A New Architecture Based on Artificial Neural Network and PSO Algorithm for Estimating Software Development Effort

Subject Areas : Project Management

Amin Moradbeiky ¹ , Amid Khatibi Bardsiri ²

1 - Islamic Azad University, Kerman Branch, Kerman, Iran
2 - 1Islamic Azad University, Kerman Branch, Kerman, Iran

Received: 2017-02-26 Accepted : 2017-12-14 Published : 2018-02-01

Keywords: Nerual Networks, Development effort estimation, software project, Particle swarm optimization,

Abstract :

Software project management has always faced challenges that have often had a great impact on the outcome of projects in future. For this, Managers of software projects always seek solutions against challenges. The implementation of unguaranteed approaches or mere personal experiences by managers does not necessarily suffice for solving the problems. Therefore, the management area of software projects requires tools and means helping software project managers confront with challenges. The estimation of effort required for software development is among such important challenges. In this study, a neural-network-based architecture has been proposed that makes use of PSO algorithm to increase its accuracy in estimating software development effort. The architecture suggested here has been tested by several datasets. Furthermore, similar experiments were done on the datasets using various widely used methods in estimating software development. The results showed the accuracy of the proposed model. The results of this research have applications for researchers of software engineering and data mining.

References:

[1] The Standish Group, “Chaos Report,” Technical report, http://www.standishgroup.com, 2009.
[2] Nelson, E.A. Management Handbook for the Estimation of Computer Programming Costs. System Developer Corp., 1966.
[3] Boehm, B. Software Engineering Economics. Prentice Hall, 1981.
[4] Boehm, B., Madachy, R. and Steece, B. Software Cost Estimation with Cocomo II. Prentice Hall, 2000.
[5] Putnam, L.H. “A General Empirical Solution to the Macro Software Sizing and Estimation Problem,” IEEE Trans. Software Eng., vol. 4, no. 4, pp. 345-361, July 1978.
[6] Albrecht, A.J. and Gaffney, J-E. “Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation,” IEEE Trans. Software Eng., vol. 9, no. 6, pp. 639-648, Nov. 1983.
[7] Finnie, G., Wittig, G. and Desharnais, J-M. “A Comparison of Software Effort Estimation Techniques: Using Function Points with Neural Networks, Case-Based Reasoning and Regression Models,” J. Systems and Software, vol. 39, pp. 281-289, 1997.
[8] Sentas, P., Angelis, L., Stamelos, I. and Bleris, G. “Software Productivity and Effort Prediction with Ordinal Regression,” Information and Software Technology, vol. 47, pp. 17-29, 2005.
[9] Briand, L., Emam, K.E., Surmann, D. and Wieczorek, I. “An Assessment and Comparison of Common Software Cost Estimation Modeling Techniques,” Proc. 21st Int’l Conf. Software Eng., pp. 313-323, May 1999.
[10] Briand, L., Langley, T. and Wieczorek, I. “A Replicated Assessment and Comparison of Common Software Cost Modeling Techniques,” Proc. 22nd Int’l Conf. Software Eng., pp. 377-386, June 2000.
[11] Shepperd, M., Schofield, C. (1997) Estimating software project effort using analogies. IEEE Trans Softw Eng 23 (11):736–743
[12] Angelis, L., Stamelos, I. (2000) A simulation tool for efficient analogy based cost estimation. Empir Softw Eng 5(1):35–68
[13] Chiu, N-H., Huang, S-J. (2007) The adjusted analogy-based software effort estimation based on similarity distances. J Syst Softw 80(4):628–640
[14] Gupta, S., Sikka, G., Verma, H. (2011) Recent methods for software effort estimation by analogy. SIGSOFT Softw Eng Notes 36(4):1–5
[15] Kocaguneli, E., Menzies, T., Bener, A. and Keung, JW. (2012) Exploiting the essential assumptions of analogy-based effort estimation. IEEE Trans Softw Eng 38(2):425–438
[16] Milios, D., Stamelos, I. and Chatzibagias, C. (2011) Global optimization of analogy-based software cost estimation with genetic algorithms, artificial intelligence applications and innovations. L. Iliadis, I. Maglogiannis and H. Papadopoulos, Springer Boston. 364:350–359
[17] Kocaguneli, E. and Menzies, T. (2013), “Software effort models should be assessed via leave-one-out validation", The Journal of Systems and Software, 1879-1890.