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AI for Automated Code Reviews and Quality Assurance

Authors:

Mariappan Ayyarrappan
Principle Software Engineer, Tracy, CA, USA
Email: mariappan.cs@gmail.com

 

Abstract

Automated code reviews and continuous quality assurance are essential in modern software development. Yet, conventional static analysis tools often produce large volumes of warnings, failing to capture deeper structural or semantic flaws. With the rise of artificial intelligence (AI), novel solutions can now parse codebases and understand patterns beyond rule-based checks—reducing false positives, spotting anti-patterns, and offering guided refactoring suggestions. This paper discusses how AI techniques, such as language models and machine learning–based code analysis, can enhance automated code reviews and ensure consistent standards at scale. We include a variety of diagrams to illustrate AI-driven review pipelines, highlight best practices for model training and data labeling, and survey the challenges surrounding security, intellectual property (IP) concerns, and developer adoption. By integrating AI into development workflows, organizations can streamline code quality management, reduce maintenance costs, and produce more robust software.

Keywords

AI Code Review, Automated QA, Static Analysis, Refactoring, Machine Learning, Developer Productivity