  Technical debt is a well-known challenge in software development, and its
negative impact on software quality, maintainability, and performance is widely
recognized. In recent years, artificial intelligence (AI) has proven to be a
promising approach to assist in managing technical debt. This paper presents a
comprehensive literature review of existing research on the use of AI powered
tools for technical debt avoidance in software development. In this literature
review we analyzed 15 related research papers which covers various AI-powered
techniques, such as code analysis and review, automated testing, code
refactoring, predictive maintenance, code generation, and code documentation,
and explores their effectiveness in addressing technical debt. The review also
discusses the benefits and challenges of using AI for technical debt
management, provides insights into the current state of research, and
highlights gaps and opportunities for future research. The findings of this
review suggest that AI has the potential to significantly improve technical
debt management in software development, and that existing research provides
valuable insights into how AI can be leveraged to address technical debt
effectively and efficiently. However, the review also highlights several
challenges and limitations of current approaches, such as the need for
high-quality data and ethical considerations and underscores the importance of
further research to address these issues. The paper provides a comprehensive
overview of the current state of research on AI for technical debt avoidance
and offers practical guidance for software development teams seeking to
leverage AI in their development processes to mitigate technical debt
effectively
