Can AI Make Code Reviews More Human? Augmenting, Not Replacing, the Review Process
Understanding the Code Review Bottleneck Problem
“A bottleneck is not a problem with the process itself; it’s a problem with the capacity to handle the flow.”
During WW2, radar technology offered the Allies a significant advantage in detecting enemy aircraft. But with radar becoming more advanced, it gave birth to a problem - the bottleneck of interpretation. On D-Day, June 6, 1944, radar operators were flooded with hundreds of radar blips on their screens. The sheer volume of signals made it difficult to distinguish between friendly and enemy planes. The bottleneck wasn’t the radar itself, it was the human capacity to interpret the overwhelming data. Much like radar operators from WW2, modern developers face a bottleneck in code reviews. When a small group of developers or senior team members is tasked with reviewing an overwhelming number of PRs, it creates delays, and the waiting time for feedback increases. With every PR, they get buried under small changes, formatting issues, and trivial revisions. This leaves them with very little bandwidth to focus on the more complex tasks.
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PRs lingering in review for days interrupt developers' workflow and significantly slow down the overall development process. This waiting period also affects team morale.
Furthermore, the longer a pull request stays open, the more the codebase continues to evolve. This increases the risk of merge conflicts.
Ultimately, this bottleneck not only impacts individual productivity but also hampers team collaboration and adds inefficiencies in the entire software development lifecycle.
Though senior developers are themselves figuring out effective code review approaches like The 3 PM Code Review Rule, most of them still have days when they spend more than 80% of their time on code reviews.
While introducing AI code review tools is one workable way, it might further overwhelm senior developers or code reviewers with unnecessary & inconsistent suggestions, noisy reviews, and an overabundance of low-priority comments.
Thus, it is crucial to adopt the approach of human-in-the-loop AI integration, where human reviewers can validate and fine-tune AI recommendations.
We need to make sure that AI code review tools alleviate the bottleneck without adding another layer of cognitive overload.
Because AI code review tools are meant to augment rather than replace human judgment.
How do code smells create bottlenecks in the code review?
Introduced by Kent Beck, an American Software Engineer, code smells refer to patterns or characteristics in the code that suggest potential problems or weaknesses, though they are not necessarily bugs or errors.
In other words, code smells are not actual faults, but signs that something could be improved to enhance code quality, readability, and maintainability.
Some common examples of code smells include,
- Long method: A method that extends too far and tries to do too many things.
- Large Class: A class that has many methods or attributes.
- Duplicated Code: When the same or very similar code appears in multiple places.
- Feature Envy: When one class frequently accesses the data or methods of another class.
- God Object: A class that tries to handle multiple responsibilities, and thus ends up making code complex and tightly coupled.
- Data Clumps: Groups of variables that are often passed together as arguments but aren't encapsulated in a class.
As you can easily comprehend that these coding practices don’t always lead to immediate bugs or defects. However, they silently degrade the code quality, clarity, and sustainability of the codebase over time. And to counter it, code refactoring would be needed.
That’s why code reviewers prefer addressing such issues early. Once these smells enter the codebase, every review becomes heavier.
Here is how code smells contribute to a code review bottleneck.
- Increased Review Time: Code smells often make the code harder to read and understand. Reviewers need to spend more time trying to decipher complex, messy, or poorly structured code.
- Reduced Reviewer Bandwidth: Code with smells demands more mental energy to process. It reduces reviewers’ capacity to handle new incoming reviews.
- More Iterations and Feedback: Code smells lead to extensive back-and-forth between the developer and the reviewer.
- Reduced Quality of Reviews: With code full of smells, reviewers may become overwhelmed by less critical problems. This distraction alters their focus away from more important concerns, like ensuring the code meets business requirements.
- Compromised Collaboration: Code smells make the codebase less collaborative. And now, with fewer developers can confidently work with the code, which adds delays even in solving the code smells problem, leading to an additional delay in the already delayed pipeline.
This combination of persistent code smells and code review bottleneck creates a dangerous recipe for code quality debt, which increases future costs associated with fixing, refactoring, and maintaining the codebase.
