Ensemble Deep Network for Secured Refactoring Framework by Predicting Code-Bad Smells in Software Projects

Abstract

In modern times, refactoring is one of the significantly utilized approaches for enhancing the software's quality like understandability, testability, and maintainability. Moreover, the refactoring effect on its security has been underrated. In addition to that, there are only a few studies that offer the classification over refactoring approaches depending on the effect over the quality attributes that help the designer to attain certain objectives by choosing the most significant approach and it is applied in the right places based on the specified software quality attributes. The contradictory outcomes are attained by considering the quality of the software creates limitations for the developers while performing the software refactoring process. In this paper, a secured deep learning-based software refactoring approach is designed. At first, software projects collected from online sources are offered as input for this software refactoring process to detect the security metrics in the projects. After detecting the security metrics, refactoring is applied in the software projects to change the internal design. Then, the security metrics of the refactored projects are detected again. Further, the security metrics computed before and after refactoring are compared with the software projects. The projects are labeled based on security, needs, and refactoring level. Then, the Ensemble Attention-based Deep Network (EA-DNet) is developed, which is designed with the Recurrent Neural Network (RNN), Deep Temporal Convolution Network (DTCN), and Bi-directional Long Short Term Memory (Bi-LSTM). This network is trained to get better results in the prediction of code-bad smells in software projects. The prior software refactoring approaches are compared with the proposed code-bad smells-based software refactoring process.