Predicting the Impact of the Defect on the Overall Environment in Function Based Systems

There is lot of work done in prediction of the fault proneness of the software systems. But, it is the severity of the faults that is more important than number of faults existing in the developed system as the major faults matters most for a developer and those major faults needs immediate attention. In this paper, we tried to predict the level of impact of the existing faults in software systems. Neuro-Fuzzy based predictor models is applied NASA’s public domain defect dataset coded in C programming language. As Correlation-based Feature Selection (CFS) evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. So, CFS is used for the selecting the best metrics that have highly correlated with level of severity of faults. The results are compared with the prediction results of Logistic Models (LMT) that was earlier quoted as the best technique in [17]. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provide a relatively better prediction accuracy as compared to other models and hence, can be used for the modeling of the level of impact of faults in function based systems. Keywords—Software Metrics, Fuzzy, Neuro-Fuzzy, Software Faults, Accuracy, MAE, RMSE.

[1]  Khaled El Emam,et al.  Issues in Validating Object-Oriented Metrics for Early Risk Prediction , 1999 .

[2]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[3]  Norman E. Fenton,et al.  A Critique of Software Defect Prediction Models , 1999, IEEE Trans. Software Eng..

[4]  Filippo Lanubile,et al.  Comparing models for identifying fault-prone software components , 1995, SEKE.

[5]  Taghi M. Khoshgoftaar,et al.  Analyzing software quality with limited fault-proneness defect data , 2005, Ninth IEEE International Symposium on High-Assurance Systems Engineering (HASE'05).

[6]  Giovanni Denaro,et al.  Estimating software fault-proneness for tuning testing activities , 2000, Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium.

[7]  Pierfrancesco Bellini,et al.  Comparing fault-proneness estimation models , 2005, 10th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'05).

[8]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[9]  Khaled El Emam,et al.  Comparing case-based reasoning classifiers for predicting high risk software components , 2001, J. Syst. Softw..

[10]  Taghi M. Khoshgoftaar,et al.  Tree-based software quality estimation models for fault prediction , 2002, Proceedings Eighth IEEE Symposium on Software Metrics.

[11]  Taghi M. Khoshgoftaar,et al.  An application of zero-inflated Poisson regression for software fault prediction , 2001, Proceedings 12th International Symposium on Software Reliability Engineering.

[12]  Taghi M. Khoshgoftaar,et al.  Regression modelling of software quality: empirical investigation☆ , 1990 .

[13]  Taghi M. Khoshgoftaar,et al.  The Detection of Fault-Prone Programs , 1992, IEEE Trans. Software Eng..

[14]  Taghi M. Khoshgoftaar,et al.  Early Quality Prediction: A Case Study in Telecommunications , 1996, IEEE Softw..

[15]  A. Kaur,et al.  Application of Random Forest in Predicting Fault-Prone Classes , 2008, 2008 International Conference on Advanced Computer Theory and Engineering.

[16]  Taghi M. Khoshgoftaar,et al.  EMERALD: software metrics and models on the desktop , 1996, Proceedings of the Fourth International Symposium on Assessment of Software Tools.

[17]  Venkata U. B. Challagulla,et al.  Empirical assessment of machine learning based software defect prediction techniques , 2005, 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems.