AI-Based Reviewer for Project Code and Architecture: A Graph-Augmented Framework for Pull Request Review and Architectural Drift Detection

Modern pull request (PR) review is increasingly constrained by repository scale, architectural complexity, and governance expectations. Prior studies show that review effectiveness depends on change understanding and reviewer context [1], while architecture-consistency research shows that implemented structures frequently drift from intended designs during software evolution [2], [3]. This paper presents AI-Based-Quality-Check-On-Project-Code-And-Architecture, a graph-augmented review framework that couples GitHub event ingestion, abstract syntax tree (AST) parsing, dependency-graph modeling, and configurable large language model (LLM) reasoning with governed project control. The framework (i) enriches pull request assessment with structural and repository context, (ii) detects circular dependencies, cross-layer violations, and coupling anomalies before they become deeply embedded in routine development, and (iii) records every review execution in an auditable, role-controlled workflow. A containerized prototype integrating a Next.js frontend, a FastAPI backend, PostgreSQL, Neo4j, and Redis provides a reproducible baseline for studying how AI-assisted review, graph-based architecture analysis, and software quality governance can be combined within a single engineering workflow.