Conceptual Model of a Hybrid Automated AI-Based Code Review System
Ensuring code quality has become one of the important challenges in modern software development processes. As software systems grow, code complexity, architectural complexity, security risks, and the likelihood of technical debt accumulation also increase. Traditional code review processes, which mainly rely on human expertise and manual analysis, do not always ensure fast and consistent evaluation. This paper presents the concept of a Hybrid Automated AI-Based Code Review System, which combines static analysis, rule-based validation, project context processing, and the capabilities of Large Language Models (LLMs). The proposed approach considers code review not merely as a process of generating comments, but as a context-aware multi-stage process that includes code change assessment, risk identification, problem localization, and generation of explainable recommendations. The research methodology is based on the analysis of existing AI-based code review approaches and the development of a hybrid architectural model. The proposed model utilizes multi-source context, including code changes, project structure, code dependencies, static analysis results, testing information, and Pull Request descriptions. The main contribution of the research lies in the development of a conceptual model of a hybrid AI-based code review system that integrates traditional software analysis methods with AI-based contextual reasoning. The proposed approach aims to improve the accuracy, explainability, and practical applicability of code review systems while supporting, rather than replacing, human reviewers.