Alternative Methods to Rank the Impact of Object Oriented Metrics in Fault Prediction Modeling using Neural Networks
The aim of this paper is to rank the impact of Object
Oriented(OO) metrics in fault prediction modeling using Artificial
Neural Networks(ANNs). Past studies on empirical validation of
object oriented metrics as fault predictors using ANNs have focused
on the predictive quality of neural networks versus standard
statistical techniques. In this empirical study we turn our attention to
the capability of ANNs in ranking the impact of these explanatory
metrics on fault proneness. In ANNs data analysis approach, there is
no clear method of ranking the impact of individual metrics. Five
ANN based techniques are studied which rank object oriented
metrics in predicting fault proneness of classes. These techniques are
i) overall connection weights method ii) Garson-s method iii) The
partial derivatives methods iv) The Input Perturb method v) the
classical stepwise methods. We develop and evaluate different
prediction models based on the ranking of the metrics by the
individual techniques. The models based on overall connection
weights and partial derivatives methods have been found to be most
accurate.
[1] S. R. Chidamber and C. F. Kemerer, "A Metrics Suite for Object
Oriented Design," IEEE Transactions on Software Engineering, vol. 20,
pp 476-493,1994
[2] V.R. Basili, et al,"A Validation of Object-Oriented Design Metrics as
Quality Indicators," IEEE Transactions on Software Engineering, vol.
22, pp 751-761,1996
[3] L.C. Briand , Jurgen Wust, "Modeling Development Effort in Object
Oriented Systems using Design Properties," IEEE Transactions on
Software Engineering, vol. 27, no. 11, 2001
[4] T.M. Khoshgoftaar, E.B.Allen, J.P. Hudephol and S.J. Aud," E.B.Allen,
J.P. Hudephol and S.J. Aud," ," Application of neural networks to
quality modeling of a very large telecommunication system", IEEE
Transactions on Neural Networks, vol.8,pp. 902-909,1997
[5] M. M. T. Thwin and T.-S. Quah,, "Application of Neural Networks for
predicting Software Development faults using Object Oriented Design
Metrics", Proceedings of the 9th International Conference on Neural
Information Processing, November 2002, pp. 2312 - 2316.
[6] M. M. T. Thwin and T.-S. Quah,,, "Prediction of Software Readiness
using Neural Networks," ICITA2002, ISBN:1-86467-114-9
[7] Tibor Gyimothy, Rudolf Fernec, and Istvan Siket , " Empirical Validaion
of Object -Oriented Metrics on Open Source Software for Fault
Prediction" , IEEE Transactions on Software Engineering, vol. 31, no.
10, October 2005
[8] K.K. Aggarwal, Y. Singh. P. Chandra,M.Puri, "Evaluation of various
training Algorithms for Sofware Engineering Applications," ACM
SIGSOFT Software Engineering Notes ,vol 30, no.4 July 2005.
[9] Dietrich Schusche, Chun Nan Hsu, Hung-Ju Huang , "A weight
analysis based wrapper approach to Neural Nets feature subset
selection," IEEE Transactions on Systems, Man and Cybernetics,
vol.32,pp. 207-21,2002
[10] G.D. Garson, "Interpreting Neural Network Connection Weights," AI
Expert 6, pp. 47-51, 1991.
[11] D.W. Ruck, S.K Rogers, M. Kabrisky, "Feature Selection using Multi
Layer Perceptrons," in Journal of Neural Network Computing, vol 2, no
. 2 , pp. 40-48 ,1990.
[12] S. Haykins, "A Comprehensive Foundation on Neural Networks ,"
Prentice Hall, 1999
[13] Khaled El Emam, "A Methodology for Validating Software Product
Metrics," National Research Council Canada, Institute for Information
Technology, ERB-1076.
