J. Wen, S. Li, Z. Lin, Y. Hu, and C. Huang, Systematic literature review of machine learning based software development effort estimation models, Information and Software Technology, vol.54, issue.1, pp.54-95, 2012.
DOI : 10.1016/j.infsof.2011.09.002

R. T. Hughes, Expert judgement as an estimating method, Information and Software Technology, vol.38, issue.2, pp.67-75, 1996.
DOI : 10.1016/0950-5849(95)01045-9

M. Jørgensen, A review of studies on expert estimation of software development effort, Journal of Systems and Software, vol.70, issue.1-2, pp.37-60, 2004.
DOI : 10.1016/S0164-1212(02)00156-5

B. W. Boehm, Software engineering economics, IEEE Trans Softw Eng, issue.10, pp.4-21, 1984.

L. H. Putnam and W. Myers, Measures for excellence: reliable software on time, within budget, 1991.

A. Bou, D. Ho, and L. Fernando, Towards an early software estimation using log-linear regression and a multilayer perceptron model, J Syst Softw, vol.86, pp.144-160, 2013.

K. Srinivasan and D. Fisher, Machine learning approaches to estimating software development effort, IEEE Transactions on Software Engineering, vol.21, issue.2, pp.126-137, 1995.
DOI : 10.1109/32.345828

A. Heiat, Comparison of artificial neural network and regression models for estimating software development effort, Information and Software Technology, vol.44, issue.15, pp.911-922, 2002.
DOI : 10.1016/S0950-5849(02)00128-3

M. Shepperd and C. Schofield, Estimating software project effort using analogies, IEEE Transactions on Software Engineering, vol.23, issue.11, pp.736-743, 1997.
DOI : 10.1109/32.637387

C. Mair, G. Kadoda, M. Lefley, K. Phalp, C. Schofield et al., An investigation of machine learning based prediction systems, Journal of Systems and Software, vol.53, issue.1, pp.53-76, 2000.
DOI : 10.1016/S0164-1212(00)00005-4

I. F. De-barcelos-tronto, J. D. Da-silva, and N. , Sant'Anna, An investigation of artificial neural networks based prediction systems in software project management, J Syst Softw, pp.81-356, 2008.

G. R. Finnie, G. E. Wittig, and J. M. Desharnais, A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models, Journal of Systems and Software, vol.39, issue.3, pp.39-281, 1997.
DOI : 10.1016/S0164-1212(97)00055-1

Y. F. Li, M. Xie, and T. N. Goh, A study of mutual information based feature selection for case based reasoning in software cost estimation, Expert Systems with Applications, vol.36, issue.3, pp.36-5921, 2009.
DOI : 10.1016/j.eswa.2008.07.062

E. Kocaguneli, S. Member, and T. Menzies, Exploiting the Essential Assumptions of Analogy-Based Effort Estimation, IEEE Transactions on Software Engineering, vol.38, issue.2, pp.425-439, 2012.
DOI : 10.1109/TSE.2011.27

M. Azzeh, D. Neagu, and P. I. Cowling, Analogy-based software effort estimation using Fuzzy numbers, Journal of Systems and Software, vol.84, issue.2, pp.270-284, 2011.
DOI : 10.1016/j.jss.2010.09.028

E. Kocaguneli, T. Menzies, and J. W. Keung, Kernel methods for software effort estimation, Empirical Software Engineering, vol.4, issue.2, pp.18-19, 2013.
DOI : 10.1007/s10664-011-9189-1

N. H. Chiu and S. J. Huang, The adjusted analogy-based software effort estimation based on similarity distances, Journal of Systems and Software, vol.80, issue.4, pp.628-640, 2007.
DOI : 10.1016/j.jss.2006.06.006

S. J. Huang and N. H. Chiu, Optimization of analogy weights by genetic algorithm for software effort estimation, Information and Software Technology, vol.48, issue.11, pp.48-1034, 2006.
DOI : 10.1016/j.infsof.2005.12.020

J. Li and G. Ruhe, Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+, Empirical Software Engineering, vol.16, issue.3, pp.63-96, 2008.
DOI : 10.1007/s10664-007-9054-4

A. L. Oliveira, P. L. Braga, R. M. Lima, and M. L. Cornélio, GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation, Information and Software Technology, vol.52, issue.11, pp.52-1155, 2010.
DOI : 10.1016/j.infsof.2010.05.009

S. Zhang, C. Zhang, and Q. Yang, Data preparation for data mining, Applied Artificial Intelligence, vol.17, issue.5-6, pp.375-381, 2003.
DOI : 10.1080/713827180

B. Twala and M. Cartwright, Ensemble missing data techniques for software effort prediction, Intell Data Analysis, pp.299-331, 2010.

