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A Collective Study of PCA and Neural Network based on COCOMO for Software Cost Estimation

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 74 - Number 16
Year of Publication: 2013
Rina M. Waghmode
L. V. Patil
S. D. Joshi

Rina M Waghmode, L V Patil and S D Joshi. Article: A Collective Study of PCA and Neural Network based on COCOMO for Software Cost Estimation. International Journal of Computer Applications 74(16):25-30, July 2013. Full text available. BibTeX

	author = {Rina M. Waghmode and L. V. Patil and S. D. Joshi},
	title = {Article: A Collective Study of PCA and Neural Network based on COCOMO for Software Cost Estimation},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {74},
	number = {16},
	pages = {25-30},
	month = {July},
	note = {Full text available}


Estimating cost is a very wearisome activity in all aspect. A person with broad scope and good thinking for the future makes more precise decisions. It helps in governing and planning the software risks which are admirably correct and precise. In 1960 regression analysis and mathematical formulae were practiced to determine cost. We need to think more than simply putting numbers into a formula and accept the results to attaining the accuracy of software cost estimation. The changing methods of estimating software cost have made the researchers to think diversely. Barry Bohem birthed COCOMO model for software cost estimation in 1981 which is considered to be more efficient as compared to previous models. Thereafter number of researchers has been trying to improve the efficiency by keeping the base of COCOMO model. The paper drafts a novel variable reduction technique called feed-forward neural network with PCA to measure the estimation model accuracy. This is based on a COCOMO sample data set which collects and maintains a large software project data repository. PCA is a kind of classification method which can reduces number of factors into a few absolute factors.


  • Anupama Kaushik, Ashish Chauhan, Deepak Mittal, Sachin Gupta. 2012. COCOMO Estimates Using Neural Networks I. J. Intelligent Systems and Applications, 2012, 9, 22-28 Published Online August 2012 in MECS
  • Bo Cheng, Xuejun Yu. 2012. The Selection of Agile Development's Effort Estimation Factors based on Principal Component Analysis. International Conference on Information and Computer Applications (ICICA 2012), IPCSIT vol. 24 IACSIT Press, Singapore 2012.
  • Vahid Khatibi, Dayang N. A. Jawawi. 2011. Software Cost Estimation Methods: A Review, Vol. 2 No. 1 Journal of Emerging Trends in Computing and Information Science. CIS Journal.
  • Attarzadeh I. Siew Hock Ow. 2010. Improving the accuracy of software cost estimation model based on a new fuzzy logic model, World Applied science journal 8(2) 2010.
  • Attarzadeh,I. Siew Hock Ow. 2010. Proposing a New Software Cost Estimation Model Based on Artificial Neural Networks, IEEE International Conference on Computer Engineering and Technology (ICCET), Volume: 3, Page(s): V3-487 - V3-491 2010.
  • Khaled Hamdan 2010. Practical Software Project Total Cost Estimation Methods. IEEE 2010
  • Sikka, G. , A. Kaur, et al. 2010. Estimating function points: Using machine learning and regression models. Education Technology and Computer (ICETC), 2nd International Conference on, 2010.
  • Justin Wong Danny Ho Luiz Fernando Capretz 2009. An Investigation of Using Neuro-Fuzzy with Software Size Estimation. IEEE ICSE'09 Workshop May 16, 2009, Vancouver, Canada
  • Chiu, N. H. , Huang, S. J. 2007. The adjusted analogy-based software effort estimation based on similarity distances, Journal of Systems and Software 80 (4), 628–640. 2007
  • Y. F. Li, M. Xie, T. N. Goh. 2007. A Study of Genetic Algorithm for Project Selection for Analogy Based Software Cost Estimation. IEEE 2007.
  • Marcio R. , Silvia Regina. 2006. Software Effort Estimation Based on Use Cases. IEEE 2006.
  • Jørgensen, M. 2005. Practical guidelines for expert-judgment-based software effort estimation, IEEE Software, 22(3), 57-63. doi:10. 1109/MS. 73, 2005.
  • Xishi Huang, Luiz F. Capretz, Jing Ren 2003. A Neuro-Fuzzy Model for Software Cost Estimation. IEEE 2003.
  • Musilek A. 2002. On the Sensitivity of COCOMO II Software Cost Estimation Model, IEEE 2002.
  • Shepperd M. 1997. Estimating Software Project Effort Using Analogies IEEE NOV. 1997.
  • K. Srinivasan and D. Fisher. 1995. Machine Learning Approaches to Estimating Software Development Effort, IEEE Transactions on Software Engineering, 21 (2), 1995.
  • Kemerer, C. 1987. An empirical validation of software cost estimation models, Communications of the ACM, 30(5), 416-429. doi: 10. 1145/22899. 22906, 1987.
  • Albrecht. A. J. and J. E. Gaffney. 1983. 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. 1981. Software Engineering Economics, Englewood Cliffs, NJ, Prentice-Hall, 1981.
  • Max Welling Kernel Principal Components Analysis.
  • dr in?. Ma?gorzata Syczewska, dr in?. Piotr W?siewicz. Contemporary techniques to manage of databases in gait analysis.