DESIGN AND IMPLEMENTATION OF AN AUTOMATED CODE REVIEW SYSTEM.

ABSTRACT In the sphere of modern software development, automated code review systems have become essential, as they endeavor to improve the security, efficiency, and quality of code. At an early stage in the development lifecycle, these systems implement a diverse array of advanced methodologies, such as deep learning, machine learning, and static analysis, to evaluate code for potential security vulnerabilities, flaws, and errors. The implementation of automated code reviews yields numerous advantages, such as expedited feedback loops, the consistent application of coding standards, and the capability to scale effectively with expanding codebases. However, despite their significant benefits, these systems encounter several challenges. These include the occurrence of false positives and negatives, difficulties in perceiving the contextual intent behind code, and complexities associated with integrating these tools into existing development workflows. Current research efforts are concentrated on enhancing the accuracy, contextual understanding, and explainability of automated code review systems, as well as assuring their seamless integration into integrated development environments (IDEs).