## Article
For this purpose, two types of filter-based feature-ranking techniques, namely, relief and Pearson's correlation coefficient, were combined using the ensemble concept to determine the best metrics and tackle the problem of high dimensionality. The sampling techniques (i.e., SMOTE, spread sub-sample, and randomize) were also combined together in an ensemble to address the imbalance class problem. Finally, three individual models (naive Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN)) and one ensemble model (random forest (RF)) were employed to predict change-proneness. These models were applied on seven publicly available datasets (i.e., antlr4, junit, MapDB, mcMMO, mct, oryx, and titan) extracted from open-source software systems [13]. The performance of the predicted models was compared and evaluated using the area under curve (AUC). The four research questions (RQs) were focused to accomplish the objectives of this paper:  
* **RQ1**. What is the impact of ensemble FS techniques on the performance of prediction models? * **RQ2**. What is the impact of ensemble sampling techniques on the performance of prediction models? * **RQ3**. What is the impact of applying both ensemble FS and sampling techniques on the performance of prediction models? * **RQ4**. What is the performance of the individual model and does the ensemble model (RF) outperform individual models? * **RQ5**. What is the impact of the MTRY parameter tuning on RF? The key contributions of this study are the following:* The capability of the ensemble models, FS, and sampling techniques on predicting of change-proneness was evaluated using four different scenarios. To the best of the authors' knowledge, this is the first study to investigate aspects such as the ensemble model, ensemble FS, and ensemble sampling techniques in predicting change-proneness. The most important finding was that RF provided a significant improvement over other prediction models and obtained the highest value of AUC in all scenarios. * More informative and recommended measure was used for skewed datasets (i.e., AUC) to evaluate prediction accuracy (rather than using only measures based on accuracy such as precision or recall), and statistical test and effect size were performed on the results to evaluate the performance of the model. * To make our predictive models refutable, conformable, and repeatable, the study used recent public datasets published in [13] and available on the PROMISE repository [14]. Two of these datasets are large and contain more than 1000 classes (i.e., mct and titan). Furthermore, a comprehensive replication package was provided which includes the new version of the datasets adapted for this study. * The impact of MTRY parameter tuning in RF was explored using grid search. The rest of the paper is organized as follows: Section 2 reviews the related work, Section 3 describes the research methodology, Section 4 presents the experimental design and setup, Section 5 discusses and analyzes the obtained results, Section 6 reports threats that could impact on the validity of this study, and Section 7 concludes this study with some directions for future work.