[1] Du, W.L., D. Ho, and L.F. Capretz, 2015. Improving software effort estimation using neuro-fuzzy model with SEER-SEM. arXiv preprint arXiv:1507.06917.
[2] Bawa, A., M.R. Chawla, and D. Karnal, 2012. Experimental analysis of effort estimation using artificial neural network. Int. J. Electron. Comput. Sci. Eng. 1(3): p. 1817-1824.
[3] Popović, J. and D. Bojić, 2012. A comparative evaluation of effort estimation methods in the software life cycle. Computer Science and Information Systems. 9(1): 456- 484.
[4] Kad, S., Chopra, V., 2012, Software development effort estimation using soft computing, International Journal of Machine Learning and Computing, 2(5): p. 437-439.
[5] Kashyap, D., Misra, A.K., 2013, An approach for software effort estimation using fuzzy numbers and genetic algorithm to deal with uncertaint, Computer Science & Information Technology (CS & IT), p.57-66.
[6] Sheta, A., Rine, D., Ayesh, A., 2008, Development of software effort and schedule estimation models using soft computing techniques, IEEE Transaction: p. 978-1.
[7] Clemmons, R.K., 2006, Project estimation with use case points, The Journal of Defense Software Engineering, p. 18-22 .
[8] Peschi, B., 2009, Recommending effort estimate method for software project management, Web Intelligence and Intelligent Agent Technologies, p. 77-80.
[9] AZ Adem, N., M Kasirun, Z., 2010, Automating function point analysis based on the functional and the non-functional requirement text, Computer and Automation Engineering, 5: p.664-669.
[10] Kumari, S., Pushkar, SH., 2013, Performance analysis of the software cost estimation methods: A review, International Journal of Advanced Research in Computer Science and Software Engineering, 3(7):p. 229-238.
[11] Suri, P.K., 2012, Comparative analysis of software effort estimation techniques, International Journal of Computer Application, 48(21):p. 1-8.
[12] Parkash, J., 2014, Cocomo ii model parameter optimization using pso and effort estimation, Journal of Information Technology & Mechanical Engineering, 1(4): p.1-11.
[13] Bardsiri, A.K. and S.M. Hashemi., 2014, Software Effort Estimation: A Survey of Well-known Approaches. International Journal of Computer Science Engineering (IJCSE). 3(1): p. 46-50
[14] Khatibi V, Jawawi DN. 2010; Software Cost Estimation Methods: A Review,Journal of Emerging Trends in Computing and Information Sciences, 1(2): p.21-29.
[15] Chattopadhyay, S., et al., 2013. A Case‐Based Reasoning system for complex medical diagnosis. Expert Systems, 30(1): p. 12-20
[16] Azzeh, M. 2011, Adjusted case-based software effort estimation using bees optimization algorithm. in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Springer, V.6882 p. 315-324
[17] Finnie GR, Wittig GE, Desharnais J-M. A,1997 comparison of software effort estimation techniques: using function points with neural networks, case-based reasoning and regression models. Journal of Systems and Software; 39(3):p.281-9.
[19] Khatibi, V., A.N. jawawi, D., 2011, Software cost estimation methods: a review ,Journal of Emerging Trends in Computing and Information Sciences, 2(1): 21-29.
[20] Khatibi Bardsiri, A. and S.M. Hashemi, 2016. A differential evolution‐based model to estimate the software services development effort. Journal of Software: Evolution and Process, 28(1): p. 57-77
[22] Ashegi Dizaji, Z., Ahmadi, R., Gholizadeh, H., Soleimanian Gharehchopogh, F., 2014, A bee colony optimization algorithm approach for software cost estimation, International Journal of Computer Applications, 104(12): p.41-44.
[23] Živadinović, Ph.D., Dragan Maksimović, Z., Damnjanović, A., Vujčić, S., Serbia, Z., 2011, Methods of effort estimation in software engineering, International Symposium Engineering Management And Competitiveness, p.417-422.
