Abstract
The modern day software industry has seen an increase in the number of software projects .With the increase in the size and the scale of such projects it has become necessary to perform an accurate requirement analysis early in the project development phase in order to perform a cost benefit analysis. Software cost estimation is the process of gauging the amount of effort required to build a software project. In this paper we have proposed a Particle Swarm Optimization (PSO) technique which operates on data sets which are clustered using the K-means clustering algorithm. The PSO generates the parameter values of the COCOMO model for each of the clusters of data values. As clustering encompasses similar objects under each group PSO tuning is more efficient and hence it generates better results and can be used for large data sets to give accurate results. Here we have tested the model on the COCOMO81 dataset and also compared the obtained values with standard COCOMO model. It is found that the developed model provides better estimation of the effort.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bailey, J.w., Basili, V.R.: A meta model for software development resource expenditures. In: Fifth International conference on software Engineering, pp. 107–129. IEEE, Los Alamitos (1981), CH-1627-9/81/0000/0107500.75@
Briand, L.C., El Emam, K., Bomarius, F.: COBRA: A Hybrid Method for Software Cost Estimation, Benchmarking, and Risk Assessment, International Software Engineering Research Network Technical Report ISERN-97-24 (Revision 2), PP 1-24 (1997)
Gruschke, T.: Empirical Studies of Software Cost Estimation: Training of Effort Estimation Uncertainty Assessment Skills. In: 11th IEEE International Software Metrics Symposium (METRICS 2005),1530-1435/05. IEEE, Los Alamitos (2005)
Jørgensen, M.: Evidence-Based Guidelines for Assessment of Software Development Cost Uncertainty. IEEE Transactions on Software Engineering 31(11), 942–954 (2005)
Sheta, A.F.: Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects. Journal of Computer Science 2(2), 118–123 (2006)
Auer, M., Trendowicz, A., Graser, B., Haunschmid, E., Biffl, S.: Optimal Project Feature Weights in Analogy-Based Cost Estimation: Improvement and Limitations. IEEE Transactions on Software Engineering 32(2), 83–92 (2006)
Hari, C.V.M.K., Prasad Reddy, P.V.G.D., Jagadeesh, M.: Interval Type 2 Fuzzy Logic for Software Cost Estimation Using Takagi-Sugeno Fuzzy Controller. In: Proceedings of 2010 International Conference on Advances in Communication, Network, and Computing. IEEE, Los Alamitos (2010), doi:10.1109/CNC.2010.14, 978-0-7695-4209-6/10
Jørgensen, M., Shepperd, M.: A Systematic Review of Software Development Cost Estimation Studies. IEEE Transactions on Software Engineering 33(1), 33–53 (2007)
Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization An overview. In: Swarm Intell., pp. 33–57. Springer, Heidelberg (2007), doi:10.1007/s11721-007-0002-0
Felix, T.S.C., Tiwari, M.K.: Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, pp. 1–548. I-TECH Education and Publishing (2007), ISBN 978-3-902613-09-7
Keung, J.W., Kitchenham, B.A., Jeffery, D.R.: Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation. IEEE Transactions on Software Engineering 34(4), 471–484 (2008)
Bin, W., Yi, Z., Shaohui, L., Zhonghi, S.: CSIM: A Document Clustering Algorithm Based on Swarm Intelligence, pp. 477–482. IEEE, Los Alamitos (2002), 0-7803-7282-4/02@2002
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sethi, T.S., Hari, C.V.M.K., Kaushal, B.S.S., Sharma, A. (2011). Cluster Analysis & Pso for Software Cost Estimation. In: Das, V.V., Thomas, G., Lumban Gaol, F. (eds) Information Technology and Mobile Communication. AIM 2011. Communications in Computer and Information Science, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20573-6_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-20573-6_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20572-9
Online ISBN: 978-3-642-20573-6
eBook Packages: Computer ScienceComputer Science (R0)