L. L. Minku and X. Yao, A principled evaluation of ensembles of learning machines for software effort estimation, Proceedings of the 7th International Conference on Predictive Models in Software Engineering, Promise '11, pp.1-10, 2011.
DOI : 10.1145/2020390.2020399

M. Azzeh, Software effort estimation based on optimized model tree, Proceedings of the 7th International Conference on Predictive Models in Software Engineering, Promise '11, 2011.
DOI : 10.1145/2020390.2020396

M. Shepperd and G. Kadoda, Comparing software prediction techniques using simulation, IEEE Transactions on Software Engineering, vol.27, issue.11, pp.1014-1022, 2001.
DOI : 10.1109/32.965341

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, issue.4, pp.34-471, 2008.
DOI : 10.1109/TSE.2008.34

K. Vinay-kumar, V. Ravi, M. Carr, and N. R. Kiran, Software development cost estimation using wavelet neural networks, Journal of Systems and Software, vol.81, issue.11, pp.81-1853, 2008.
DOI : 10.1016/j.jss.2007.12.793

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, issue.2, pp.82-241, 2009.
DOI : 10.1016/j.jss.2008.06.001

K. Strike, K. E. Emam, and N. Madhavji, Software cost estimation with incomplete data, IEEE Transactions on Software Engineering, vol.27, issue.10, pp.890-908, 2001.
DOI : 10.1109/32.962560

J. Van-hulse and T. M. Khoshgoftaar, A comprehensive empirical evaluation of missing value imputation in noisy software measurement data, Journal of Systems and Software, vol.81, issue.5, pp.81-691, 2008.
DOI : 10.1016/j.jss.2007.07.043

I. Myrtveit, E. Stensrud, and U. H. Olsson, Analyzing data sets with missing data: an empirical evaluation of imputation methods and likelihood-based methods, IEEE Transactions on Software Engineering, vol.27, issue.11, pp.27-999, 2001.
DOI : 10.1109/32.965340

P. Sentas and L. Angelis, Categorical missing data imputation for software cost estimation by multinomial logistic regression, Journal of Systems and Software, vol.79, issue.3, pp.79-404, 2006.
DOI : 10.1016/j.jss.2005.02.026

Y. Seo and D. Bae, On the value of outlier elimination on software effort estimation research, Empirical Software Engineering, vol.81, issue.5, pp.659-698, 2013.
DOI : 10.1007/s10664-012-9207-y

M. Tsunoda, T. Kakimoto, A. Monden, and K. Matsumoto, An empirical evaluation of outlier deletion methods for analogy-based cost estimation, Proceedings of the 7th International Conference on Predictive Models in Software Engineering, Promise '11, pp.1-10, 2011.
DOI : 10.1145/2020390.2020407

J. Keung, E. Kocaguneli, and T. Menzies, Finding conclusion stability for selecting the best effort predictor in software effort estimation, Automated Software Engineering, vol.4, issue.2, pp.543-567, 2013.
DOI : 10.1007/s10515-012-0108-5

M. Azzeh, D. Neagu, and P. I. Cowling, Fuzzy grey relational analysis for software effort estimation, Empirical Software Engineering, vol.90, issue.1, pp.60-90, 2009.
DOI : 10.1007/s10664-009-9113-0

N. Mittas and L. , LSEbA: least squares regression and estimation by analogy in a semi-parametric model for software cost estimation, Empirical Software Engineering, vol.27, issue.10, pp.523-555, 2010.
DOI : 10.1007/s10664-010-9128-6

J. Li, G. Ruhe, A. Al-emran, and M. M. Richter, A flexible method for software effort estimation by analogy, Empirical Software Engineering, vol.4, issue.2, pp.12-65, 2007.
DOI : 10.1007/s10664-006-7552-4

Q. Song and M. Shepperd, A new imputation method for small software project data sets, Journal of Systems and Software, vol.80, issue.1, pp.51-62, 2007.
DOI : 10.1016/j.jss.2006.05.003

L. Angelis and I. Stamelos, A simulation tool for efficient analogy based cost estimation, Empirical Software Engineering, vol.5, issue.1, pp.35-68, 2000.
DOI : 10.1023/A:1009897800559

E. Kocaguneli, S. Member, and T. Menzies, On the Value of Ensemble Effort Estimation, IEEE Transactions on Software Engineering, vol.38, issue.6, pp.1403-1416, 2012.
DOI : 10.1109/TSE.2011.111

