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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)534-550
Journal / PublicationIEEE Transactions on Software Engineering
Volume44
Issue number6
Early online date24 Jul 2017
StatePublished - Jun 2018

Abstract

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.

Research Area(s)

  • Class imbalance learning, Classification problems, Data sampling methods, Software defect prediction, Synthetic sample generation

Citation Format(s)

MAHAKIL : Diversity based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction. / Bennin, Kwabena Ebo; Keung, Jacky; Phannachitta, Passakorn; Monden, Akito; Mensah, Solomon.

In: IEEE Transactions on Software Engineering, Vol. 44, No. 6, 06.2018, p. 534-550.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review