Abstract
Nowadays, mature software companies are more interested to have a precise estimation of software metrics such as project time, cost, quality, and risk at the early stages of software development process. The ability to precisely estimate project time and costs by project managers is one of the essential tasks in software development activities, and it named software effort estimation. The estimated effort at the early stage of project development process is uncertain, vague, and often the least accurate. It is because that very little information is available at the beginning stage of project. Therefore, a reliable and precise effort estimation model is an ongoing challenge for project managers and software engineers. This research work proposes a novel soft computing model incorporating Constructive Cost Model (COCOMO) to improve the precision of software time and cost estimation. The proposed artificial neural network model has good generalisation, adaption capability, and it can be interpreted and validated by software engineers. The experimental results show that applying the desirable features of artificial neural networks on the algorithmic estimation model improves the accuracy of time and cost estimation and estimated effort can be very close to the actual effort.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Boehm, B.: Software Engineering Economics. Prentice-Hall, Englewood Cliffs (1981)
Boehm, B., Abts, C., Chulani, S.: Software Development Cost Estimation Approaches – A Survey, University of Southern California Center for Software Engineering, Technical Reports, USC-CSE-2000-505 (2000)
Putnam, L.H.: A General Empirical Solution to the Macro Software Sizing and Estimating Problem. IEEE Transactions on Software Engineering 4(4), 345–361 (1978)
Srinivasan, K., Fisher, D.: Machine Learning Approaches to Estimating Software Development Effort. IEEE Transactions on Software Engineering 21(2), 123–134 (1995)
Molokken, K., Jorgensen, M.: A review of software surveys on software effort estimation. In: IEEE International Symposium on Empirical Software Engineering, ISESE, pp. 223–230 (2003)
Huang, S., Chiu, N.: Applying fuzzy neural network to estimate software development effort. Applied Intelligence Journal 30(2), 73–83 (2009)
Witting, G., Finnie, G.: Using Artificial Neural Networks and Function Points to Estimate 4GL Software Development Effort. Journal of Information Systems 1(2), 87–94 (1994)
Samson, B.: Software cost estimation using an Albus perceptron. Journal of Information and Software 4(2), 55–60 (1997)
Boetticher, G.D.: An assessment of metric contribution in the construction of a neural network-based effort estimator. In: Proceedings of Second International Workshop on Soft Computing Applied to Software Engineering, pp. 234–245 (2001)
Karunanitthi, N., Whitely, D., Malaiya, Y.K.: Using Neural Networks in Reliability Prediction. IEEE Software Engineering 9(4), 53–59 (1992)
Srinivasan, K., Fisher, D.: Machine learning approaches to estimating software development effort. IEEE Transaction on Software Engineering 21(2), 126–137 (1995)
Khoshgoftar, T.M., Allen, E.B., Xu, Z.: Predicting testability of program modules using a neural network. In: Proceeding of 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology, pp. 57–62 (2000)
Khoshgoftar, T.M., Seliya, N.: Fault prediction modeling for software quality estimation: comparing commonly used techniques. Journal of Empirical Software Engineering 8(3), 255–283 (2003)
Shepperd, M., Schofield, C.: Estimating Software Project Effort Using Analogies. IEEE Transactions on Software Engineering 23(11), 736–743 (1997)
Jorgenson, M.: A review of studies on expert estimation of software development effort. Journal of Systems and Software 70(4), 37–60 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Attarzadeh, I., Ow, S.H. (2011). Software Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model. In: Chen, R. (eds) Intelligent Computing and Information Science. ICICIS 2011. Communications in Computer and Information Science, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18129-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-18129-0_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18128-3
Online ISBN: 978-3-642-18129-0
eBook Packages: Computer ScienceComputer Science (R0)