A. Corazza, S. D. Martino, F. Ferrucci, C. Gravino, F. Sarro et al., How effective is Tabu search to configure support vector regression for effort estimation?, Proceedings of the 6th International Conference on Predictive Models in Software Engineering, PROMISE '10, pp.1-10, 2010.
DOI : 10.1145/1868328.1868335

A. Corazza, S. D. Martino, F. Ferrucci, C. Gravino, and E. Mendes, Applying support vector regression for web effort estimation using a cross-company dataset, 2009 3rd International Symposium on Empirical Software Engineering and Measurement, pp.191-202, 2009.
DOI : 10.1109/ESEM.2009.5315991

N. Mittas, M. Athanasiades, and L. , Angelis, Improving analogy-based software cost estimation by a resampling method, Inf Softw Technol, pp.50-221, 2008.

J. Li, A. Al-emran, and G. Ruhe, Impact Analysis of Missing Values on the Prediction Accuracy of Analogy-based Software Effort Estimation Method AQUA, First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), pp.126-135, 2007.
DOI : 10.1109/ESEM.2007.10

E. Mendes, A Comparison of Techniques for Web Effort Estimation, First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), pp.334-343, 2007.
DOI : 10.1109/ESEM.2007.14

J. Keung, Empirical evaluation of analogy-x for software cost estimation, Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement, ESEM '08
DOI : 10.1145/1414004.1414057

F. Ferrucci, E. Mendes, and F. Sarro, Web effort estimation, Proceedings of the 8th International Conference on Predictive Models in Software Engineering, PROMISE '12, pp.2012-2041
DOI : 10.1145/2365324.2365330

P. C. Pendharkar, G. H. Subramanian, and J. A. Rodger, A probabilistic model for predicting software development effort, IEEE Trans Softw Eng, pp.31-615, 2005.

M. Auer, A. Trendowicz, B. Graser, E. Haunschmid, and S. Biffl, Optimal project feature weights in analogy-based cost estimation: improvement and limitations, IEEE Transactions on Software Engineering, vol.32, issue.2, pp.32-83, 2006.
DOI : 10.1109/TSE.2006.1599418

T. Menzies, A. Butcher, D. Cok, A. Marcus, L. Layman et al., Local versus Global Lessons for Defect Prediction and Effort Estimation, IEEE Transactions on Software Engineering, vol.39, issue.6, pp.39-822, 2013.
DOI : 10.1109/TSE.2012.83

A. Brady and T. Menzies, Case-based reasoning vs parametric models for software quality optimization, Proceedings of the 6th International Conference on Predictive Models in Software Engineering, PROMISE '10, pp.1-10, 2010.
DOI : 10.1145/1868328.1868333

E. Kocaguneli and T. Menzies, How to Find Relevant Data for Effort Estimation?, 2011 International Symposium on Empirical Software Engineering and Measurement, pp.255-264, 2011.
DOI : 10.1109/ESEM.2011.34

Y. F. Li, M. Xie, and T. N. Goh, A study of the non-linear adjustment for analogy based software cost estimation, Empirical Software Engineering, vol.4, issue.2, pp.14-603, 2009.
DOI : 10.1007/s10664-008-9104-6

R. Borges and T. Menzies, Learning to change projects, Proceedings of the 8th International Conference on Predictive Models in Software Engineering, PROMISE '12, pp.11-18, 2012.
DOI : 10.1145/2365324.2365328

E. Kocaguneli, T. Menzies, J. Hihn, and B. H. Kang, Size doesn't matter? On the value of software size features for effort estimation, the 8th International Conference on Predictive Models in Software Engineering (PROMISE'12), pp.2012-89

Q. Liu, W. Z. Qin, R. Mintram, and M. Ross, Evaluation of preliminary data analysis framework in software cost estimation based on ISBSG R9 Data, Software Quality Journal, vol.39, issue.7, pp.16-411, 2008.
DOI : 10.1007/s11219-007-9041-4

V. K. Bardsiri, D. N. Jawawi, S. Z. Hashim, and E. Khatibi, A PSO-based model to increase the accuracy of software development effort estimation, Software Quality Journal, vol.4, issue.2, pp.501-526, 2013.
DOI : 10.1007/s11219-012-9183-x

A. Corazza, S. Martino, F. Ferrucci, C. Gravino, F. Sarro et al., Using tabu search to configure support vector regression for effort estimation, Empirical Software Engineering, vol.14, issue.3, pp.18-506, 2013.
DOI : 10.1007/s10664-011-9187-3