How Does AI Code Review Unclog the Code Review Pipeline?
The manual code review process often becomes the victim of the sheer volume of code or PR that needs to be reviewed, combined with the time-consuming nature of human feedback. This results in delayed releases, creates friction between teams, and leads to inconsistent quality.
Reviewers often miss major issues due to fatigue, distractions, or tight deadlines. Though they never wish, code smells, security vulnerabilities, and inefficient practices easily slip through the cracks.
AI-powered code review tools address this problem head-on. It automates much of the review process. Trained on code data, learning patterns, best practices, and common pitfalls across various programming languages and frameworks, AI code review tools identify and flag common issues such as bugs, security flaws, and inefficient code structures.
The best part? It does this job before issues ever reach the human review stage. This allows for faster, more consistent feedback by reducing review time and helping teams catch issues early.
The following is a high-level working mechanism of AI PR review tools.
- Code Analysis and Parsing
AI code review tools begin by parsing the codebase. It breaks down the code into functions, classes, and variables. Then the intelligence module of the tools examines these components in depth, recognizes patterns, structures, and potential anomalies.
- Issue Detection and Classification
The AI algorithm scans for bugs, security vulnerabilities, code smells, and performance bottlenecks. It even classifies these issues based on severity.
- Context-Aware Review
Unlike traditional static analysis tools, AI-powered tools consider the context of the code. In other words, it understands the link between different components and how changes in one part of the codebase may affect the overall system. This allows these tools to offer more relevant suggestions.
- Automated Fix Suggestions
Some advanced AI tools detect issues and also recommend fixes. These suggestions are based on best practices and patterns learned from large codebases.
- Learning from Past Reviews
Like all other AI tools, AI code review tools also learn and improve their knowledge base. As they are exposed to more code, they learn from previous reviews and developer feedback.
- Seamless Integration into Development Workflow
AI code review tools can seamlessly integrate with popular version control systems (like GitHub or GitLab). This makes working with AI code review tools frictionless, as it is now a part of the SDLC. At the advanced level, this integration makes the metrics capturing and tracking easier. For example, it can now identify the reason behind the extended time it takes for code changes to go from development to production and contextually connect it with DORA’s Change Lead Time metric.
Why do I suggest a human-in-the-loop approach for reviewing code with AI?
Undoubtedly, AI code review tools are powerful allies. But it is crucial to understand that its role is not to replace human reviewers, but to empower them.
The following table will give you a detailed idea of why combining human intelligence with artificial intelligence is a game-changer compared to playing solo with AI or humans!
Sounds fantastic? But not sure how to implement a human-in-the-loop approach for AI code reviews. You can follow this framework.
AI’s Role in Each Review Stage
What inspired us to build an AI Code Review Agent at Hivel?
Our Software Engineering Intelligence platform has captured some eye-opening signals in the PR lifecycle. According to it, the average pull request wait time is 4.6 days, out of which the majority of PRs get opened and reviewed on day four.
Till day four, the reviewers remain busy reviewing previous PRs. This depicts the clear code review bottleneck problem in SDLC.
With software engineering requirements getting complex and vibe coding taking over, the code review bottleneck problem is only expected to rise. And it makes a powerful, context-aware, and outcome-oriented AI assistant for the code review process inevitable.
Thus, by harnessing the inspiration of empowering developers and code reviewers with an agent to not just speed up the code review process but enhance overall productivity, and allow them to focus on more complex issues while automating routine checks, we built one at Hivel.
With our AI code review agent, we aim to help the engineering team achieve faster iteration cycles, improved code quality, and a more efficient development pipeline.
To realize that vision, we chose to avoid scratching the surface, but rather went the extra mile and built a code review agent with key differentiators like…
- Deep codebase understanding
- Focus on high-signal, actionable feedback
- Potential for customization and learning
- Flexible, enterprise-ready deployment options
So, are you already wondering, ‘What if reviewing code feels less like a chore and more like a strategic advantage?’
Join us today and be among the first to receive exclusive access to our faster, smarter, and insight-driven code review agent.
