Handbook, 2nd edn. (1999)
Marquardt, D.W.: An algorithm for least squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics 11, 431–441 (1963)
McLachlan, G., Basford, K.: Mixture Models: Inference and Applications to Clustering. Marcel Dekker, New York (1988)
Rissanen, J.: A Universal Prior for Integers and Estimation by Minimum Description Length. Annals of Statistics 2(11), 417–431 (1983)
NESMA, NESMA FPA Counting Practices Manual, CPM 2.0 (1996)
Dreger, J.B.: Function Point Analysis. Prentice Hall, Englewood Cliffs (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garre, M., Sicilia, M.A., Cuadrado, J.J., Charro, M. (2006). Regression Analisys of Segmented Parametric Software Cost Estimation Models Using Recursive Clustering Tool. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_102
Download citation
DOI: https://doi.org/10.1007/11875581_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)