Solving the Imbalanced Class Problem in Software Defect Prediction Using GANS
S.Kaliraj1, Aman Jaiswal2
1S.Kaliraj, Department of Software Engineering, Kattankulathur Campus, SRM Institute of Science and Technology (Formerly Known as SRM University), India.
2Aman Jaiswal, Department of Software Engineering, Kattankulathur Campus, SRM Institute of Science and Technology (Formerly Known as SRM University), India.
Manuscript received on 11 August 2019. | Revised Manuscript received on 17 August 2019. | Manuscript published on 30 September 2019. | PP: 8683-8687 | Volume-8 Issue-3 September 2019 | Retrieval Number: A2165058119/2019©BEIESP | DOI: 10.35940/ijrte.A2165.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction of software defects is a highly researched and important domain for cost – saving advantage in software development. Different methods of classification using attributes of static code were used to predict defects in software.However, the defective instances count is very minimal compared to the count of non – defective instances and this leads to imbalanced data, where the ratio of data class is not equal. For such data, conventional machine learning techniques give poor results.While there are different strategies to address this issue, normal oversampling methods are different versions of the SMOTE algorithm, These approaches are based on local information,instead of the complete distribution of minority class.GANs is used to approximate the true data distribution of minority class data used for software defect prediction.
Index Terms: Class Imbalanced, Software Defects , Gans.
Scope of the Article: Problem Solving and Planning