J. Li and G. Ruhe, Decision Support Analysis for Software Effort Estimation by Analogy, Third International Workshop on Predictor Models in Software Engineering (PROMISE'07: ICSE Workshops 2007), pp.6-16, 2007.
DOI : 10.1109/PROMISE.2007.5

L. L. Minku and X. Yao, Ensembles and locality: Insight on improving software effort estimation, Information and Software Technology, vol.55, issue.8, pp.55-1512, 2013.
DOI : 10.1016/j.infsof.2012.09.012

M. Azzeh, A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation, Empirical Software Engineering, vol.4, issue.2, pp.90-127, 2012.
DOI : 10.1007/s10664-011-9176-6

C. Hsu and C. Huang, Comparison of weighted grey relational analysis for software effort estimation, Software Quality Journal, vol.4, issue.2, pp.165-200, 2010.
DOI : 10.1007/s11219-010-9110-y

E. Kocaguneli, T. Menzies, J. Keung, D. Cok, and R. Madachy, Active learning and effort estimation: Finding the essential content of software effort estimation data, IEEE Transactions on Software Engineering, vol.39, issue.8, pp.39-1040, 2013.
DOI : 10.1109/TSE.2012.88

N. Mittas and L. , Angelis, Ranking and clustering software cost estimation models through a multiple comparisons algorithm, IEEE Trans Softw Eng, pp.39-2013

C. Lopez-martin, C. Isaza, and A. Chavoya, Software development effort prediction of industrial projects applying a general regression neural network, Empirical Software Engineering, vol.81, issue.3, pp.738-756, 2011.
DOI : 10.1007/s10664-011-9192-6

A. Bakir, B. Turhan, and A. B. Bener, A new perspective on data homogeneity in software cost estimation: a study in the embedded systems domain, Software Quality Journal, vol.16, issue.1, pp.18-57, 2009.
DOI : 10.1007/s11219-009-9081-z

A. Bakir, B. Turhan, and A. Bener, A comparative study for estimating software development effort intervals, Software Quality Journal, vol.2, issue.13, pp.537-552, 2011.
DOI : 10.1007/s11219-010-9112-9

N. Ramasubbu and R. K. Balan, Overcoming the challenges in cost estimation for distributed software projects, 2012 34th International Conference on Software Engineering (ICSE), pp.91-101, 2012.
DOI : 10.1109/ICSE.2012.6227203

M. V. Kosti, N. Mittas, and L. Angelis, Alternative methods using similarities in software effort estimation, Proceedings of the 8th International Conference on Predictive Models in Software Engineering, PROMISE '12, pp.59-68, 2012.
DOI : 10.1145/2365324.2365333

D. Rodríguez, M. A. Sicilia, E. García, and R. Harrison, Empirical findings on team size and productivity in software development, Journal of Systems and Software, vol.85, issue.3, pp.562-570, 2012.
DOI : 10.1016/j.jss.2011.09.009

Q. Song, M. Shepperd, and M. Cartwright, A Short Note on Safest Default Missingness Mechanism Assumptions, Empirical Software Engineering, vol.27, issue.10, pp.235-243, 2005.
DOI : 10.1007/s10664-004-6193-8

J. Moses and M. Farrow, Assessing Variation in Development Effort Consistency Using a Data Source with Missing Data, Software Quality Journal, vol.27, issue.10, pp.71-89, 2005.
DOI : 10.1007/s11219-004-5261-z

J. Van-hulse and T. M. Khoshgoftaar, Incomplete-case nearest neighbor imputation in software measurement data, Information Sciences, vol.259, pp.596-610, 2014.
DOI : 10.1016/j.ins.2010.12.017

S. Huang and N. Chiu, Optimization of analogy weights by genetic algorithm for software effort estimation, Information and Software Technology, vol.48, issue.11, pp.48-1034, 2006.
DOI : 10.1016/j.infsof.2005.12.020

Z. Chen, T. Menzies, D. Port, and B. Boehm, Feature subset selection can improve software cost estimation accuracy, the 2005 International Conference on Predictor Models in Software Engineering (PROMISE'05), pp.1-6, 2005.

S. Das, Filters, wrappers and a boosting-based hybrid for feature selection, the 18th International Conference on Machine Learning (ICML'01), pp.74-81, 2001.

E. Mendes, I. Watson, C. Triggs, N. Mosley, and S. , A comparative study of cost estimation models for web hypermedia applications, Empirical Software Engineering, vol.8, issue.2, pp.163-196, 2003.
DOI : 10.1023/A:1023062629183

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J Mach Learn Res, vol.3, pp.1157-1182, 2003.

