Skip to main content

Fuzzy Based PSO for Software Effort Estimation

  • Conference paper
Information Technology and Mobile Communication (AIM 2011)

Abstract

Software Effort Estimation is the most important activity in project planning for Project Management. This Effort estimation is required for estimation of resources, time to complete the project successfully. Many models have been proposed, but because of differences in the data collected, type of projects and project attributes, no model has been proven successful at effectively and consistently predicting software development effort due to the uncertainty factors. The Uncertainty in effort estimation controlled by using fuzzy logic and the parameters of the Effort estimation are tuned by the Particle Swarm Optimization with Inertia Weight. We proposed three models for software effort estimation using fuzzy logic and PSO with Inertia Weight. The valuated effort is optimized using the incumbent archetypal and tested and tried on NASA software projects on the basis of three touchstones for assessment of software cost estimation models. A comparison of the all models is done and it is found that the incumbent archetypal cater better values.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  2. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley and Sons, Chichester (2002)

    MATH  Google Scholar 

  3. 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); ISSN 1549-3636

    Article  Google Scholar 

  4. Sheta, A., Rine, D., Ayesh, A.: Development of Software Effort and Schedule Estimation Models Using Soft Computing Techniques. In: 2008 IEEE Congress on Evolutionary Computation, CEC 2008 (2008); 978-1-4244-1823-7/

    Google Scholar 

  5. Gonsalves, T., Ito, A., Kawabata, R., Itoh, K.: Swarm Intelligence in the Optimization of Software Development Project Schedule. IEEE, Los Alamitos (2008); 0730-3157/08

    Book  Google Scholar 

  6. Pahariya, J.S., Ravi, V., Carr, M.: Software Cost Estimation using Computational Intelligence Techniques. 2009 World Congress on Nature & Biologically Inspired Computing (2009)

    Google Scholar 

  7. Attarzadeh, I., Ow, S.H.: Soft Computing Approach for Software Cost Estimation. International Journal of Software Engineering (IJSE) 3(1) (January 2010)

    Google Scholar 

  8. Huang, X., Ho, D., Ren, J., Capretz, L.F.: Improving the COCOMO model using a neuro-fuzzy approach. Elsevier, Amsterdam (2005), doi:10.1016/j.asoc.2005.06.007

    Google Scholar 

  9. Sheta, A., Rine, D., Ayesh, A.: Development of Software Effort and Schedule Estimation Models Using Soft Computing Techniques. IEEE, Los Alamitos (2008) 978-1-4244-1823-7/08

    Book  Google Scholar 

  10. 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@ 1981

    Google Scholar 

  11. Ying, H.: The Takagi-Sugeno fuzzy controllers using the simplified linear control rules are nonlinear variable gain controllers. Automatica 34(2), 157–167 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  12. Pang, W., Wang, K.-P., Zhou, C.-G., Dong, L.-J.: Fuzzy Discrete Particle Swarm Optimization for Solving Traveling Salesman Problem. In: Proceedings of the Fourth International Conference on Computer and Information Technology (CIT 2004) (2004) 0-7695-2216-5/04

    Google Scholar 

  13. 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

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Prasad Reddy, P.V.G.D., Hari, C.V.M.K. (2011). Fuzzy Based PSO for Software Effort 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_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20573-6_36

  • 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)

Publish with us

Policies and ethics