ABSTRACT Software refactoring plays a vital role in maintaining and improving the quality of software systems. The software refactoring network aims to connect developers, researchers, and practitioners to share knowledge, best practices, and tools related to refactoring. However, the network faces various challenges, such as the complexity of software systems, the diversity of refactoring techniques, and the need for automated and intelligent solutions to assist developers in making refactoring decisions. By leveraging deep learning techniques, the software refactoring network can enhance the speed, accuracy, and relevance of refactoring suggestions, ultimately improving the overall quality and maintainability of software systems. So, in this paper, an advanced deep learning–based software refactoring framework is proposed. The suggested model performs three phases as (a) data collection, (b) feature extraction, and (c) prediction of software refactoring. Initially, the data is collected from ordinary datasets. Then, the collected data is fed to the feature extraction stage, where the source code, process, and ownership metrics of all refactored and non‐refactored data are retrieved for further processing. After that, the extracted features are predicted using Adaptive and Attentive Dilation Adopted Hybrid Network (AADHN) techniques, in which it is performed using Deep Temporal Context Networks (DTCN) with a Bidirectional Long‐Short Term Memory (Bi‐LSTM) model. Here, the parameters in the hybrid networking model are optimized with the help of Constant Integer Updated Golden Tortoise Beetle Optimizer (CIU‐GTBO) for improving the prediction process. Therefore, the accuracy of the developed algorithm has achieved for different datasets, whereas it shows the value of 96.41, 96.38, 96.38, 96.38, 96.41, 96.38, and 96.39 for antlr4, junit, mapdb, mcMMO, mct, oryx, and titan. Also, the precision of the developed model has shown the better performance of 96.38, 96.32, 96.37, 96.33, 96.35, 96.37, and 96.31 for the datasets like antlr4, junit, mapdb, mcMMO, mct, oryx, and titan.

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.