[1] S. R. Chidamber and C. F. Kemerer, "A Metrics Suite for Object
Oriented Design," IEEE Transactions on Software Engineering, vol. 20,
pp 476-493,1994
[2] V.R. Basili, et al,"A Validation of Object-Oriented Design Metrics as
Quality Indicators," IEEE Transactions on Software Engineering, vol.
22, pp 751-761,1996
[3] L.C. Briand , Jurgen Wust, "Modeling Development Effort in Object
Oriented Systems using Design Properties," IEEE Transactions on
Software Engineering, vol. 27, no. 11, 2001
[4] T.M. Khoshgoftaar, E.B.Allen, J.P. Hudephol and S.J. Aud," E.B.Allen,
J.P. Hudephol and S.J. Aud," ," Application of neural networks to
quality modeling of a very large telecommunication system", IEEE
Transactions on Neural Networks, vol.8,pp. 902-909,1997
[5] M. M. T. Thwin and T.-S. Quah,, "Application of Neural Networks for
predicting Software Development faults using Object Oriented Design
Metrics", Proceedings of the 9th International Conference on Neural
Information Processing, November 2002, pp. 2312 - 2316.
[6] M. M. T. Thwin and T.-S. Quah,,, "Prediction of Software Readiness
using Neural Networks," ICITA2002, ISBN:1-86467-114-9
[7] Tibor Gyimothy, Rudolf Fernec, and Istvan Siket , " Empirical Validaion
of Object -Oriented Metrics on Open Source Software for Fault
Prediction" , IEEE Transactions on Software Engineering, vol. 31, no.
10, October 2005
[8] K.K. Aggarwal, Y. Singh. P. Chandra,M.Puri, "Evaluation of various
training Algorithms for Sofware Engineering Applications," ACM
SIGSOFT Software Engineering Notes ,vol 30, no.4 July 2005.
[9] Dietrich Schusche, Chun Nan Hsu, Hung-Ju Huang , "A weight
analysis based wrapper approach to Neural Nets feature subset
selection," IEEE Transactions on Systems, Man and Cybernetics,
vol.32,pp. 207-21,2002
[10] G.D. Garson, "Interpreting Neural Network Connection Weights," AI
Expert 6, pp. 47-51, 1991.
[11] D.W. Ruck, S.K Rogers, M. Kabrisky, "Feature Selection using Multi
Layer Perceptrons," in Journal of Neural Network Computing, vol 2, no
. 2 , pp. 40-48 ,1990.
[12] S. Haykins, "A Comprehensive Foundation on Neural Networks ,"
Prentice Hall, 1999
[13] Khaled El Emam, "A Methodology for Validating Software Product
Metrics," National Research Council Canada, Institute for Information
Technology, ERB-1076.
@article{"International Journal of Information, Control and Computer Sciences:58468", author = "Kamaldeep Kaur and Arvinder Kaur and Ruchika Malhotra", title = "Alternative Methods to Rank the Impact of Object Oriented Metrics in Fault Prediction Modeling using Neural Networks", abstract = "The aim of this paper is to rank the impact of Object
Oriented(OO) metrics in fault prediction modeling using Artificial
Neural Networks(ANNs). Past studies on empirical validation of
object oriented metrics as fault predictors using ANNs have focused
on the predictive quality of neural networks versus standard
statistical techniques. In this empirical study we turn our attention to
the capability of ANNs in ranking the impact of these explanatory
metrics on fault proneness. In ANNs data analysis approach, there is
no clear method of ranking the impact of individual metrics. Five
ANN based techniques are studied which rank object oriented
metrics in predicting fault proneness of classes. These techniques are
i) overall connection weights method ii) Garson-s method iii) The
partial derivatives methods iv) The Input Perturb method v) the
classical stepwise methods. We develop and evaluate different
prediction models based on the ranking of the metrics by the
individual techniques. The models based on overall connection
weights and partial derivatives methods have been found to be most
accurate.", keywords = "Artificial Neural Networks (ANNS),
Backpropagation, Fault Prediction Modeling.", volume = "2", number = "7", pages = "2468-6", }