A Combined-Learning Based Framework for Improved Software Fault Prediction

Abstract

Software Fault Prediction (SFP) is found to be vital to predict the fault-proneness of software modules, which allows software engineers to focus development activities on fault-prone modules, thereby prioritize and optimize tests, improve software quality and make better use of resources. In this regard, machine learning has been successfully applied to solve classification problems for SFP. Nevertheless, the presence of different software metrics, the redundant and irrelevant features and the imbalanced nature of software datasets have created more and more challenges for the classification problems. Therefore, the objective of this study is to independently examine software metrics with multiple Feature Selection (FS) combined with Data Balancing (DB) using Synthetic Minority Oversampling Techniques for improving classification performance. Accordingly, a new framework that efficiently handles those challenges in a combined form on both Object Oriented Metrics (OOM) and Static Code Metrics (SCM) datasets is proposed. The experimental results confirm that the prediction performance could be compromised without suitable Feature Selection Techniques (FST). To mitigate that, data must be balanced. Thus our combined technique assures the robust performance. Furthermore, a combination of Random Forts (RF) with Information Gain (IG) FS yields the highest Receiver Operating Characteristic (ROC) curve (0.993) value, which is found to be the best combination when SCM are used, whereas the combination of RF with Correlation-based Feature Selection (CFS) guarantees the highest ROC (0.909) value, which is found to be the best choice when OOM are used. Therefore, as shown in this study, software metrics used to predict the fault proneness of the software modules must be carefully examined and suitable FST for software metrics must be cautiously selected. Moreover, DB must be applied in order to obtain robust performance. In addition to that, dealing with the challenges mentioned above, the proposed framework ensures the remarkable classification performance and lays the pathway to quality assurance of software.

Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction

“…We use all data points collected from Release 1 as the training set. Because of the class imbalance issue in the training set we apply SMOTE [43], [70], [86] to oversample the minority class (i.e., classified as faultprone) so that the size of fault-prone samples is equal to the size of samples that are non-fault-prone. For the training set, we randomly split all fault-prone classes in the training set into 30 equal-sized groups.…”

An Empirical Study for Enhanced Software Defect Prediction Using a Learning-Based Framework

“…Class imbalance is common among different applications including software quality evaluation. However, an imbalance in data was identified to pose a significant challenge to learners by Khoshgoftaar et al [10] in which a process for best FS was proposed to improve model performance when dealing with imbalance data. This was the case, especially in software defect datasets which are often used for training in classification for SDP.…”

Novel method to forecast software aging problems

“…The results show that the study of software ageing of third‐party libraries can help clients maintain software systems of their libraries. Yohannese et al [14] used a combination of a random forts with the information gain to improve software fault prediction and find that this method can guarantee the highest ROC with 0.909. Also, the method is a helpful supplement for ours.…”