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Develop Efficient Technique of Cost Estimation Model for Software Applications

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 87 - Number 16
Year of Publication: 2014
Lalit V. Patil
Sagar K Badjate
S. D. Joshi

Lalit V Patil, Sagar K Badjate and S D Joshi. Article: Develop Efficient Technique of Cost Estimation Model for Software Applications. International Journal of Computer Applications 87(16):18-22, February 2014. Full text available. BibTeX

	author = {Lalit V. Patil and Sagar K Badjate and S. D. Joshi},
	title = {Article: Develop Efficient Technique of Cost Estimation Model for Software Applications},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {87},
	number = {16},
	pages = {18-22},
	month = {February},
	note = {Full text available}


Software cost estimation predicts the amount of effort and development time required to build the system. Instead of just putting values into giving equation to calculate the cost and effort, we require more work on a scale and cost drivers to increase the accuracy of software cost estimation. The Software cost estimation process depends on the attributes such as peoples working in teams, programming language used and software tools used, salaries and overhead costs associated with the development team, database size used, training cost, accidental rework, a policy used in an organization, cost of shared facilities such as a library, restaurant, resources used such as light, network etc, which gives a clear idea about software cost estimation. There are so many models available categorized into algorithmic and non-algorithmic model each of their strengths and weakness. We propose a hybrid approach, which consists of Functional Link Artificial Neural Network (FLANN) and COCOMO-II with training algorithm. FLANN reduces the computational complexity in multilayer neural network. It does not have any hidden layer, and it has fast learning ability.


  • Vahid Khatibi, Dayang N. A. Jawawi "Software Cost Estimation Methods: A Review", CIS Journal 2011.
  • Albrecht. A. J. and J. E. Gaffney, "Software function, source lines of codes, and development effort prediction: a software science validation", IEEE Trans Software Eng. SE, pp. 639-648, 1983.
  • Boehm B. W. "Software Engineering Economics", Englewood Cliffs, NJ, Prentice-Hall, 1981.
  • Musilek A. "On the Sensitivity of COCOMO II Software Cost Estimation Model" IEEE 2002.
  • Jorgensen, M. "Practical guidelines for expert-judgment-based software effort estimation", IEEE Software, 22(3), 57-63. Doi: 10. 1109/MS. 73, 2005.
  • Shepperd M. "Estimating Software Project Effort Using Analogies" IEEE NOV. 1997.
  • K. Srinivasan and D. Fisher, "Machine Learning Approaches to Estimating Software Development Effort", IEEE Transactions on Software Engineering, 21 (2), 1995.
  • Iman Attarzadeh, Siew Hock Ow, "Improving Estimation Accuracy of the COCOMO II Using an Adaptive Fuzzy Logic Model" 2011 IEEE International Conference on Fuzzy Systems June 27-30, 2011, Taipei, Taiwan.
  • Xishi Huang, Luiz F. Capretz, Jing Ren, Danny Ho, "A Neuro-Fuzzy Model for Software Cost Estimation" IEEE Proceedings of the Third International Conference on Quality Software (QSIC'03).
  • Adriano L. I. Oliveira , Petronio L. Braga, Ricardo M. F. Lima, Márcio L. Cornélio "GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation" Journal of Information and Software Technology 52 (2010) 1155–1166.
  • K. Vinay Kumar, V. Ravi, Mahil Carr, N. Raj Kiran- "Software development cost estimation using wavelet neural networks" The Journal of Systems and Software 81 (2008) 1853–1867.
  • Y. H. Pao, Adaptive Pattern Recognition and Neural Networks, Reading, MA: Addison-Wesley, 1989.