Using Hybridized techniques for Prediction of Software Maintainability using Imbalanced data

Abstract

Maintainability is an essential dimension of software quality. Software Maintainability Prediction (SMP) is gaining the attention of researchers to develop maintainable software systems. Early prediction of software maintainability aid the software practitioners to focus on those software modules or classes that requires high maintainability effort in the maintenance phase. However, the imbalanced distribution of training data is a challenging and serious problem that is encountered while developing prediction models for software maintainability. This paper apply oversampling methods namely: Adaptive Synthetic Oversampling technique (AdaS), BorderlineSynthetic Minority Oversampling technique (BSMOTE), Synthetic Minority Oversampling technique (SMOTE), and SafeLevel Synthetic Minority Oversampling technique (SSMOTE) to treat the imbalanced data before learning the models for software maintainability. We also investigate the effectiveness of hybridized techniques for learning the prediction models using three popular Apache datasets. The outcome of the study supports the use of investigated oversampling methods with hybridized techniques to develop effective prediction models for software maintainability.