Software cost estimation using computational intelligence techniques

This paper presents computational intelligence techniques for software cost estimation. We proposed a new recurrent architecture for Genetic Programming (GP) in the process. Three linear ensembles based on (i) arithmetic mean (ii) geometric mean and (iii) harmonic mean are implemented. We also performed GP based feature selection. The efficacy of these techniques viz Multiple Linear Regression, Polynomial Regression, Support Vector Regression, Classification and Regression Tree, Multivariate Adaptive Regression Splines, Multilayer FeedForward Neural Network, Radial Basis Function Neural Network, Counter Propagation Neural Network, Dynamic Evolving Neuro-Fuzzy Inference System, Tree Net, Group Method of Data Handling and Genetic Programming has been tested on the International Software Benchmarking Standards Group (ISBSG) release 10 dataset. Ten-fold cross validation is performed throughout the study. The results obtained from our experiments indicate that new recurrent architecture for Genetic Programming outperformed all the other techniques.

[1]  B. Kermanshahi,et al.  Feedforward versus recurrent neural networks for forecasting monthly japanese yen exchange rates , 1996 .

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

[3]  Barry W. Boehm,et al.  Software Engineering Economics , 1993, IEEE Transactions on Software Engineering.

[4]  R. Hecht-Nielsen.  Counterpropagation networks. , 1987, Applied optics.

[5]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[6]  Taghi M. Khoshgoftaar,et al.  Identification of fuzzy models of software cost estimation , 2004, Fuzzy Sets Syst..

[7]  Jing Ren,et al.  A neuro-fuzzy model for software cost estimation , 2003, Third International Conference on Quality Software, 2003. Proceedings..

[8]  K. K. Aggarwal,et al.  An expert committee model to estimate lines of code , 2005, SOEN.

[9]  Martin J. Shepperd,et al.  Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets , 2003, GECCO.

[10]  Thong Ngee Goh,et al.  A study of project selection and feature weighting for analogy based software cost estimation , 2009, J. Syst. Softw..

[11]  Marjana Novič,et al.  Counter-propagation neural networks in Matlab , 2008 .

[12]  Joseph M. Mellichamp,et al.  Software Development Cost Estimation Using Function Points , 1994, IEEE Trans. Software Eng..

[13]  Riccardo Poli,et al.  A Field Guide to Genetic Programming , 2008 .

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

[15]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[16]  J. Friedman.  Stochastic gradient boosting , 2002 .

[17]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[18]  Barbara A. Kitchenham,et al.  Modeling Software Bidding Risks , 2003, IEEE Trans. Software Eng..

[19]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[20]  Andrew R. Gray,et al.  A simulation-based comparison of empirical modeling techniques for software metric models of development effort , 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

[21]  June M. Verner,et al.  A Software Size Model , 1992, IEEE Trans. Software Eng..

[22]  Dipti Srinivasan,et al.  Energy demand prediction using GMDH networks , 2008, Neurocomputing.

[23]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[24]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[25]  Taghi M. Khoshgoftaar,et al.  Can neural networks be easily interpreted in software cost estimation? , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[26]  Vadlamani Ravi,et al.  Software development cost estimation using wavelet neural networks , 2008, J. Syst. Softw..

[27]  Barbara A. Kitchenham,et al.  A Simulation Study of the Model Evaluation Criterion MMRE , 2003, IEEE Trans. Software Eng..

[28]  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.

[29]  Mahil Carr,et al.  Software Cost Estimation using Soft Computing Approaches , 2010 .