Document Type: Original Research Paper


Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran


A successful software should be finalized with determined and predetermined cost and time. Software is a production which its approximate cost is expert workforce and professionals. The most important and approximate software cost estimation (SCE) is related to the trained workforce. Creative nature of software projects and its abstract nature make extremely cost and time of projects difficult to estimate. Various methods have been presented in the software project cost estimation for performing a software project in the area of software engineering. COCOMO II model is one of the most documented models among template-based methods that has been proposed by Bohm. Common methods for estimating the time and cost are essentially abstract, accordingly, providing new methods for SCE is required and necessary. In this paper, a new method is presented to solve the problem of SCE by using hybrid particle swarm optimization (PSO) algorithm and K-nearest neighbor (KNN) algorithm. The method was evaluated on 6 multiple datasets with 8 different evaluation criteria. Obtained results show the more accurate performance of the proposed method.

Graphical Abstract


[1] L. Zhangf, “Software Cost Estimation in Handbook of Software Engineering and Knowledge Engineering,” World Scientific Pub. Co, River Edge, NJ, 2001.

[2] B.W. Boehm, S. Chulani, and D. Reifer, The Rosetta Stone: Making COCOMO 81 Files Work with COCOMO II,1998.

[3] F. S.Gharehchopogh, L. Ebrahimi, I. Maleki, and S. Jodati, “A new novel PSObased approach with hybrid of fuzzy c-means and learning automata in software cost estimation,” Indian Journal of Science and Technology, vol. 7, pp. 795-803, 2014.

[4] Ziauddin, Sh. Kamal, Sh. Khan, and J.A. Nasir, “A fuzzy logic based software cost estimation model,” International Journal of Software Engineering and Its Applications, vol. 7, no. 2, pp. 7- 18, 2013.

[5] P.V.G.D.P. Reddy, CH.V.M.K. Hari, and R.T. Srinivasa, “Multi objective particle swarm optimization for software cost estimation,” International Journal of Computer Applications, vol. 32, no.3, pp. 13-17, 2011.

[6] T.R. Benala, S. Dehuri, S.C. Satapathy, and S. Madhurakshara, “Genetic algorithm for optimizing functional link artificial neural network based software cost estimation,” in Proc. 2012 International Conference on Information Systems Design, pp. 75–82.

[7] F. S.Gharehchopogh, Z.A. Dizaji, “A new approach in software cost estimation with hybrid of bee colony and chaos optimizations algorithms,” MAGNT Research Report, vol. 2, no. 6, pp. 1263-127, 2014.

[8] Z.A.Khalifelu, F.S.Gharehchopogh,“Comparison and evaluation data mining techniques with algorithmic models in software cost estimation,” Procedia-Technology Journal, vol. 1, pp. 65- 71, 2012.

[9] Z.A. Dizaji, F. S.Gharehchopogh, “A hybrid of ant colony optimization and chaos optimization algorithms approach for software cost estimation,” Indian Journal of Science and Technology, vol. 8, no.2, pp.128–133,2015.

[10] E.E. Miandoab, F.S. GHAREHCHOPOGH, “A novel hybrid algorithm for software cost estimation based on cuckoo optimization and k-nearest neighbors algorithms,” Engineering, Technology & Applied Science Research, vol. 6, no. 3, pp. 1018-1022, 2016.

[11] L.F. Capretz, V. Marza, “Improving effort estimation by voting software estimation models,” Advances in Software Engineering, pp. 1-8, 2009.

[12] S. Kumari, S. Pushkar, “Performance analysis of the software cost estimation methods: a review,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, pp. 229-238, 2013.

[13] T.M. Cover, P.E. Hart, “nearest neighbor pattern classification,” IEEE Trans. Inform. Theory, vol. 13, pp 2-21, 1967. [14] J. Kennedy, R.C. Eberhart, “Particle swarm optimization,” 1995 IEEE Conference on Neural Networks, Perth, Australia; pp. 1942–1948.

[15] F.D. Mokri, M. Molanli, “software cost estimation using adaptive neuro fuzzy inference system,” International Journal of Academic Research in Computer Engineering, vol. 1, no. 1, pp. 34-39, 2016.

[16] F. S.Gharehchopogh, S. Jodati, and I. Maleki, “object oriented software engineering models in software industry,” International Journal of Computer Applications, vol. 95,no.3, pp. 13-16, 2014.