Performance Analysis of Software Effort Estimation Models Using Neural Networks
Автор: E.Praynlin, P.Latha
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 9 Vol. 5, 2013 года.
Бесплатный доступ
Software Effort estimation involves the estimation of effort required to develop software. Cost overrun, schedule overrun occur in the software development due to the wrong estimate made during the initial stage of software development. Proper estimation is very essential for successful completion of software development. Lot of estimation techniques available to estimate the effort in which neural network based estimation technique play a prominent role. Back propagation Network is the most widely used architecture. ELMAN neural network a recurrent type network can be used on par with Back propagation Network. For a good predictor system the difference between estimated effort and actual effort should be as low as possible. Data from historic project of NASA is used for training and testing. The experimental Results confirm that Back propagation algorithm is efficient than Elman neural network.
Back Propagation Network (BPN), ELMAN Network, Mean Magnitude of Relative Error (MMRE)
Короткий адрес: https://sciup.org/15011970
IDR: 15011970
Список литературы Performance Analysis of Software Effort Estimation Models Using Neural Networks
- M. Jorgenson, “Forecasting of software development work effort: Evidence on expert judgment and formal models,” International Journal of forecasting 23pp.449–462, 2007.
- Martin Shepperd and Chris Schofield, “Estimating Software Project Effort Using Analogies,” IEEE Transactions On Software Engineering, Vol. 23, No. 12, PP.736-743 November 1997
- ImanAttarzadeh, Siew Hock Ow, “A Novel Algorithmic Cost Estimation Model Based on Soft Computing Technique,” Journal of computer science,pp. 117-125, 2010.
- VahidKhatibi B., Dayang N.A. Jawawi, SitiZaitonMohdHashim and ElhamKhatibi, “A New Fuzzy Clustering Based Method to Increase the Accuracy of Software Development Effort Estimation” World Applied Sciences Journal, 2011, 1265-1275.
- Magne Jorgenson and Martin Shepperd, “A Systematic Review of Software Development Cost Estimation Studies”, IEEE Transactions on software engineering, Vol.33, No.1,pp.33-53, January 2007.
- Abbas Heiat, “Comparison of artificial neural network and regression models for estimating software development effort” Information and Software Technology, 2002, 911–922.
- Rudy Setiono, KarelDejaeger, WouterVerbeke, David Martens, and Bart Baesens “Software Effort prediction using Regression Rule Extraction from Neural Networks”22nd International Conference on Tools with Artificial Intelligence,2010,pp.45-52.
- G.Witting, and G. Finnie, “Using Artificial Neural Networks and Function Points to Estimate 4GL Software Development Effort”, Journal of Information Systems,1994, vol.1, no.2, pp.87-94.
- G.Witting, and G.Finnie, “Estimating software development effort with connectionist models,” Inf. Software Technology, 1997,vol.39, pp.369-476.
- N. Karunanitthi, D.Whitely, and Y.K.Malaiya, “Using Neural Networks in Reliability Prediction,” IEEE Software, 1992.vol.9, no.4, pp.53-59.
- B. Samson, D. Ellison, and P. Dugard, “Software Cost Estimation Using Albus Perceptron (CMAC),”Information and Software Technology, 1997, vol.39, pp.55-60.
- K. Srinivazan, and D. Fisher, “Machine Learning Approaches to Estimating Software Development Effort” IEEE Transactions on Software Engineering, February1995, vol.21,no.2, pp. 126-137.
- Iris Fabiana de BarcelosTronto, Jose´ Demı´sioSimo˜ es da Silva, NilsonSant’Anna,”An investigation of artificial neural networks based prediction systems in software project management”. The journal of system and software, June 2007, pp.356-367.
- Ricardo de A. Araújo, Adriano L.I. Oliveira , Sergio Soares, “A shift-invariant morphological system for software development cost estimation “Expert Systems with Applications,2011,4162-4168.
- Yan-Fu Li, Min Xie, Thong-NgeeGoh, “Adaptive ridge regression system for software cost estimating on multi-collinear datasets” The Journal of Systems and Software, 2010, 2332–2343.
- Boehm B. W. “Software Engineering Economics”, Englewood Cliffs, NJ, Prentice-Hall, 1981.
- Dr.S.N.Sivanandam, Dr.S.N.Deepa,”Principles of soft computing” 2nd edition, Wiley-India, ISBN: 978-81-265-2741-0.
- Barry Boehm, COCOMO II: Model Definition Manuel. Version 2.1, Center for Software Engineering, USC, 2000.
- Donald J. Reifer, Barry W. Boehm and Sunithachulani, “The Rosetta stone: Making COCOMO 81 Estimates work with COCOMO II”, CROSSTALK The Journal of Defense Software Engineering, pp 11 – 15, Feb.1999.
