Artificial Intelligence in Code Optimization and Refactoring

Abstract

AI has become useful in software development to help improve on code optimization/refactoring exercises thus boosting on productivity, performance and sustainable maintainability. AI tools including CodeT5, Codex, Intel's Neural Compressor, and Refactoring Miner help the developers to analyze the code, minimize it and advance refactoring engagements. This paper investigates the deployment of Al in code optimization and their performances in optimizing common codes used across industries on real-world case, highlighting the impacts of Al in enhancing system performance, code read abilities, and Reducing on the over burdensome and ailing technical debt stock. It also explores new frontiers in Al for software engineering; testing & quality assurance; self-adaptive code; program synthesis, which may completely alter the development cycle and coding methods during the subsequent decade. This paper also responds to other essential concerns: data accessibility, the generalization ofan Al model, interpretability and expandability, which affects the applicability and adoption of AI solutions. This paper aims to discuss how such advancements and challenges show how Al is valuable in identifying code improvement possibilities and supports the creation of efficient methods for improving software quality on an ongoing basis.