AI Code Review Flow v1.0
To improve code quality and review efficiency, I have designed and am operating an AI Code Review system based on GitLab Webhooks.
[v1.0] Current Code Review Process
The current process is designed so that AI code review is performed only after QA verification is completed.
- Development & QA Completion
After feature development, a stable codebase is secured by passing QA tests. - Merge Request (MR) Creation
An MR is created to merge the QA-approved source code into the main branch. - Webhook Trigger
GitLab sends the source code diff to the AI Code Review server via a webhook. - AI Analysis
The AI engine analyzes the transmitted diff files and performs an in-depth code review. - Feedback Delivery
The AI-generated review results are automatically posted as comments on the GitLab MR. - Final Approval
The team lead reviews the AI feedback and approves the final merge if no issues are identified. - Product Deployment
The approved code is ultimately deployed to the production environment.
Limitations Identified in Real-World Operation
While operating this process, I identified three critical risks stemming from the fact that the validation point occurs too late in the development lifecycle.
1. Increased Cost of Fixes
When logical defects are discovered after QA has already been completed,
even minor changes require re-running QA, resulting in disproportionately high costs and resource consumption.
2. Release Schedule Uncertainty
If security or performance issues are detected at the final pre-release stage,
they immediately lead to release delays and negatively impact the overall delivery schedule.
3. Accumulation of Technical Debt
Under intense deadline pressure, even meaningful AI feedback tends to be deferred with a
“release now, fix later” mindset, allowing potential defects to remain as technical debt.
To address these issues, I am considering a strategy to shift the timing of AI code reviews to the moment when code is pushed to a Git branch, rather than after QA completion.
Improvement Direction
AI code reviews will be performed continuously from the moment developers push their code, even before requesting QA validation.
Expected Benefits
- Maximized Review Utilization
Developers receive immediate feedback during development, allowing issues to be fixed in real time. - Consistently High Code Quality
High-quality code is ensured before entering QA, reducing overall quality management costs. - Higher QA Success Rate
QA is performed on AI-filtered, more stable code, shortening test cycles and improving efficiency.