## Article  
The Impact of Ensemble Techniques on Software Maintenance Change Prediction: An Empirical Study  
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use of source code metrics as dependent variables [4; 5], but investigations are relatively rare compared with other software maintainability measurements [3]. Ensemble models, which combine several models instead of using just an individual model, have been applied in machine learning to improve performance. These models have an ability to decrease variance and have been proven to be of great practical value in various areas of software engineering [4; 5; 6]. However, there are few studies of prediction change-proneness using ensemble models [3]. Feature selection (FS) techniques have been introduced to address the problem of high-dimensional datasets, where the number of features is more than the number of observations. These techniques have the potential to increase prediction accuracy, reduce the time of model building, and identify the most vital features that affect the target attribute. Many studies have investigated various types of FS in software quality prediction and conclude that the ensemble FS techniques, which integrate the output of several FS strategies, are able to improve prediction accuracy [7], but, again, there is limited application of ensemble FS techniques in studies of software quality prediction [7]. Class imbalance is another dataset problem, where one class of the dataset has a small number of instances compared to the others, and machine learning models which fail to account for this end up predicting only from the majority class and ignoring the minority class. Several sampling techniques may be used to resolve this problem by adjusting the class distribution. These techniques are generally classified into three types: oversampling to increase the observations of the minority class (e.g., SMOTE [8] and UPSAMPLE [9]); undersampling to decrease the observations of the majority class (e.g., spread sub-sample [10] and random undersampling [11]), and ensemble sampling to combine the results of over- and undersampling (e.g., SMOTE and bootstrap sampling [12]). Although ensemble sampling techniques have been proven to increase the prediction accuracy, their application in software quality prediction is also relatively rare [9]. This study aims to investigate the performance of ensemble models, FS, and sampling techniques on the problem of predicting change-proneness using source code metrics. In order to achieve this aim, we examine four different scenarios on change-proneness datasets as follows: (a) datasets without FS and sampling techniques, (b) datasets with FS and without sampling, (c) datasets without FS and with sampling, and (d) datasets with FS and sampling.