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.
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., Rodríguez, 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., Rodríguez, D.: Segmented Parametric Software Estimation Models: Using the EM algorithm with the ISBSG 8 database. Information Technology Interfaces, Croacia ( junio 20-23, 2005)
Garre, M., Sicilia, M.A., Cuadrado, J.J., Charro, M.: Regression analysis of segmented parametric software cost estimation models using recursive clustering tool. In: Corchado, E.S., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 302–9743. Springer, Heidelberg (2006)
McLachlan, G., Krishnan, T.: The EM Algorithm and Extensions. Wiley series in probability and statistics. John Wiley & Sons (1997)
McLachlan, G., Peel, D.: Finite Mixture Model. Wiley, New York (2000)
Boehm, B., Abts, C., Sunita Chulani.: 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)
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 ed. (1999)
NESMA, NESMA FPA Counting Practices Manual (CPM 2.0) (1996)
Dreger, J.B.: Function Point Analysis. Prentice Hall, Englewood Cliffs (1989)
Boehm, B.: Software Engineering Economics, vol. 10, Prentice-Hall (1981)
De Marco, T.: Controlling Software Projects. Yourdan Press (1982)
Conte, S.D., Dunsmore, H.E., Shen, V.Y.: Software Engineering Metrics and Models. Benjamin/Cumming Co., Inc., Menlo Park (1986)
Fenton, N.E.: Software metrics: a rigorous approach. Chapman & Hall, Londres (1991)
Fairley, R.E.: Recent advances in software estimation techniques. In: International Conference on Software Engineering, ACM, New York (1992)
Walkerden, F., Jeffery, D.: Software cost estimation: A review of models, process, and practice. Advances in Computers 44, 59–125 (1997)
Wieczorek, I., Briand, L.: Resource estimation in software engineering, Technical Report, International Software Engineering Research Network (2001)
Idri, A., Abran, A.: Fuzzy Case-Based Reasoning Models for Software Cost Estimation (2002)
Idri, A., Abran, A., Khoshgoftaar, T.M.: Fuzzy Analogy: A new Approach for Software Cost Estimation. In: Proceedings of the 11th International Workshop on Software Measurements, Montreal, Canada, pp. 93–101 (2001)
Shepperd, M., Schofield, C.: Estimating software project effort using analogies. IEEE Transactions on Software Engineering (1997)
Dolado, J.J.: On the problem of the software cost function. Information and Software Technology 43, 61–72 (2001)
Dolado, J.J., Fernández, L.: Genetic Programming, Neural Networks and Linear Regression in Software Project Estimation, INSPIRE III, Process Improvement thorough Training and Education, pp. 155–171. The British Computer Society (1998)
Mair, C., Kadoda, G., Lefley, M., Keith, P., Schofield, C., Shepperd, M., Webster, S.: An investigation of machine learning based prediction systems. The Journal of Systems and Software 53, 23–29 (2000)
Briand, L., Langley, T., Wieczorek, I.: Using the European Space Agency Data Set: A Replicated Assessment and Comparison of Common Software Cost Modeling. In: Proceedings of the 22th International Conference on Software Engineering, Limerick, Ireland, pp. 377–386 (2000)
Briand, L.C., El Emam, K., Maxwell, K., Surmann, D., Wieczorek, I.: An Assessment and Comparison of Common Cost Software Project Estimation Methods. In: Proc. International Conference on Software Engineering, ICSE 1999, pp. 313–322 (1999)
Lee, A., Cheng, C.H., Balakrishann, J.: Software development cost estimation: Integrating neural network with cluster analysis. Information & Management 34, 1–9 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-85553-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85552-1
Online ISBN: 978-3-540-85553-8
eBook Packages: Computer ScienceComputer Science (R0)