Analysis of Hybridized Techniques with Class Imbalance Learning for Predicting Software Maintainability

Abstract

Software maintainability is a vital concern of organizations that develop and maintain large software products. The models that assess the maintainability of software systems at initial development stages play a significant role. In the Software Maintainability Prediction (SMP), a prevalent issue that needs to be taken care of is imbalanced data problem. For SMP, imbalanced data problem arises when the software classes that require high maintenance effort are less in number than classes that require low maintenance effort. In this paper, we dealt with the imbalanced data problem by the data resampling. With the imbalanced data, efficient machine learning algorithms are unable to predict the data points of both classes competently. Therefore, we examine the effectiveness of hybridized (HYB) techniques. The HYB techniques aid in finding an optimal solution for a problem by judging the goodness of multiple solutions. As per the results of the study, Adaptive synthetic minority oversampling technique (Adasyn) and Safe level synthetic minority oversampling technique (SafeSMOTE) are the best techniques of imbalanced data. Also, among the investigated HYB techniques, Fuzzy LogitBoost (GFS-LB) and Particle Swarm Optimization with Linear Discriminant Analysis (PSOLDA) emerged as the best techniques to predict maintainability.