Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Different classifiers find different defects although with different level of consistency
AU - Bowes, David
AU - Hall, Tracy
AU - Petrić, Jean
PY - 2015/10/21
Y1 - 2015/10/21
N2 - BACKGROUND - During the last 10 years hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. OBJECTIVE - We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. METHOD - We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in 12 NASA data sets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty is compared against different classifiers. RESULTS - Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. CONCLUSIONS - Our results confirm that a unique sub-set of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Classifier ensembles with decision making strategies not based on majority voting are likely to perform best.
AB - BACKGROUND - During the last 10 years hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. OBJECTIVE - We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. METHOD - We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in 12 NASA data sets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty is compared against different classifiers. RESULTS - Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. CONCLUSIONS - Our results confirm that a unique sub-set of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Classifier ensembles with decision making strategies not based on majority voting are likely to perform best.
U2 - 10.1145/2810146.2810149
DO - 10.1145/2810146.2810149
M3 - Conference contribution/Paper
AN - SCOPUS:84947607088
BT - PROMISE '15 Proceedings of the 11th International Conference on Predictive Models and Data Analytics in Software Engineering
PB - Association for Computing Machinery, Inc
CY - New York
T2 - 11th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2015
Y2 - 21 October 2015
ER -