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- W3201334186 abstract "The application of ensemble learning techniques is continuously increasing, since they have proven to be superior over traditional machine learning techniques in various domains. These algorithms could be employed for bug prediction purposes as well. Existing studies investigated the performance of ensemble learning techniques only for PROMISE and the NASA MDP public datasets; however, it is important to evaluate the ensemble learning techniques on additional public datasets in order to test the generalizability of the techniques. We investigated the performance of the two most widely-used ensemble learning techniques AdaBoost and Bagging on the Unified Bug Dataset, which encapsulates 3 class level public bug datasets in a uniformed format with a common set of software product metrics used as predictors. Additionally, we investigated the effect of using 3 different resampling techniques on the dataset. Finally, we studied the performance of using Decision Tree and Naive Bayes as the weak learners in the ensemble learning. We also fine tuned the parameters of the weak learners to have the best possible end results." @default.
- W3201334186 created "2021-09-27" @default.
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- W3201334186 date "2021-01-01" @default.
- W3201334186 modified "2023-09-23" @default.
- W3201334186 title "Assessing Ensemble Learning Techniques in Bug Prediction" @default.
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- W3201334186 doi "https://doi.org/10.1007/978-3-030-87007-2_26" @default.
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