MAHAKIL: Diversity Based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction

Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software defect datasets tend to have fewer defective modules than non-defective modules. Synthetic oversampling approaches address this concern by creating new minority defective modules to balance the class distri...

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Bibliographic Details
Published inIEEE transactions on software engineering Vol. 44; no. 6; pp. 534 - 550
Main Authors Bennin, Kwabena Ebo, Keung, Jacky, Phannachitta, Passakorn, Monden, Akito, Mensah, Solomon
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2018
IEEE Computer Society
Subjects
Animals
Biological cells
class imbalance learning
classification problems
data sampling methods
Datasets
Defects
Electronic mail
False alarms
Mathematical models
Modules
Oversampling
Parents
Predictive models
Sampling methods
Software
Software defect prediction
synthetic sample generation
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Summary:Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software defect datasets tend to have fewer defective modules than non-defective modules. Synthetic oversampling approaches address this concern by creating new minority defective modules to balance the class distribution before a model is trained. Notwithstanding the successes achieved by these approaches, they mostly result in over-generalization (high rates of false alarms) and generate near-duplicated data instances (less diverse data). In this study, we introduce MAHAKIL, a novel and efficient synthetic oversampling approach for software defect datasets that is based on the chromosomal theory of inheritance. Exploiting this theory, MAHAKIL interprets two distinct sub-classes as parents and generates a new instance that inherits different traits from each parent and contributes to the diversity within the data distribution. We extensively compare MAHAKIL with SMOTE, Borderline-SMOTE, ADASYN, Random Oversampling and the No sampling approach using 20 releases of defect datasets from the PROMISE repository and five prediction models. Our experiments indicate that MAHAKIL improves the prediction performance for all the models and achieves better and more significant pf values than the other oversampling approaches, based on Brunner's statistical significance test and Cliff's effect sizes. Therefore, MAHAKIL is strongly recommended as an efficient alternative for defect prediction models built on highly imbalanced datasets.
ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2017.2731766