H. Peng, F. Long, and C. Ding, Feature selection based on mutual information: Criteria of maxdependency , max-relevance, and min-redundancy, IEEE Trans Pattern Anal Mach Intell, pp.27-1226, 2005.

P. L. Braga, A. L. Oliveira, and S. R. Meira, A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation, Proceedings of the 2008 ACM symposium on Applied computing , SAC '08, pp.1788-1792, 2008.
DOI : 10.1145/1363686.1364116

C. Kirsopp and M. Shepperd, Case and Feature Subset Selection in Case-Based Software Project Effort Prediction, Research and Development in Intelligent Systems XIX, pp.61-74, 2003.
DOI : 10.1007/978-1-4471-0651-7_5

M. Fernández-diego and F. , González-Ladrón-de-Guevara, Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review

N. Mittas and L. , Visual comparison of software cost estimation models by regression error characteristic analysis, Journal of Systems and Software, vol.83, issue.4, pp.621-637, 2010.
DOI : 10.1016/j.jss.2009.10.044

N. Mittas and L. , A permutation test based on regression error characteristic curves for software cost estimation models, Empirical Software Engineering, vol.39, issue.8, pp.34-61, 2011.
DOI : 10.1007/s10664-011-9177-5

Y. Seo, D. Bae, and R. Jeffery, AREION: Software effort estimation based on multiple regressions with adaptive recursive data partitioning, Information and Software Technology, vol.55, issue.10, pp.55-1710, 2013.
DOI : 10.1016/j.infsof.2013.03.007

J. M. Desharnais, Analyse statistique de la productivitie des projets informatique a partie de la technique des point des foncti\ on, 1989.

M. Jørgensen and M. Shepperd, A Systematic Review of Software Development Cost Estimation Studies, IEEE Transactions on Software Engineering, vol.33, issue.1, pp.33-53, 2007.
DOI : 10.1109/TSE.2007.256943

F. Walkerden and R. Jeffery, Empirical study of analogy-based software effort estimation, Empirical Software Engineering, vol.4, issue.2, pp.135-158, 1999.
DOI : 10.1023/A:1009872202035

A. R. Gray and S. G. Macdonell, A comparison of techniques for developing predictive models of software metrics, Information and Software Technology, vol.39, issue.6, pp.39-425, 1997.
DOI : 10.1016/S0950-5849(96)00006-7

Y. F. Li, M. Xie, and T. N. Goh, Adaptive ridge regression system for software cost estimating on multi-collinear datasets, Journal of Systems and Software, vol.83, issue.11, pp.2332-2343, 2010.
DOI : 10.1016/j.jss.2010.07.032

T. Foss, E. Stensrud, B. Kitchenham, and I. Myrtveit, A simulation study of the model evaluation criterion mmre, IEEE Transactions on Software Engineering, vol.29, issue.11, pp.29-985, 2003.
DOI : 10.1109/TSE.2003.1245300

I. Myrtveit, E. Stensrud, and M. Shepperd, Reliability and validity in comparative studies of software prediction models, IEEE Transactions on Software Engineering, vol.31, issue.5, pp.31-380, 2005.
DOI : 10.1109/TSE.2005.58

M. Ochodek, J. Nawrocki, and K. Kwarciak, Simplifying effort estimation based on Use Case Points, Information and Software Technology, vol.53, issue.3, pp.53-200, 2011.
DOI : 10.1016/j.infsof.2010.10.005

M. A. Ahmed, I. Ahmad, and J. S. Alghamdi, Probabilistic size proxy for software effort prediction: A framework, Information and Software Technology, vol.55, issue.2, pp.55-241, 2013.
DOI : 10.1016/j.infsof.2012.08.001

B. Kitchenham and E. Mendes, Why comparative effort prediction studies may be invalid, Proceedings of the 5th International Conference on Predictor Models in Software Engineering, PROMISE '09, p.4, 2009.
DOI : 10.1145/1540438.1540444

E. Mendes and C. Lokan, Replicating studies on cross- vs single-company effort models using the ISBSG Database, Empirical Software Engineering, vol.45, issue.6, pp.3-37, 2008.
DOI : 10.1007/s10664-007-9045-5

E. Mendes, S. D. Martino, F. Ferrucci, and C. Gravino, Cross-company vs. single-company web effort models using the Tukutuku database: An extended study, Journal of Systems and Software, vol.81, issue.5, pp.81-673, 2008.
DOI : 10.1016/j.jss.2007.07.044

M. Shepperd and S. Macdonell, Evaluating prediction systems in software project estimation, Information and Software Technology, vol.54, issue.8, pp.820-827, 2012.
DOI : 10.1016/j.infsof.2011.12.008