Software measurement data reduction using ensemble techniques

Software defect prediction models are used to identify program modules that are high-risk, or likely to have a high number of faults. These models are built using software metrics which are collected during the software development process. Various techniques and approaches have been created for improving fault predictions. One of these is feature (metric) selection. Choosing the most important features is important to improve the effectiveness of defect predictors. However, using a single feature subset selection method may generate local optima. Ensembles of feature selection methods attempt to combine multiple feature selection methods instead of using a single one. In this paper, we present a comprehensive empirical study examining 17 different ensembles of feature ranking techniques (rankers) including six commonly used feature ranking techniques, the signal-to-noise filter technique, and 11 threshold-based feature ranking techniques. This study utilized 16 real-world software measurement data sets of different sizes and built 54,400 classification models using four well known classifiers. The main conclusion is that ensembles of very few rankers are very effective and even better than ensembles of many or all rankers.