Golnoush Abaei
ABSTRACT
To make most software defect-free, a considerable amount of budget needs to be allocated to the software testing phase. As each day goes by, this budget slowly rises, as most software grows in size and complexity, which causes an issue for specific companies that cannot allocate sufficient resources towards testing. To tackle this, many researchers use machine learning methods to create software fault prediction models that can help detect defect-prone modules so that resources can be allocated more efficiently during testing. Although this is a feasible plan, the effectiveness of these machine learning models also depends on a few factors, such as the issue of data imbalance. There are many known techniques in class imbalance research that can potentially improve the performance of prediction models through processing the dataset before providing it as input. However, not all methods are compatible with one another. Before building a prediction model, the dataset undergoes the preprocessing step, the under-sampling, and the feature selection process. This study uses an under-sampling process by employing the Instance Hardness Threshold (IHT), which reduces the number of data present in the majority class. The performance of the proposed approach is evaluated based on eight machine learning algorithms by applying it to eight moderate and highly imbalanced NASA datasets. The results of our proposed approach show improvement in AUC and F1-Score by 33% and 26%, respectively, compared to other research work in some datasets.
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