Cluster Analysis & Pso for Software Cost Estimation

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 147)


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.


Particle Swarm Optimization (PSO) K-Means Software Cost Estimation Constructive Cost Model (COCOMO) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Dept. of CSEGitam UniversityVisakhapatnamIndia
  2. 2.Dept. of ITGitam UniversityVisakhapatnamIndia

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