An AI Powered Automated Code Review System Using Large Language Models and Static Analysis

Abstract: Reviewing pull requests by hand is slow and in-consistent, and the automated aids available today each solve only half of the problem: static analysers enforce fixed rules but cannot grasp the context of a change, while large language models (LLMs) can reason about code yet may return inaccu-rate or non-reproducible feedback. This paper surveys work that pairs LLMs with static or program analysis for automated code review, looking at system architectures, the way the two components are combined, the evidence reported, and the de-sign questions that remain open. Ten sources were examined, and five systems with enough accessible detail were set side by side. Combining an LLM with a rule-based or analysis-based component recurs across the set, yet the designs differ in how analysis supports the model, in deployment cost, and in the strength of their evidence. That evidence is uneven: one source gives a precision figure for generated comments, one records favourable reactions from developers, and the rest offer quali-tative or limited detail, so ranking the systems would be unjus-tified. Six open issues are identified: comparable quantitative evaluation, deployment cost and accessibility, merging of con-textual and rule-based findings, verification and reproducibility of model output, handling of failures, and language coverage. A related pull request review system is placed against these is-sues; its software modules are reported as implemented, while end-to-end validation and the final merged report are still in progress. The contribution is therefore a comparison limited to its sources, a gap analysis that stays within the evidence, and a plan for evaluation, not a claim that any system is better.

Keywords: automated code review, large language mod-els, static analysis, Semgrep, pull request, hybrid review