Ensembles based combined learning for improved software fault prediction: A comparative study

Software Fault Prediction (SFP) research has made enormous endeavor to accurately predict fault proneness of software modules to maximize precious software test resources, reduce maintenance cost, help to deliver software products on time and satisfy customer, which ultimately contribute to produce quality software products. In this regard, Machine Learning (ML) has been successfully applied to solve classification problems for SFP. Moreover, from ML, it has been observed that Ensemble Learning Algorithms (ELA) are known to improve the performance of single learning algorithms. However, neither of ELA alone handles the challenges created by redundant and irrelevant features and class imbalance problem in software defect datasets. Therefore, the objective of this paper is to independently examine and compare prominent ELA and improves their performance combined with Feature Selection (FS) and Data Balancing (DB) techniques to identify more efficient ELA that better predict the fault proneness of software modules. Accordingly, a new framework that efficiently handles those challenges in a combined form is proposed. The experimental results confirm that the proposed framework has exhibited the robustness of combined techniques. Particularly the framework has high performance when using combined bagging ELA with DB on selected features. Therefore, as shown in this study, ensemble techniques used for SFP must be carefully examined and combined with both FS and DB in order to obtain robust performance.