Document Type: Other

Authors

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

Abstract

The literature review shows software development projects often neither meet time deadlines, nor run within the allocated budgets. One common reason can be the inaccurate cost estimation process, although several approaches have been proposed in this field. Recent research studies suggest that in order to increase the accuracy of this process, estimation models have to be revised. The Constructive Cost Model (COCOMO) has often been referred as an efficient model for software cost estimation. The popularity of COCOMO is due to its flexibility; it can be used in different environments and it covers a variety of factors. In this paper, we aim to improve the accuracy of cost estimation process by enhancing COCOMO model. To this end, we analyze the cost drivers using meta-heuristic algorithms. In this method, the improvement of COCOMO is distinctly done by effective selection of coefficients and reconstruction of COCOMO. Three meta-heuristic optimization algorithms are applied synthetically to enhance the process of COCOMO model. Eventually, results of the proposed method are compared to COCOMO itself and other existing models. This comparison explicitly reveals the superiority of the proposed method.

Keywords

Main Subjects

[1] P. Naur and B. Randell, "Report on a conference sponsored by the nato science committee garmisch germany," 7th to 11th October 1968. Scientific Affairs Division, NAto, 1969.
[2] O. Hazzan and Y. Dubinsky, Agile software engineering: Springer Science & Business Media, 2009.
[3] B. W. Boehm, Software engineering economics vol. 197: Prentice-hall Englewood Cliffs (NJ), 1981.
[4] I. Attarzadeh and S. H. Ow, "Soft computing approach for software cost estimation," Int. J. of Software Engineering, IJSE, vol. 3, pp. 1-10, 2010.
[5] R. D. Stutzke, Software estimating technology: A survey: Los. Alamitos, CA: IEEE Computer Society Press, 1997.
[6] B. W. Boehm, R. Madachy, and B. Steece, Software cost estimation with Cocomo II with Cdrom: Prentice Hall PTR, 2000.
[7] Y.-F. Li, M. Xie, and T. N. Goh, "A study of project selection and feature weighting for analogy based software cost estimation," Journal of Systems and Software, vol. 82, pp. 241-252, 2009.
[8] Z. Fei and X. Liu, "f-COCOMO: fuzzy constructive cost model in software engineering," in Fuzzy Systems, 1992., IEEE International Conference on, 1992, pp. 331-337.
[9] A. Idri, A. Abran, and T. M. Khoshgoftaar, "Estimating software project effort by analogy based on linguistic values," in Software Metrics, 2002. Proceedings. Eighth IEEE Symposium on, 2002, pp. 21-30.
[10] Z. Xu and T. M. Khoshgoftaar, "Identification of fuzzy models of software cost estimation," Fuzzy Sets and Systems, vol. 145, pp. 141-163, 2004.
[11] A. F. Sheta and S. Aljahdali, "Software effort estimation inspired by COCOMO and FP models: A fuzzy logic approach," International Journal of Advanced Computer Science and Applications, vol. 4, 2013.
[12] L. V. Patil, N. M. Shivale, S. Joshi, and V. Khanna, "Improving the accuracy of CBSD effort estimation using fuzzy logic," in Advance Computing Conference (IACC), 2014 IEEE International, 2014, pp. 1385-1391.
[13] K. Moløkken and M. Jørgensen, "Expert estimation of web-development projects: are software professionals in technical roles more optimistic than those in non-technical roles?," Empirical Software Engineering, vol. 10, pp. 7-30, 2005.
[14] B. W. Boehm and R. Valerdi, "Achievements and challenges in cocomo-based software resource estimation," IEEE software, vol. 25, 2008.
[15] V. Khatibi and D. N. Jawawi, "Software cost estimation methods: A review 1," 2011.
[16] J. W. Keung, B. A. Kitchenham, and D. R. Jeffery, "Analogy-X: Providing statistical inference to analogy-based software cost estimation," IEEE Transactions on Software Engineering, vol. 34, pp. 471-484, 2008.
[17] C.-c. Lai and W.-l. Lee, "A WICE approach to real-time construction cost estimation," Automation in Construction, vol. 15, pp. 12-19, 2006.
[18] G.-H. Kim, S.-H. An, and K.-I. Kang, "Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning," Building and environment, vol. 39, pp. 1235-1242, 2004.
[19] V. Khatibi Bardsiri and M. Dorosti, "An Improved COCOMO based Model to Estimate the Effort of Software Projects," Journal of Advances in Computer Engineering and Technology, vol. 2, pp. 11-22, 2016.
[20] E. Khatibi, "Investigating the effect of software project type on accuracy of software development effort estimation in COCOMO model," in Fourth International Conference on Machine Vision (ICMV 11), 2011, pp. 83500G-83500G-7.
[21] A. Idri, A. Zakrani, and A. Zahi, "Design of radial basis function neural networks for software effort estimation," IJCSI International Journal of Computer Science Issues, vol. 7, 2010.
[22] G. S. Sandhu and D. S. Salaria, "A Bayesian Network Model of the Particle Swarm Optimization for Software Effort Estimation," International Journal of Computer Applications, vol. 96, 2014.
[23] A. Dhiman and C. Diwaker, "Optimization of COCOMO II effort estimation using genetic algorithm," American International Journal of Research in Science, Technology, Engineering & Mathematics, vol. 3, 2013.
[24] D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical report-tr06, Erciyes university, engineering faculty, computer engineering department2005.
[25] B. T. Rao, B. Sameet, G. K. Swathi, K. V. Gupta, C. RaviTeja, and S. Sumana, "A novel neural network approach for software cost estimation using Functional Link Artificial Neural Network (FLANN)," International Journal of Computer Science and Network Security, vol. 9, pp. 126-131, 2009.
[26] C. S. Reddy and K. Raju, "A concise neural network model for estimating software effort," International Journal of Recent Trends in Engineering, vol. 1, pp. 188-193, 2009.
[27] B. K. Singh and A. Misra, "Software effort estimation by genetic algorithm tuned parameters of modified constructive cost model for nasa software projects," International Journal of Computer Applications, vol. 59, 2012.
[28] V. K. Bardsiri, D. N. A. Jawawi, S. Z. M. Hashim, and E. Khatibi, "A PSO-based model to increase the accuracy of software development effort estimation," Software Quality Journal, vol. 21, pp. 501-526, 2013.
[29] S. K. Sehra, Y. S. Brar, N. Kaur, and G. Kaur, "Optimization of COCOMO Parameters using TLBO Algorithm," International Journal of Computational Intelligence Research, vol. 13, pp. 525-535, 2017.
[30] R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on, 1995, pp. 39-43.
[31] Y. Shi and R. Eberhart, "A modified particle swarm optimizer," in Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, 1998, pp. 69-73.
[32] J. Kennedy, "The particle swarm: social adaptation of knowledge," in Evolutionary Computation, 1997., IEEE International Conference on, 1997, pp. 303-308.
[33] D. Goldberg, "Genetic algorithms in optimization, search and machine learning," Reading: Addison-Wesley, 1989.
[34] L. M. Schmitt, "Theory of genetic algorithms," Theoretical Computer Science, vol. 259, pp. 1-61, 2001.