Naive Bayes Software Defect Prediction Model

Although the value of using static code attributes to learn defect predictor has been widely debated, there is no doubt that software defect predictions can effectively improve software quality and testing efficiency. Many data mining methods have already been introduced into defect predictions. We noted there have several versions of defect predictor based on Naive Bayes theory, and analyzed their difference estimation method and algorithm complexity. We found the best one which is Multi- variants Gauss Naive Bayes (MvGNB) by performing prediction performance evaluation, and we compared this model with decision tree learner J48. Experiment results on the benchmarking data sets of MDP made us believe that MvGNB would be useful for defect predictions.

[1]  Standard Glossary of Software Engineering Terminology , 1990 .

[2]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[3]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[4]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[5]  Norman E. Fenton,et al.  Software Measurement: Uncertainty and Causal Modeling , 2002, IEEE Softw..

[6]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[7]  Taghi M. Khoshgoftaar,et al.  Analyzing software measurement data with clustering techniques , 2004, IEEE Intelligent Systems.

[8]  Tibor Gyimóthy,et al.  Empirical validation of object-oriented metrics on open source software for fault prediction , 2005, IEEE Transactions on Software Engineering.

[9]  Ingunn Myrtveit,et al.  Reliability and validity in comparative studies of software prediction models , 2005, IEEE Transactions on Software Engineering.

[10]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[11]  Hongfang Liu,et al.  Building effective defect-prediction models in practice , 2005, IEEE Software.

[12]  Qinbao Song,et al.  Software defect association mining and defect correction effort prediction , 2006 .

[13]  Taghi M. Khoshgoftaar,et al.  An empirical study of predicting software faults with case-based reasoning , 2006, Software Quality Journal.

[14]  Qinbao Song,et al.  Software defect association mining and defect correction effort prediction , 2006, IEEE Transactions on Software Engineering.

[15]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[16]  Tim Menzies,et al.  Data Mining Static Code Attributes to Learn Defect Predictors , 2007, IEEE Transactions on Software Engineering.

[17]  Xiuzhen Zhang,et al.  Comments on "Data Mining Static Code Attributes to Learn Defect Predictors" , 2007, IEEE Trans. Software Eng..

[18]  Bart Baesens,et al.  Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings , 2008, IEEE Transactions on Software Engineering.

[19]  Yue Jiang,et al.  Comparing design and code metrics for software quality prediction , 2008, PROMISE '08.