Create accurate predictions of software effort has been the target of many researchers for more than thirty years. Many different methods have been created with the objective of trying to achieve this goal. Unfortunately, none of these methods developed so far have been able to prove a consistent estimation. Until now, most researches have focused on trying to find a method which the estimations of software development effort are the most accurate. However, due to the complexity and the differences between various software projects, it would be very difficult for a single method to be accurate in all the cases.
We will try to show that combining software effort estimation methods can be the solution for improving the accuracy of software effort estimation. It has been shown that using only one method to estimate effort has the advantage of simplicity, but it has the disadvantage of losing the contribution of useful information others methods can provide.
The objectives of this master thesis are to integrate several software effort estimation methods and assigning the right weights to each method. We will propose a useful approach that can assign the weights based on the outcome of a criterion used to measure the accuracy of software estimation methods. Seven public datasets and five different estimation methods are use to evaluate the accuracy of our combination models.
Experimental results have shown that the proposed combination models can be a useful method for improving the estimation accuracy.

[1] M. Agrawal, K. Chari, “Software Effort, Quality, and Cycle Time: A Study of CMM Level 5 Projects”, IEEE Transactions on Software Engineering, 33(3), March, 2007, pp. 145-156.
[2] A.J Albrecht and J.E. Gaffney Jr., “Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation”, IEEE Transactions on Software Engineering, SE-9(6), Nov. 1983, pp. 639-648.
[3] M. Azzeh, D. Neagu, and P. Cowling, “Software effort estimation based on weighted fuzzy grey relational analysis”, In Proceedings of the 5th international Conference on Predictor Models in Software Engineering, Vancouver, British Columbia, Canada, 18 – 19 May 2009. PROMISE '09. ACM, New York, NY, pp. 1-10.
[4] J.W. Bailey and V.R. Basili, “A Meta-Model for Software Development Resource Expenditures”, In Proceedings of the International Conference on Software Engineering, March 1981, pp. 107-116.
[5] B.W. Boehm, Software Engineering Economics, Prentice Hall, 1981.
[6] B.W. Boehm and R. Ross, “Theory-W Software Project Management Principles and Examples”, IEEE Transactions on Software Engineering, 15(7), July 1989, pp. 902-916.
[7] B.W. Boehm, R. Valerdi, J. Lane, and A.W. Brown, “COCOMO Suite Methodology and Evolution”, CrossTalk - The Journal of Defense Software Engineering, 2005.
[8] C.J. Burgess and M. Lefley, “Can Genetic Programming Improve Software Effort Estimation? A comparative Evaluation”, World Scientific Publishing Co., 16 May 2005, pp. 95-105.
[9] R.T Clemen, “Combining forecasts: A review and annotated bibliography”, International Journal of Forecasting, Elsevier, vol. 5(4), 1989, pp. 559-583.
[10] J.L. Deng, “Introduction to Grey System Theory”. The Journal of Grey System 1(1), 1989, pp.1-24.
[11] J.M. Desharnais, “Analyse Statistique de la Roductivitie des Projets Informatique a Partie de la Technique des Point des Fonction,”Masters Thesis, Univ. of Montreal, 1989.
[12] B.E. Hansen, “Least-squares forecast averaging”, Journal of Econometrics, 146(2), October 2008, pp. 342-350.
[13] S. Hashem, “Optimal Linear Combinations of Neural Networks”, Ph.D. Thesis, Purdue University, School of Industrial Engineering, Lafayette, IN 1993.
[14] A. Heiat, “Comparison of Artificial Neural Network and Regression Models for Estimating Software Development Effort”, Information and Software Technology, 44 (15), 1 Dec. 2002, pp. 911-922.
[15] C.J. Hsu and C.Y. Huang, “Improving Effort Estimation Accuracy by Weighted Grey Relational Analysis During Software Development”, The 14th Asia-Pacific Software Engineering Conference (APSEC), Dec. 2007, pp.534-541.
[16] B. Hunt, B. Turner, and K. McRitchie, “Software Maintenance Implications on Cost and Schedule”, Aerospace Conference, 2008 IEEE, 1-8 March 2008, pp.1-6.
[17] A. Idri, A. Zakrani, A. Abran, “Functional Equivalence between Radial Basis Function Neural Networks and Fuzzy Analogy in Software Cost Estimation”, Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008, April 2008, pp.1-5.
[18] M. Jørgensen, U. Indahl, and D. Sjøberg, “Software Effort Estimation by Analogy and 'Regression Toward the Mean'”, Journal of Systems and Software, 68(3), Dec. 2003, pp. 253-262.
[19] M. Jørgensen and M.J. Shepperd, “A Systematic Review of Software Development Cost Estimation Studies”, IEEE Transactions on Software Engineering, 33(1), Jan. 2007, pp.33-53.
[20] C.F. Kemerer, “An Empirical Validation of Software Cost Estimation Models”, Commun. ACM, 30(5), May 1987, pp. 416-429.
[21] M. Lefley and M.J. Shepperd, “Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets”, Proceedings of GECCO 2003, LNCS 2724, Springer-Verlag, pp. 2477-2487.
[22] Q. Li, Q. Wang, Y. Yang, and M. Li, “Reducing Biases in Individual Software Effort Estimations: A Combining Approach”, ESEM '08 ACM, New York, NY, Oct. 2008, pp. 223-232.
[23] M.R. Lyu and A. Nikora, “A Heuristic Approach for Software Reliability Prediction: The Equally-Weighted Linear Combination Model”, in Proceedings 1991 International Symposium on Software Reliability Engineering, Austin, Texas, May 1991, pp. 172-181.
[24] M.R. Lyu and A. Nikora, “Applying Software Reliability Models More Effectively”, IEEE Software, 9(4), July 1992, pp. 43–52.
[25] S.G. MacDonell and M.J. Shepperd, “Combining Techniques to Optimize Effort Predictions in Software Project Management”, Journal of Systems and Software, 66(2), May 2003, pp. 91-98.
[26] C. Mair and M.J. Shepperd, “Looking at Comparisons of Regression and Analogy-based Software Project Cost Prediction”, Available at ww1.ucmss.com/books/LFS/CSREA2006/SER4099.pdf , Brunel University, UK.
[27] T. Menzies, Z. Chen, J. Hihn, and K. Lum, “Selecting Best Practices for Effort Estimation”, IEEE Transactions on Software Engineering, 32(11), Nov. 2006, pp. 883- 895.
[28] I. Myrtveit and E. Stensrud, “A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models”, IEEE Transactions on Software Engineering, 25(4), July-Aug. 1999, pp.510-525.
[29] A.P. Nikora, M.R. Lyu, and T.M. Antczak, “A Linear Combination Software Reliability Modeling Tool with A Graphically-Oriented User Interface”, Proceedings of Symposium on Assessment of Quality Software Development Tools, New Orleans, Louisana, May 1992, pp. 21-31.
[30] J.S. Reel, “Critical success factors in software projects”, Software IEEE, 16(3), May/Jun 1999, pp.18-23.
[31] M.J. Shepperd, C. Schofield, and B.A. Kitchenham, “Effort Estimation Using Analogy”, Proc. 18th Int’l Conf. Software Eng., Berlin: IEEE CS Press, 1996.K.
[32] M.J. Shepperd and C. Schofield, “Estimating Software Project Effort Using Analogies”, IEEE Transactions on Software Engineering, 23(12), Nov 1997, pp. 736-743.
[33] Y. Singh, P.K. Bhatia, A. Kaur, and O. Sangwan, “A Review of Studies on Effort Estimation Techniques of Software Development”, MATEIT-2008, 26 Sept. 2008, pp. 188-196.
[34] K. Srinivasan and D. Fisher, “Machine Learning Approaches to Estimating Software Development Effort”, IEEE Transactions on Software Engineering, 21(2), Feb 1995, pp.126-137.
[35] A. Timmermann, “Forecast Combinations”, Handbook of Economic Forecasting. Elsevier Press, 2006, pp. 135-195.
[36] F. Walkerden and R. Jeffery, “An Empirical Study of Analogy-based Software Effort Estimation”, Empirical Software Engineering, 4(2), 01 June 1999, pp.135-158.
[37] Y. Wang, Q.B. Song, and J.Y. Shen, “Grey Learning Based Software Stage-Effort Estimation”, International Conference on Machine Learning and Cybernetics, 2007, 3, Aug. 2007, pp.1470-1475.
[38] Y. Yang, “Combining Forecasting Procedures: Some Theoretical Results”, RT Journal, Cambridge Journals Online, 20(01), Feb. 2004.
[39] Y. Yang, M. He, M. Li, Q. Wang, and B. Boehm, “Phase distribution of software development effort”, ESEM '08: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement, 2008, pp. 61-69.