[24] Soleimanian Gharehchopogh, F., Asheghi Dizaji, Z., 2014, A new approach in software cost estimation with hybrid of bee colony and chaos optimizations algorithms, Magnt Research Report, 2(6): p. 1263-1271.
[25] Khatibi, V., A.N. Jawawi, D., Mohd Hashim, S.Z., Khatibi, E., 2012, A pso-based model to increase the accuracy of software development effort estimation, Software Qual J , 21: p.501-526.
[26] Nasiri, B. and M. Meybodi, 2012, Speciation based firefly algorithm for optimization in dynamic environments. International Journal of Artificial Intelligence. 8(S12): p. 118-132
[27] Kwiecień, J. and B. Filipowicz, 2012. Firefly algorithm in optimization of queueing systems. Bulletin of the Polish Academy of Sciences: Technical Sciences, 60(2): p. 363-368
[28] Attarzadeh, I., Hock Ow, S., 2009, Proposing a new high performance model for software cost estimation, International Conference on Computer and Electrical Engineering, 2: p. 112-119.
[29] Chattopadhyay, S., et al. 2013, A Case‐Based Reasoning system for complex medical diagnosis. Expert Systems, 30(1): p. 12-20
[30] Finnie, G.R., G.E. Wittig, and J.-M. Desharnais, A, 1997 comparison of software effort estimation techniques: using function points with neural networks, case-based reasoning and regression models. Journal of Systems and Software. 39(3): p. 281-289
[31] Ketata, R., Bellaaj, H., Chtourou, M., Amer, M.B., 2007, Adjustment of membership functions, generation and reduction of fuzzy rule base from numerical data, Malaysian Journal of Computer Science, 20(2):p. 147-169.
[32] Hamdy, A., 2012, Fuzzy logic for enhancing the sensitivity of cocomo cost model, Journal of Emerging Trends in Computing and Information Sciences, 3(9): p.1292-1297.
[33] Krishnanand, K. and D. Ghose, 2009. Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm intelligence, 3(2): p. 87-124
[34] Yang, X.-S., 2010, Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons,INC, Publication
[35] Adriano, L.I. O., 2010, GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation,
Information and Software Technology, 52(11): p.1155-1168.
[36] Dan, Z. 2013, Improving the accuracy in software effort estimation: Using artificial neural network model based on particle swarm optimization. in Service Operations and Logistics, and Informatics (SOLI) ,2013 IEEE International Conference on, p.180-185.
[37] Khatibi Bardsiri, A., S.M. Hashemi, and M. Razzazi, 2016, GVSEE: a new global model to estimate software services development effort. Journal of the Chinese Institute of Engineers: v. 39 , p. 1-12.
[38] Madhusudan, T., J.L. Zhao, and B. Marshall, 2004. A case-based reasoning framework for workflow model management. Data & Knowledge Engineering, 50(1): p. 87-115
[39] Kocaguneli, E., et al., 2012. Exploiting the essential assumptions of analogy-based effort estimation. IEEE Transactions on Software Engineering, 38(2): p. 425-438
[40] Idri A, azzahra Amazal F, Abran A. 2015, Analogy-based software development effort estimation: A systematic mapping and review. Information and Software Technology; 58: p. 206–230
[41] Bardsiri, A.K., S.M. Hashemi, and M. Razzazi, 2015. Statistical Analysis of The Most Popular Software Service Effort Estimation Datasets. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 7(1): p.8.96-7
[42] Elsheikh, y., Alseid, M., Azzeh, M., 2014, An optimized analogy-based project effort estimation, International Journal of Advanced Computer Science and Applications, 5(4): p. 6-11.
[44] Meysam Mousavi , S., Tavakkoli-Moghaddam, R., Vahdani , B., Hashemi , H., sanjari, M.J., 2013, A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects,
Robotics and Computer-Integrated Manufacturing, 29(1): p.157-168.
[45] خطیبی بردسیری, ع., س.م. هاشمی, and م. رزازی, ارائه یک مدل جدید جهت تخمین تلاش لازم برای توسعه سرویس های نرم افزاری. مدل سازی در مهندسی, 2017. 15(49): p. 20-20.