Hybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems

Software reliability is a paramount determinant of software quality. In this research paper, we delve into utilizing Genetic Algorithms (GAs) for feature selection and classification. We undertake a comprehensive evaluation and comparative analysis of Machine Learning models, specifically Random Forest and Logistic Regression, both with and without Genetic Algorithmdriven feature selection. Our findings substantiate the significant impact of Genetic Algorithms in improving the accuracy of software reliability analysis.

I. Introduction

Software plays an integral role in modern society, driving everything from communication to commerce. Ensuring the reliability of software is paramount as it directly impacts user experience, operational efficiency, and potentially critical outcomes. Software reliability analysis ensures software systems consistently perform as intended, a critical component of software quality, particularly vital in high-stakes environments.