Bug Resolution Prediction for Open-Source Software Using Ensembles of Instance Selection Algorithms

Studies about maintenance effort estimation of open-source software have investigated the impact of single instance selection on the performance machine learning techniques. However, Ensemble of Instance Selection (EIS) has not been investigated for this topic, especially for bug resolution prediction. This empirical study considers the impact of EIS on the performance of k-Nearest Neighbor, Support Vector Machine, and Multinomial Naïve Bayes techniques. A set of 27 classifiers are built using Bagging and Random Feature Subset Ensembles based on AllkNN single instance selection algorithm on three datasets. The results are presented, together with a comparative analysis of the built classifiers' performance. The results show that the investigated EIS algorithms improve the performance of ML classifiers and outperform the single instance selection algorithm-based classifiers.