Abstract
Many modeling studies that aimed at providing an accurate relationship between the software project effort (or cost) and the involved cost drivers have been conducted for effective management of software projects. However, the derived models are only applicable for a specific project and its variables. In this chapter, we present the use of back-propagation neural network (NN) to model the software development (SD) effort of 18 SD NASA projects based on six cost drivers. The performance of the NN model was also compared with a multi-regression model and other models available in the literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shukla, R. Misra, A.K.: Estimating software maintenance effort - a neural network approach. 1st India, Software Engineering Conference. 107–112 (2008).
Jorgensen, M.: A review of studies on expert estimation of software development effort. J Syst. Software. 70(1–2), 37–60 (2004)
Jorgensen, M., Shepperd, M.: A systematic review of software development cost estimation studies. IEEE T. Software Eng. 33(1), 33–53 (2007)
Verner, J.M., Evanco, W.M., Cerpa, N.: State of the practice: an exploratory analysis of schedule estimation and software project success prediction. Inform. Software Tech. 49(2), 181–193 (2007)
Jorgensen, M., Ostvold, K.M.: Reasons for software effort estimation error: impact of respondent role, information collection approach, and data analysis method. IEEE T. Software Eng. 30(12), 993–1007 (2004)
Huang, X., Ho, D., Ren, J., Capretz, L.F.: A soft computing framework for software effort estimation. Soft Comput. 10, 170–177 (2006)
Tronto, I.F.B., Silva, J.D.S., Anna, N.S.: An investigation of artificial neural networks based prediction systems in software project management. J Syst. Software. 81(3), 356–367 (2008)
Kumar, K.V., Ravi, V., Carr, M., Kiran, N.R.: Software development cost estimation using wavelet neural networks. J Syst. Software. 81(11), 1853–1860 (2008)
Sheta, A.: Estimation of the COCOMO model parameters using genetic algorithms for NASA software projects. J. Comput. Sci. 2(2), 118–123 (2006)
Xu, Z.W., Khoshgoftaar, T.M.: Identification of fuzzy models of software cost estimation. Fuzzy Set. Syst. 145(1), 141–163 (2004)
Ahmed, M., Saliu, M.O., Alghamdi, J.: Adaptive fuzzy logic-based framework for software development effort prediction. Inform. Software Tech. 47(1), 31–48 (2005)
Kazemifard, M., Zaeri, A., Ghasem-Aghaee, N., Nematbakhsh, M.A., Mardukhi, F.: Fuzzy emotional COCOMO II software cost estimation (FECSCE) using multi-agent systems. Appl. Soft Comput. 11(2), 2260–2270 (2011)
Shukla, K.K.: Neuro-genetic prediction of software development effort. Inform. Software Tech. 42, 701–713 (2000)
Huang, S.J., Chiu, N.H.: Optimization of analogy weights by genetic algorithm for software effort estimation. Inform. Software Tech. 48, 1034–1045 (2006)
Sheta, A. F., Al-Afeef, A.: A GP effort estimation model utilizing line of code and methodology for NASA software projects. 10th International Conference on Intelligent Systems Design and Applications. 290–295 (2010).
Jorgensen, M.: Regression models of software development effort estimation accuracy and bias. Empir. Softw. Eng. 9, 297–314 (2004)
Shukla, R., Misra, A.K.: Software maintenance effort estimation-neural network vs regression modeling approach. Int. J. Comput. Applic. 1(29), 83–89 (2010)
Bailey, J.W., Basili, V.R.: A metamodel for software development resource expenditures. 5th IEEE International Conference on, Software Engineering. 107–116 (1981).
www.minitab.com (2012).
www.mathworks.com/access/helpdesk/help/pdf_doc/nnet/nnet.pdf (2012).
Acknowledgments
The authors would like to acknowledge the financial support extended by the Faulty of Engineering and Built Environment, University of Johannesburg.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Shukla, R., Shukla, M., Marwala, T. (2014). Neural Network and Statistical Modeling of Software Development Effort. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_21
Download citation
DOI: https://doi.org/10.1007/978-81-322-1602-5_21
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1601-8
Online ISBN: 978-81-322-1602-5
eBook Packages: EngineeringEngineering (R0)