Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.349
The existing fault-proneness prediction methods are based on unsampling and the training dataset does not contain the information on the number of faults of each module and the fault distributions among these modules. In this paper, we propose an oversampling method using the number of faults to improve fault-proneness prediction. Our method uses the information on the number of faults in the training dataset to support better prediction of fault-proneness. Our test illustrates that the difference between the predictions of oversampling and unsampling is statistically significant and our method can improve the prediction of two probability models, i.e. logistic regression and naïve Bayes with kernel estimators.
software engineering, quality assessment, fault-proneness prediction, statistical analysis, learner, bugs
Lianfa Li, Hareton Leung, "Using the Number of Faults to Improve Fault-Proneness Prediction of the Probability Models", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 722-726, doi:10.1109/CSIE.2009.349