[1] A. Sharma, D.S. Kushwaha, Estimation of software development effort from requirements based complexity, Procedia Technology, 4 (2012) 716-722.
[2] G.-H. Kim, S.-H. An, K.-I. Kang, Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning, Building and environment, 39(10) (2004) 1235-1242.
[3] B. Boehm, Cost estimation with COCOMO II, in, University of Southern California, Center for Software Engineering, 2002.
[4] F.J. Heemstra, Software cost estimation, Information and software technology, 34(10) (1992) 627-639.
[5] I. Sommerville, Software engineering 9th Edition, ISBN-10, 137035152 (2011) 18.
[6] A.R. Gray, S.G. MacDonell, A comparison of techniques for developing predictive models of software metrics, Information and software technology, 39(6) (1997) 425-437.
[7] V.K. Bardsiri, D.N.A. Jawawi, S.Z.M. Hashim, E. Khatibi, A PSO-based model to increase the accuracy of software development effort estimation, Software Quality Journal, 21(3) (2013) 501-526.
[8] D. Wu, J. Li, C. Bao, Case-based reasoning with optimized weight derived by particle swarm optimization for software effort estimation, Soft Computing, 22(16) (2018) 5299-5310.
[9] N. Dalkey, O. Helmer, An experimental application of the Delphi method to the use of experts, Management science, 9(3) (1963) 458-467.
[10] A.J. Albrecht, J.E. Gaffney, Software function, source lines of code, and development effort prediction: a software science validation, IEEE transactions on software engineering, (6) (1983) 639-648.
[11] B.W. Boehm, Software engineering economics, IEEE transactions on Software Engineering, (1) (1984) 4-21.
[12] M. Shepperd, C. Schofield, B. Kitchenham, Effort estimation using analogy, in: Proceedings of IEEE 18th International Conference on Software Engineering, IEEE, 1996, pp. 170-178.
[13] F. Walkerden, R. Jeffery, An empirical study of analogy-based software effort estimation, Empirical software engineering, 4 (1999) 135-158.
[14] A. Idri, A. Zahi, A. Abran, Generating fuzzy term sets for software project attributes using fuzzy c-means and real coded genetic algorithms, in: Proceedings of the International Conference on Information and Communication Technology for the Muslim World (ICT4M), Malaysia, 2006, pp. 120-127.
[15] M. Azzeh, D. Neagu, P. Cowling, Software project similarity measurement based on fuzzy C-means, in: Making Globally Distributed Software Development a Success Story: International Conference on Software Process, ICSP 2008 Leipzig, Germany, May 10-11, 2008 Proceedings, Springer, 2008, pp. 123-134.
[16] J.W. Keung, B.A. Kitchenham, D.R. Jeffery, Analogy-X: providing statistical inference to analogy-based software cost estimation, IEEE Transactions on Software Engineering, 34(4) (2008) 471-484.
[17] I. Attarzadeh, S.H. Ow, Proposing a new software cost estimation model based on artificial neural networks, in: 2010 2nd International Conference on Computer Engineering and Technology, IEEE, 2010, pp. V3-487-V483-491.
[18] M. Azzeh, D. Neagu, P.I. Cowling, Analogy-based software effort estimation using Fuzzy numbers, Journal of Systems and Software, 84(2) (2011) 270-284.
[19] F.-A. Amazal, A. Idri, A. Abran, An analogy-based approach to estimation of software development effort using categorical data, in: 2014 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement, IEEE, 2014, pp. 252-262.
[20] V. Khatibi Bardsiri, E. Khatibi, Insightful analogy-based software development effort estimation through selective classification and localization, Innovations in Systems and Software Engineering, 11 (2015) 25-38.
[21] S. Kumari, S. Pushkar, A framework for analogy-based software cost estimation using multi-objective genetic algorithm, in: Proceedings of the world congress on engineering and computer Science, 2016.
[22] A. Idri, I. Abnane, Fuzzy analogy based effort estimation: An empirical comparative study, in: 2017 IEEE International Conference on Computer and Information Technology (CIT), IEEE, 2017, pp. 114-121.
[23] S. Ezghari, A. Zahi, Uncertainty management in software effort estimation using a consistent fuzzy analogy-based method, Applied Soft Computing, 67 (2018) 540-557.
[24] H. Mustapha, N. Abdelwahed, Investigating the use of random forest in software effort estimation, Procedia computer science, 148 (2019) 343-352.
[25] M.A. Shah, D.N.A. Jawawi, M.A. Isa, M. Younas, A. Abdelmaboud, F. Sholichin, Ensembling artificial bee colony with analogy-based estimation to improve software development effort prediction, IEEE Access, 8 (2020) 58402-58415.
[26] S. Ranichandra, Optimizing non‐orthogonal space distance using ACO in software cost estimation, Mukt Shabd J, 9(4) (2020) 1592-1604.
[27] Z. Shahpar, V.K. Bardsiri, A.K. Bardsiri, An evolutionary ensemble analogy‐based software effort estimation, Software: Practice and Experience, (2021).
[28] S. Samavatian, K. Mohebbi, Improving the Estimation of Software Development Effort Using the Combination of Cuckoo Search and Particle Swarm Optimization Algorithms, Journal of Soft Computing and Information Technology, 10(3) (2021) 86-98.
[29] Z. Shahpar, V.K. Bardsiri, A.K. Bardsiri, Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing, Concurrency and Computation: Practice and Experience, 33(20) (2021) e6358.
[30] M. Dashti, T.J. Gandomani, D.H. Adeh, H. Zulzalil, A.B.M. Sultan, LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model, PeerJ Computer Science, 7 (2022) e800.
[31] S. Hameed, Y. Elsheikh, M. Azzeh, An optimized case-based software project effort estimation using genetic algorithm, Information and Software Technology, 153 (2023) 107088.
[32] A. Moradbeiky, FEEM: A Flexible Model based on Artificial Intelligence for Software Effort Estimation, Journal of AI and Data Mining, 11(1) (2023) 39-51.
[33] N. Pal, M.P. Yadav, D.K. Yadav, Appropriate number of analogues in analogy based software effort estimation using quality datasets, Cluster Computing, 27(1) (2024) 531-546.
[34] R. Eberhart, J. Kennedy, Particle swarm optimization, in: Proceedings of the IEEE international conference on neural networks, Citeseer, 1995, pp. 1942-1948.
[35] M. Mitchell, An introduction to genetic algorithms, MIT press, 1998.
[36] S.D. Conte, H.E. Dunsmore, V.Y. Shen, Software engineering metrics and models, Benjamin-Cummings Publishing Co., Inc., 1986.
[37] K.D. Maxwell, Applied statistics for software managers, Applied Statistics for Software Managers, (2002).
[38] J. Desharnais, Analyse statistique de la productivitie des projects informatique a partie de la technique des point des function, Masters Thesis University of Montreal, (1989).
[39] K. Khan, The Evaluation of Well-known Effort Estimation Models based on Predictive Accuracy Indicators, in, 2010.
[40] E. Kocaguneli, T. Menzies, Software effort models should be assessed via leave-one-out validation, Journal of Systems and Software, 86(7) (2013) 1879-1890.
[41] J. Wen, S. Li, Z. Lin, Y. Hu, C. Huang, Systematic literature review of machine learning based software development effort estimation models, Information and Software Technology, 54(1) (2012) 41-59.