Adaptive Abstraction with AI for Managing Software Antipatterns Throughout the Software Lifecycle

Antipatterns, commonly described as solutions that initially appear promising but have negative consequences, present significant risks in software development, particularly with respect to security. This paper explores the classification of antipatterns in various domains, such as architecture, design, and implementation, while highlighting their impact on cybersecurity. We propose the integration of Artificial Intelligence (AI) and Machine Learning (ML) to detect and mitigate antipatterns in real-time through automated tooling and DevOps pipelines. Furthermore, the role of abuse stories in requirement elicitation and the application of UML diagrams for identifying antipatterns are examined. The paper culminates with a framework for AI-driven antipattern management within modern software development, illustrating its potential through case studies and practical experiments.