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Recurrent Neural Network based Prediction of Software Effort

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Lujain A. Hussein, Kulood A. Nassar, Maysaa A. Naser

Lujain A Hussein, Kulood A Nassar and Maysaa A Naser. Recurrent Neural Network based Prediction of Software Effort. International Journal of Computer Applications 177(1):40-46, November 2017. BibTeX

	author = {Lujain A. Hussein and Kulood A. Nassar and Maysaa A. Naser},
	title = {Recurrent Neural Network based Prediction of Software Effort},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2017},
	volume = {177},
	number = {1},
	month = {Nov},
	year = {2017},
	issn = {0975-8887},
	pages = {40-46},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017915664},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The enormous efforts of software systems and unexpected efforts in the late phases of software development in software engineering field led to using methods to estimate software effort at early stages of software preparing phases. Therefore, the question remains how can develop an estimation method to be more accurate and gives a prediction for future software efforts. This paper presents a proposed method for software effort prediction, to enhance software effort estimation phase. The proposed method utilizes feed-forward neural network in recurrent fashion to make a prediction and adapt to handle with varying software types in software engineering. The proposed method (RFFNN) used to enhance the results of ordinary software effort estimation methods, RFFNN gives more efficient results by making a prediction for future software efforts.


  1. I. Marsic, Software Engineering. Rutgers University, 2012.
  2. I. Sommerville, Software Engineering. Pearson, 2010.
  3. S. Waghmode and K. Kolhe, “A Novel Way of Cost Estimation in Software Project Development Based on Clustering Techniques,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 2, no. 4, pp. 3892–3899, 2014.
  4. H. Hamza, a Kamel, and K. Shams, “Software Effort Estimation Using Artificial Neural Networks: A Survey of the Current Practices,” Inf. Technol. New Gener. (ITNG), 2013 Tenth Int. Conf., pp. 731–733, 2013.
  5. H. Karna and S. Gotovac, “Estimating software development effort using Bayesian networks,” pp. 229–233, 2015.
  6. P. Kaur and R. Singh, “A Proposed Framework for Software Effort Estimation Using the Combinational Approach of Fuzzy Logic and Neural Networks,” Int. J. Hybrid Inf. Technol., vol. 8, no. 10, pp. 73–80, 2015.
  7. A. Idri, A. Hassani, and A. Abran, “RBFN Networks-based Models for Estimating Software Development Effort: A Cross-validation Study,” 2015 IEEE Symp. Ser. Comput. Intell., vol. 39, no. Ml, pp. 976–983, 2015.
  8. [8] M. T. Hagan, H. B. Demuth, and M. H. Beale, “Neural Network Design,” Bost. Massachusetts PWS, vol. 2, p. 734, 1995.
  9. B. Krose and P. Van Der Smagt, An Introduction to Neural Networks, no. University of Amsterdam. 1996.
  10. M. Kubat, “Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7.,” The Knowledge Engineering Review, vol. 13, no. 4. pp. 409–412, 1999.
  11. Laurene V. Fausett, Fundamentals of Neural Networks. Pearson Education, 1994.
  12. R. C. Lyne and J. C. Maximino, “An Artificial Neural Network Approach to Structural Cost Estimation of Building Projects in the Philippines,” Present. DLSU Res. Congr. 2014, vol. 3, pp. 1–8, 2014.
  13. S. Abbinaya and M. S. Kumar, “Software effort and risk assessment using decision table trained by neural networks,” 2015 Int. Conf. Commun. Signal Process., pp. 1389–1394, 2015.
  14. P. Agrawal and S. Kumar, “Early Phase Software Effort Estimation Model,” 2016 Symp. Colossal Data Anal. Netw., 2016.
  15. M. Bisi and N. K. Goyal, “Software development efforts prediction using artificial neural network,” IET Inst. Eng. Technol., vol. 10, no. 3, pp. 63–71, 2016.


Software efforts estimation, Soft Computing, Recurrent Feed-Forward Neural Network, Neural Networks, Software effort prediction.