Assessment of voting ensemble for estimating software development effort

This paper reports and discusses the results of an assessment study, which aimed to determine the extent to which the voting ensemble model offers reliable and improved estimation accuracy over five individual models (MLP, RBF, RT, KNN and SVR) in estimating software development effort. Five datasets were used for this purpose. The results confirm that individual models are not reliable as their performance is inconsistence and unstable across different datasets. However, the ensemble model provides more reliable performance than individual models. In three out of the five datasets that were used in this study, the ensemble model outperformed the individual models. In the other two datasets, the ensemble model achieved the second best performance, which was still very competitive as there was no statistically significant difference between it and the best models in these two datasets.

[1]  Colin J Burgess,et al.  Can genetic programming improve software effort estimation? A comparative evaluation , 2001, Inf. Softw. Technol..

[2]  Cornelio Yáñez-Márquez,et al.  Predictive accuracy comparison of fuzzy models for software development effort of small programs , 2008, J. Syst. Softw..

[3]  Adriano Lorena Inácio de Oliveira,et al.  Estimation of software project effort with support vector regression , 2006, Neurocomputing.

[4]  Sun-Jen Huang,et al.  The adjusted analogy-based software effort estimation based on similarity distances , 2007, J. Syst. Softw..

[5]  Taghi M. Khoshgoftaar,et al.  Estimating software project effort by analogy based on linguistic values , 2002, Proceedings Eighth IEEE Symposium on Software Metrics.

[6]  John E. Gaffney,et al.  Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation , 1983, IEEE Transactions on Software Engineering.

[7]  Magne Jørgensen,et al.  Inconsistency of expert judgment-based estimates of software development effort , 2007, J. Syst. Softw..

[8]  Y. Miyazaki,et al.  Robust regression for developing software estimation models , 1994, J. Syst. Softw..

[9]  Parag C. Pendharkar,et al.  A Probabilistic Model for Predicting Software Development Effort , 2003, ICCSA.

[10]  Sérgio Soares,et al.  A Morphological-Rank-Linear Approach for Software Development Cost Estimation , 2009, 2009 21st IEEE International Conference on Tools with Artificial Intelligence.

[11]  Douglas Fisher,et al.  Machine Learning Approaches to Estimating Software Development Effort , 1995, IEEE Trans. Software Eng..

[12]  Jae Kyu Lee,et al.  Quasi-optimal case-selective neural network model for software effort estimation , 2001, Expert Syst. Appl..

[13]  José Demisio Simões da Silva,et al.  An investigation of artificial neural networks based prediction systems in software project management , 2008, J. Syst. Softw..

[14]  Magne Jørgensen,et al.  A review of studies on expert estimation of software development effort , 2004, J. Syst. Softw..

[15]  Ayse Basar Bener,et al.  ENNA: software effort estimation using ensemble of neural networks with associative memory , 2008, SIGSOFT '08/FSE-16.

[16]  Ekrem Kocaguneli,et al.  Combining Multiple Learners Induced on Multiple Datasets for Software Effort Prediction , 2009 .

[17]  Kaushal K. Shukla,et al.  Neuro-genetic prediction of software development effort , 2000, Inf. Softw. Technol..

[18]  Abbas Heiat,et al.  Comparison of artificial neural network and regression models for estimating software development effort , 2002, Inf. Softw. Technol..

[19]  Martin J. Shepperd,et al.  Estimating Software Project Effort Using Analogies , 1997, IEEE Trans. Software Eng..

[20]  Silvio Romero de Lemos Meira,et al.  Bagging Predictors for Estimation of Software Project Effort , 2007, 2007 International Joint Conference on Neural Networks.

[21]  B. Baskeles,et al.  Software effort estimation using machine learning methods , 2007, 2007 22nd international symposium on computer and information sciences.

[22]  Lawrence H. Putnam,et al.  A General Empirical Solution to the Macro Software Sizing and Estimating Problem , 1978, IEEE Transactions on Software Engineering.

[23]  ShinMiyoung,et al.  Empirical Data Modeling in Software Engineering Using Radial Basis Functions , 2000 .