Call for Paper - July 2019 Edition
IJCA solicits original research papers for the July 2019 Edition. Last date of manuscript submission is June 20, 2019. Read More

SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011
Hari .CH.V.M.K
Tegjyot Singh Sethi
Kaushal .B.S.S

Hari .CH.V.M.K, Tegjyot Singh Sethi and Jagadeesh.M. Article:SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques. International Journal of Computer Applications 31(4):12-19, October 2011. Full text available. BibTeX

	author = {Hari .CH.V.M.K and Tegjyot Singh Sethi and Jagadeesh.M},
	title = {Article:SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {31},
	number = {4},
	pages = {12-19},
	month = {October},
	note = {Full text available}


Software Effort estimation is the process of gauging the amount of effort required to complete the project. With the proliferation of software projects and the heterogeneity in there genre, there is a need for efficient software effort estimation techniques to enable the project managers to perform proper planning of the Software Life Cycle activates. In this article, a new hybrid toolbox based on soft computing techniques for effort estimation is introduced. Particle swarm optimization and cluster analysis has been implemented to perform efficient estimation of effort values with learning ability. The main aim of the toolbox is to provide an efficient, flexible and user friendly way of performing the effort estimation task, by catering to the needs of both the technical and the nontechnical users. The toolbox also implements the COCOMO model to enable a comparative analysis of the proposed model. It was observed that the model when provided with enough training data gave better results when compared with the standard COCOMO values.


  • Bailey, J.W., Basili, R.: A Meta model for software development resource expenditures. In. Fifth International conference on software Engineering, CH-1627-9/81/0000/0107500.75@ 1981 IEEE, PP 107-129(1981).
  • Briand, L.C., Emam , K.E., 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),doi: 1530-1435/05 © IEEE(2005).
  • Sheta,A.F.: Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects. Journal of Computer Science 2 (2): PP: 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, VOL. 32, NO. 2, PP: 83-92 FEBRUARY (2006).
  • Hari, CH.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.DOI 10.1109/CNC.2010.14, 978-0-7695-4209-6/10 © IEEE (2010).
  • Jørgensen, M., Shepperd, M.: A Systematic Review of Software Development Cost Estimation Studies. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 33, NO. 1, PP: 33-53, JANUARY (2007).
  • Poli, R., Kennedy, J., Blackwell, and T.: Particle swarm optimization an overview. Swarm Intell,PP: 33–57, Springer, DOI 10.1007/s11721-007-0002-0 (2007).
  • Felix, Chan, T.S., Tiwari, M.K.: Swarm Intelligence: Focus on Ant and Particle Swarm Optimization. I-TECH Education and Publishing, ISBN 978-3-902613-09-7, PP: 1- 548, (2007).
  • Bin, W., Yi, Z., Shaohui, L., and Zhonghi.S: CSIM: A Document Clustering Algorithm Based on Swarm Intelligence. 0-7803-7282-4/02@2002 IEEE PP: 477-482,(2002).
  • Huang, X., Ho, D., Ren, J., Capretz, and L.F.: Improving the COCOMO model using a neuro-fuzzy approach. applied soft computing 7,pp-29-40,2007.
  • Zamani, M., Netaji, H., et. al.: Toolbox for Interval Type-2 Fuzzy Logic Systems.In: Proceedings of the 11th joint Conference on Information Sciences, atlantis press(2008).
  • Sethi, T.S., Hari, CH.V.M.K. Kaushal, B.S.S., Sharma,A.:Cluster Analysis & Pso for Software Cost Estimation.In: Das, V.V., Thomas, G., Gaol, F.L.(eds.) AIM 2011.CCIS,147, pp.281-286,Springer-Verlag Berlin, Heidelberg(2011).
  • Sheta A., Rine D. and Ayesh A.: Development of Software Effort and Schedule Estimation Models Using Soft Computing Techniques, IEEE Congress on Evolutionary Computation (CEC 2008).978-1-4244-1823-7/(2008).
  • Tadayon N. : “Neural Network Approach for Software Cost Estimation”, Proceedings of the International Conference on Information Technology: Coding and Computing(ITCC’05),IEEE,2005.