Software Effort Estimation Using Functional Link Neural Networks Optimized by Improved Particle Swarm Optimization

  • Tirimula Rao Benala
  • Rajib Mall
  • Satchidananda Dehuri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


This paper puts forward a new learning model based on improved particle swarm optimization (ISO) for functional link artificial neural networks (FLANN) to estimate software effort. The improved PSO uses the adaptive inertia to balance the tradeoff between exploration and exploitation of the search space while training FLANN. The Chebyshev polynomial has been used for mapping the original feature space from lower to higher dimensional functional space. The method has been evaluated exhaustively on different test suits of PROMISE repository to study the performance. The simulation results show that the ISO learning algorithm greatly improves the performance of FLANN and its variants for software development effort estimation.


Software effort estimation ISO Back propagation FLANN 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    de Araújo, R.A., Oliveira, A.L.I., Soares, S.: A Shift-Invariant Morphological System for Software Development Cost Estimation. Expert Systems with Applications 38, 4162–4168 (2011)CrossRefGoogle Scholar
  2. 2.
    Braga, P.L., Oliveira, A.L.I., Ribeiro, G.H.T., Meira, S.R.L.: Software Effort Estimation Using Machine Learning Techniques with Robust Confidence Intervals. In: Proceedings of IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 181–185 (2007)Google Scholar
  3. 3.
    Dehuri, S., Roy, R., Cho, S.-B., Ghosh, A.: An improved particle swarm optimized functional link artificial neural network (ISO-FLANN) for classification. The Journal of System and Software 85, 1333–1345 (2012)CrossRefGoogle Scholar
  4. 4.
    Foss, T., Stensrud, E., Kitchenham, B., Myrtveit, I.: A Simulation Study of the Model Evaluation Criterion MMRE. IEEE Transactions on Software Engineering 29(11), 985–995 (2003)CrossRefGoogle Scholar
  5. 5.
    Keung, J.W.: Theoretical Maximum Prediction Accuracy for Analogy-Based Software Cost Estimation. In: Proceedings of 15th Asia-Pacific Software Engineering Conference, pp. 495–502 (2008)Google Scholar
  6. 6.
    Mendes, E., Watson, I., Triggs, C., Mosley, N., Counsell, S.: A Comparative Study of Cost Estimation Models for Web Hypermedia Applications. Empirical Software Engineering 8, 163–196 (2003)CrossRefGoogle Scholar
  7. 7.
    McQueen, J.B.: Some methods of classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  8. 8.
    Oliveira, A.L.I.: Estimation of software project effort with support vector regression. Neurocomputing 69(13-15), 1749–1753 (2006)CrossRefGoogle Scholar
  9. 9.
    Stensrud, E., Foss, T., Kitchenham, B.A., Myrtveit, I.: An Empirical Validation of the Relationship Between the Magnitude of Relative Error and Project Size. In: Proceedings of the IEEE 8th Metrics Symposium, pp. 3–12 (2002)Google Scholar
  10. 10.
    Tirimula Rao, B., Sameet, B., Kiran Swathi, G., Vikram Gupta, K., Raviteja, C., Sumana, S.: A Novel Neural Network approach for Software Cost Estimation Using Functional Link Artificial Neural Networks. International Journal of Computer Science and Network Security (IJCSNS) 9(6), 126–131 (2009)Google Scholar
  11. 11.
    Tirimula Rao, B., Dehuri, S., Mall, R.: Functional Link Artificial Neural Networks for Software Cost Estimation. International Journal of Applied Evolutionary Computation (IJAEC) 3(2), 62–82 (2012)CrossRefGoogle Scholar
  12. 12.
    Tirimula Rao, B., Chinnababu, K., Mall, R., Dehuri, S.: A Particle Swarm Optimized Functional Link Artificial Neural Network (PSO-FLANN) in Software Cost Estimation. In: Satapathy, S.C., Udgata, S.K., Biswal, B.N. (eds.) Proceedings of Int. Conf. on Front. of Intell. Comput. AISC, vol. 199, pp. 59–66. Springer, Heidelberg (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Tirimula Rao Benala
    • 1
  • Rajib Mall
    • 2
  • Satchidananda Dehuri
    • 3
  1. 1.Department of Information TechnologyJawaharlal Nehru Technological University Kakinada, University College Of EngineeringVizianagaramIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia
  3. 3.Department of System EngineeringAjou UniversitySuwonSouth Korea

Personalised recommendations