Software Effort Estimation Models Using Radial Basis Function Network
Software Effort Estimation is the process of estimating the effort required to develop software. By estimating the effort, the cost and schedule required to estimate the software can be determined. Accurate Estimate helps the developer to allocate the resource accordingly in order to avoid cost overrun and schedule overrun. Several methods are available in order to estimate the effort among which soft computing based method plays a prominent role. Software cost estimation deals with lot of uncertainty among all soft computing methods neural network is good in handling uncertainty. In this paper Radial Basis Function Network is compared with the back propagation network and the results are validated using six data sets and it is found that RBFN is best suitable to estimate the effort. The Results are validated using two tests the error test and the statistical test.
[1] Martin Shepperdand Michelle Cartwright,Predicting with Sparse Data, IEEE Transactions on Software Engineering, Vol. 27, n. 11, pp. 987-998, 2001.
[2] IngunnMyrtviet, Erik Stensrudand Martin Shepperd, Reliability and Validity in Comparative Studies of Software Prediction Models, IEEE Transactions on Software Engineering, Vol. 35, n. 5, pp. 380-391, 2005.
[3] Steve McConnell, Rapid Development: Taming Wild Software Schedules, Microsoft Press, 1996.
[4] Magne Jorgensen, The Impact of Irreverent or Misleading Information on Software Development Effort Estimate, IEEE Transaction on Software Engineering, Vol.37, n.5, pp. 695-707, 2011.
[5] Magne Jorgensen and Martin Shepperd, A Systematic Review of Software Development Cost Estimation Studies, IEEE Transaction on Software Engineering, Vol.33, n.1, 2007.
[6] Venkatachalam, Software Cost Estimation Using Artificial Neural Networks, International Joint Conference on Neural Networks, Nogoya, IEEE, 1993.
[7] MiyoungShin and AmritL.Goel, Emprical Modelling in Software Engineering Using Radial Basis Functions, IEEE Transaction on Software Engineering, Vol.26, n.6, 2000
[8] Abbas Heiat, Comparison of Artificial Neural Network and Regression Models for Estimating Software Development Effort, Information and Software Technology, vol.44, pp.911-922, 2002.
[9] M.Ochodek, J.Nowrocki, K.Kwarciak, Simplifying Effort Estimation Based On Use Case Points, Information and Software Technology, vol.53, no.3,pp.200-213, 2011.
[10] Dr.S.N.Sivanandam, Dr.S.N.Deepa, Principles of softComputing(Wiley - India, 2004).
[11] Broomhead.D.S, D.Lowe, Radial Basis Function, Multivariable Function Interpolation and Adaptive Networks, Royal Signals and Radar Establishment, Memorandum. 4148, 1988
[12] Yue Wu, Huiwang, Biaobiaozhang, K.L.Du, Using Radial Basis Function Networks for Function Approximation and Classification, ISRN Applied Mathematics, pp.1-34, Vol.2012
[13] Sathyananda Reddy, KVSVN Raju, "Improving the Accuracy ofEffort Estimation through Fuzzy Set Combination of Size and Cost Drivers,” WSEAS Transaction on Computers,” Vol.8, no.6, PP.926-936, June 2009.
[14] Barry Boehm, "COCOMO II: Model Definition Manuel. Version 2.1,”Center for Software Engineering, USC, 2000.
[15] 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.
[16] E.Praynlin, P.Latha, "Performance Analysis of Software Effort Estimation Models Using Neural Network”, International Journal of Information Technology and Computer Science, PP. 101-107, August 2013.
[1] Martin Shepperdand Michelle Cartwright,Predicting with Sparse Data, IEEE Transactions on Software Engineering, Vol. 27, n. 11, pp. 987-998, 2001.
[2] IngunnMyrtviet, Erik Stensrudand Martin Shepperd, Reliability and Validity in Comparative Studies of Software Prediction Models, IEEE Transactions on Software Engineering, Vol. 35, n. 5, pp. 380-391, 2005.
[3] Steve McConnell, Rapid Development: Taming Wild Software Schedules, Microsoft Press, 1996.
[4] Magne Jorgensen, The Impact of Irreverent or Misleading Information on Software Development Effort Estimate, IEEE Transaction on Software Engineering, Vol.37, n.5, pp. 695-707, 2011.
[5] Magne Jorgensen and Martin Shepperd, A Systematic Review of Software Development Cost Estimation Studies, IEEE Transaction on Software Engineering, Vol.33, n.1, 2007.
[6] Venkatachalam, Software Cost Estimation Using Artificial Neural Networks, International Joint Conference on Neural Networks, Nogoya, IEEE, 1993.
[7] MiyoungShin and AmritL.Goel, Emprical Modelling in Software Engineering Using Radial Basis Functions, IEEE Transaction on Software Engineering, Vol.26, n.6, 2000
[8] Abbas Heiat, Comparison of Artificial Neural Network and Regression Models for Estimating Software Development Effort, Information and Software Technology, vol.44, pp.911-922, 2002.
[9] M.Ochodek, J.Nowrocki, K.Kwarciak, Simplifying Effort Estimation Based On Use Case Points, Information and Software Technology, vol.53, no.3,pp.200-213, 2011.
[10] Dr.S.N.Sivanandam, Dr.S.N.Deepa, Principles of softComputing(Wiley - India, 2004).
[11] Broomhead.D.S, D.Lowe, Radial Basis Function, Multivariable Function Interpolation and Adaptive Networks, Royal Signals and Radar Establishment, Memorandum. 4148, 1988
[12] Yue Wu, Huiwang, Biaobiaozhang, K.L.Du, Using Radial Basis Function Networks for Function Approximation and Classification, ISRN Applied Mathematics, pp.1-34, Vol.2012
[13] Sathyananda Reddy, KVSVN Raju, "Improving the Accuracy ofEffort Estimation through Fuzzy Set Combination of Size and Cost Drivers,” WSEAS Transaction on Computers,” Vol.8, no.6, PP.926-936, June 2009.
[14] Barry Boehm, "COCOMO II: Model Definition Manuel. Version 2.1,”Center for Software Engineering, USC, 2000.
[15] 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.
[16] E.Praynlin, P.Latha, "Performance Analysis of Software Effort Estimation Models Using Neural Network”, International Journal of Information Technology and Computer Science, PP. 101-107, August 2013.
@article{"International Journal of Information, Control and Computer Sciences:66824", author = "E. Praynlin and P. Latha", title = "Software Effort Estimation Models Using Radial Basis Function Network", abstract = "Software Effort Estimation is the process of estimating the effort required to develop software. By estimating the effort, the cost and schedule required to estimate the software can be determined. Accurate Estimate helps the developer to allocate the resource accordingly in order to avoid cost overrun and schedule overrun. Several methods are available in order to estimate the effort among which soft computing based method plays a prominent role. Software cost estimation deals with lot of uncertainty among all soft computing methods neural network is good in handling uncertainty. In this paper Radial Basis Function Network is compared with the back propagation network and the results are validated using six data sets and it is found that RBFN is best suitable to estimate the effort. The Results are validated using two tests the error test and the statistical test.
", keywords = "Software cost estimation, Radial Basis Function Network (RBFN), Back propagation function network, Mean Magnitude of Relative Error (MMRE). ", volume = "8", number = "1", pages = "265-6", }
