DOI: https://doi.org/10.36347/sjet.2026.v14i02.002

Pages: 87-89

The integration of Artificial Intelligence (AI) into code quality analysis addresses the limitations of traditional manual and static analysis by leveraging machine learning (ML) and Large Language Models (LLMs) to identify syntax, semantic, and architectural issues. As of 2026, AI-driven tools have become foundational in Agile development, streamlining repetitive review tasks and enforcing consistent coding standards. While AI excels at rapid bug detection and security vulnerability identification, challenges remain in its ability to fully comprehend complex business logic and project-specific intent. Consequently, modern workflows emphasize a hybrid approach: "AI-assisted, human-verified". This strategy uses AI to handle the "repetitive 70%" of analysis, allowing human developers to focus on the remaining 30% of high-value, creative decision-making.