Document Type: Original Research Paper

Authors

1 Kerman Branch, Islamic AzadUniversity

2 Kerman Branch, Islamic Azad University, Kerman, Iran

Abstract

One of important aspects of software projects is estimating the cost and time required to develop projects. Nowadays, this issue has become one of the key concerns of project managers. Accurate estimation of essential effort to produce and develop software is heavily effective on success or failure of software projects and it is highly regarded as a vital factor. Failure to achieve convincing accuracy and little flexibility of current models in this field have attracted the attention of researchers in the last few years. Despite improvements to estimate effort, no agreement was obtained to select estimation model as the best one. One of effort estimation methods which is highly regarded is COCOMO. It is an extremely appropriate method to estimate effort. Although COCOMO was invented many years ago, it enjoys the effort estimation capability in software projects. Researchers have always attempted to improve the effort estimation capability in COCOMO through improving its structure. However, COCOMO results are not always satisfactory. The present study introduces a hybrid model for increasing the accuracy of COCOMO estimation. Combining bee colony algorithm with COCOMO estimation method, the proposed method obtained more efficient coefficient relative to the basic mode of COCOMO. Selecting the best coefficients maximizes the efficiency of the proposed method. The simulation results revealed the superiority of the proposed model based on MMRE and PRED(0.15).

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Main Subjects

[1].Pressman, R. S. Software Engineering: A Practioner's Approach (6th ed.): McGraw-Hill New York, USA. (2005).
[2].Dhiman A, Diwaker C. Optimization of COCOMO II Effort Estimation using Genetic Algorithm. American International Journal of Research in Science, Technology,Engineering & Mathematics. 2013:208-212.
[3].Li Y-F, Xie M, Goh TN. A study of project selection and feature weighting for analogy based software cost estimation. Journal of Systems and Software, 2009;82(2):241-252.
[4]. Fei Z, Liu X, f-COCOMO: fuzzy constructive cost model in software engineering.IEEE International Conference on Fuzzy Systems; 1992;331-337.
[5]. Idri A, Abran A, Khoshgoftaar TM, Estimating software project effort by analogy based on linguistic values. Eighth IEEE Sympoium on Software Metrics; 2002:21-30.
[6]. Xu Z, Khoshgoftaar TM. Identification of fuzzy models of software cost estimation. Fuzzy Sets and Systems. 2004;145(1):141-163.
[7]. Sheta A, Aljahdali S. Software effort estimation inspired by COCOMO and FP models: A fuzzy logic approach. International Journal of Advanced Computer Science and Applications. 2013;4(11):192-197.
[8].Patil LV, Shivale NM, Joshi S, Khanna V. Improving the accuracy of CBSD effort estimation using fuzzy logic. International Advance Computing Conference (IACC), 2014;1385-1391.
[9].Moløkken K, Jørgensen M. Expert estimation of web-development projects: Are software professionals in technical roles more optimistic than those in non-technical roles? .Empirical Software Engineering. 2005;10(1):7-30.
[10].Boehm BW, Valerdi R. Achievements and challenges in cocomo-based software resource estimation. Software, IEEE. 2008;25(5):74-83.
[11].Khatibi V, Jawawi DN. Software Cost Estimation Methods: A Review, Journal of Emerging Trends in Computing and Information Sciences, 2010;1(2):21-29.
[12].Keung JW, Kitchenham BA, Jeffery DR. Analogy-X: providing statistical inference to analogy-based software cost estimation. IEEE Transactions on Software Engineering, 2008;34(4):471-484.
[13].Yu W-d, Lai C-c, Lee W-l. A WICE approach to real-time construction cost estimation. Automation in Construction. 2006;15(1):12-19.
[14].Kim G-H, An S-H, Kang K-I. Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and environment. 2004;39(10):1235-1242.
[15].Ismaeel HR, Jamil AS. Software Engineering Cost Estimation Using COCOMO II Model.2007
[16].Khatibi E, Investigating the effect of software project type on accuracy of software development effort estimation in COCOMO model. Fourth International Conference on Machine Vision (ICMV); 2011;8(3):60-70.
[17].Idri A, Zakrani A, Zahi A. Design of radial basis function neural networks for software effort estimation. International Journal of Computer Science Issues. 2010;7(4):1-10.
[18].Rao GS, Krishna CVP, Rao KR, Multi Objective Particle Swarm Optimization for Software Cost Estimation. ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of CSI, 2014;1(5);125-140.
[19].SinghSandhu G, Singh Salaria D. A Bayesian Network Model of the Particle Swarm Optimization for Software Effort Estimation. International Journal of Computer Applications. 2014;96(4):52-58.
[20].Karaboga D. An Idea Based on Honey Bee Swarm For Numerical Optimization. Technical Report-TR06. 2005.
[21].Rao BT, Sameet B, Swathi GK, Gupta KV, RaviTeja C, Sumana S. A novel neural network approach for software cost estimation using Functional Link Artificial Neural Network (FLANN). International Journal of Computer Science and Network Security. 2009;9(6):126-131.
[22].Reddy CS, Raju K. A concise neural network model for estimating software effort. International Journal of Recent Trends in Engineering. 2009;1(1):188-93.
[23].Berry MJ, Linoff G. Data mining techniques: for marketing, sales, and customer support, 1997.
[24].Singh BK, Misra A. Software Effort Estimation by Genetic Algorithm Tuned Parameters of Modified Constructive Cost Model for NASA Software Projects. International Journal of Computer Applications. 2012;59(9):22-36.
[25].Bardsiri VK, Jawawi DNA, Hashim SZM, Khatibi E. A PSO-based model to increase the accuracy of software development effort estimation. Software Quality Journal. 2013;21(3):501-526.