LITERATURE SURVEY ON AUTOMATED STATIC EVALUATION OF E-COMMERCE WEBSITE USABILITY AND SECURITY USING MACHINE LEARNING

Abstract

The growth of e-commerce websites has prompted extensive research into usability and security evaluation methods to enhance user experiences and safeguard sensitive information. Traditional usability testing and security analysis methods often require dynamic, manual testing, which can be time-consuming and inefficient. In recent years, static machine learning (ML) techniques have emerged as a potential solution for automating and improving the analysis of these critical factors. This paper surveys current literature on static machine learningbased evaluation methods for usability and security analysis in e-commerce websites, highlighting key 
contributions, methodologies, challenges, and future directions.