Leveraging Generative Artificial Intelligence for Software Antipattern Detection

Traditional software security assessment focuses on detecting as well as patching known vulnerabilities. In this study, we propose that security assessment based on the antipattern approach should shift attention to broader architectural and coding practices. This research explores the potential application of Generative Artificial Intelligence (AI) to identify and mitigate software antipatterns. Antipatterns represent standard design or implementation flaws that hinder software maintainability, scalability, and overall quality. With cognitive models, we aim to mitigate coding errors in software development. By leveraging generative AI models, our goal is to automate the antipattern detection process, providing developers with a powerful tool to enhance code quality and reduce technical debt.