The Development of the Secure Quality Dataset (SQDS): Combining Security and Quality Measures Using Deep Machine Learning for Code Smell Detection

Abstract

Code smells are an indication of deviation from design principles or implementation in the source code. Early detection of these code smells increases software quality by using refactoring techniques that will help the developers in software engineering maintain the process of software. Security is included as one of the requirements of software artifact quality in the ISO/IEC 25010 standard so we thought the security in the design phase is more efficient than after delivery of the software to the customer. A study aims to create a new dataset containing security metrics besides the quality metrics that will help software engineering researchers by detecting both the presence of a security illusion and god class bad smell at the same time in a program, we take Fonata's dataset of god class that have 61features of quality metrics, then calculate the security metrics on these 74 software written in java by programming a parser to analyze each software, finally used five machine learning algorithms on the proposed datasets (SQDS), after that, we used accuracy performance metric was employed for comparing the results. The experimental findings suggest that the proposed dataset demonstrates superior performance in identifying code smell security vulnerability and augmenting the training data can improve the accuracy of predictions. Finally, we applied three deep machine learning (RNN, LSTM, and GRU) on both the original Fonata’s Dataset of God Class bad smell and our proposed SQDS dataset and made a comparison between them.