As an engineering team grows, the code review process is often one of the first things to show signs of strain. What used to be a quick, collaborative check turns into a queue. Pull requests start to pile up, delivery slows down, and you can feel the impact on the team’s pace. The default assumption is usually that reviewers are too busy, but throwing more people at the problem rarely fixes it. The bottleneck is usually hidden somewhere else in the system.
A stalled code review process creates problems that compound quickly. Developers submit a change and move on to another task while waiting for feedback, which leads to costly context switching when that feedback finally arrives. Large changes sit idle for days, block other work, and make final integration increasingly difficult. Over time, this buildup starts to affect team engagement. Developers begin to cut corners in their own submissions or do more superficial reviews just to clear the queue. Gradually, the system starts to prioritize flow at any cost, while technical debt grows with little visibility.
The most common mistake is interpreting the symptom, “waiting for review,” as the root cause. A long queue doesn’t always mean a lack of review capacity. It can very well mean that the pull requests entering the queue are too hard, too confusing, or too large to be reviewed effectively. Focusing only on reviewer speed is like trying to fix a traffic jam by telling drivers to speed up, without checking whether there’s a closed bridge further ahead.
Identifying the problem in your code review process
On the submission side: everything that happens before a reviewer is even assigned. This is where many problems start. Pull requests that are too large, with vague descriptions or without explaining the intent of the change, force reviewers to spend time just understanding what’s being proposed. This is also where the lack of automated checks shows up, forcing people to waste time on style details or to catch bugs that a linter could have flagged on its own.
On the review side: these are the more obvious issues. Sometimes there simply aren’t enough people with the right context to review a change. In other cases, the most experienced engineers get overloaded and end up becoming the bottleneck. Inconsistent feedback is also common: different reviewers point out conflicting things, which creates rework and frustration for the person submitting the PR.
On the process side: this is where the mechanics and culture of review come in. The rules aren’t always clear. How long is it acceptable to wait for a first review? Who decides when something can be merged? Poorly defined ownership is a classic problem, especially when a PR spans multiple domains and sits idle waiting for approvals from teams that aren’t actually involved. Inadequate tools also get in the way, making it harder to understand changes or track the status of checks.
The key is to understand how all of this is connected. A culture of giant PRs (submission side) inevitably leads to reviewer overload (review side) and long review cycles (process side). The symptom is slow reviews, but the root cause lies in how the change was prepared from the very beginning.
How to figure out where your bottleneck is
You can’t fix what you can’t see. Getting a clear view of your code review process requires a combination of looking at data and talking to the people involved.
Quantitative metrics: what to measure and why
Tracking metrics helps move from “it feels slow” to “we know where it’s slow.” Instead of looking only at averages, focus on flow-oriented metrics that tell the story of a PR’s lifecycle.
- Pull request size: Look at the distribution of lines of code changed. A few large PRs can skew the average and are almost always a source of slow and shallow reviews.
- Time to first review: How long does a PR sit before anyone even looks at it? A long delay here usually points to unclear ownership, hesitation to review a large change, or a notification system that isn’t working.
- Review churn rate: How many comments or update cycles does a PR typically go through? A high rate can indicate unclear requirements, low submission quality, or inconsistent reviewer feedback.
- Time to merge: This is the total cycle time, from PR creation to merge. Look at the outliers. Which PRs take the longest, and what do they have in common?
- Review distribution: Do a small number of people do most of the reviews? That can indicate a knowledge silo or a senior engineer who has become a single point of failure.
A quick diagnosis
Go through these questions with the team to get a sense of where things might be breaking down:
- Does our CI catch most style and lint issues, or are reviewers spending time on that?
- When you open a PR, do you know exactly who should review it?
- Does the average PR have fewer than a few hundred lines of code, or are changes with more than 1,000 lines common?
- Do PR descriptions clearly explain the “what” and the “why” of the change, or do reviewers have to guess?
- Does the team have a shared understanding of how deep a review should go?
- Are there PRs that have been open for more than a week? If so, why?
Qualitative feedback: listening to the team
Metrics show what’s happening. People explain why. Often, the best insights come from simply asking the team where the biggest pain points are.
- Dedicated retrospectives: Run a retro focused specifically on the development and review flow. What consumes the most time? Which parts feel like waste?
- One-on-one conversations: Talk individually with developers and tech leads. People tend to be more candid about process issues in one-on-one conversations than in groups. Ask them to walk you through the last PRs they opened.
Tackling the main problems
Once you have a better idea of where the bottlenecks are, you can apply fixes that address the root causes rather than just the symptoms.
Improving submission quality
This is the highest-leverage point. A well-prepared PR speeds up the entire review process.
- Promote smaller pull requests: this is the single most impactful change. Smaller, focused PRs are easier to understand, faster to review, and less risky to merge. If a change is too large, it probably needs to be split.
- Establish clear PR templates: A good PR template encourages the author to provide context: what problem is being solved, how it was tested, and what the reviewer should focus on. This reduces the cognitive load on reviewers.
- Leverage automated checks: Get as much out of the way as possible via CI. Linters, static analysis, and automated tests should be the first line of defense. A human reviewer’s time is better spent on logic, architecture, and clarity, not on things a machine can catch. Automated checks like AI tools can significantly improve this process.
Allocating reviewers better and aligning expectations
Making sure a PR reaches the right people quickly makes all the difference to flow.
- Set realistic expectations: be explicit about what’s expected from a “good” review. Is the goal to catch every possible bug, or primarily to evaluate architecture and maintainability? Clarifying the scope of a review avoids both superficial approvals and overly nitpicky feedback.
- Distribute review responsibility: involve the entire team in the process, not just the most experienced engineers. This spreads the load, shares knowledge, and helps more junior developers understand the codebase and its standards.
How to keep the process healthy over time
Your code review process shouldn’t be static. It needs to evolve alongside the team and the codebase.
- Run regular process retrospectives: Revisit the topic every quarter, for example. Are the changes working? Have new bottlenecks appeared?
- Experiment and document: try new tools or workflows. If something works, document it and fold it into team standards. Good standards create a stable foundation and reduce the need for constant intervention.
