A junior developer on your team just merged their first pull request. They're thrilled. The Slack channel erupts with party emojis.
Three hours later, production is down.
The code looked fine on the surface. It passed the linter. The tests were green. But the PR was 2,000 lines across 47 files, and the reviewer spent exactly four minutes on it before clicking "Approve."
This is the paradox of pull requests. They're the most important quality gate in modern software development, and most teams are terrible at them. The PR process is where bugs get caught or missed, where junior developers learn or don't, and where engineering velocity either accelerates or grinds to a halt.
If you're new to development or leading a team that wants to get better at shipping code, understanding pull requests deeply matters far more than most people realize.
#What Is a Pull Request?
A pull request (often abbreviated to PR) is a proposal to merge code changes from one branch into another. It's a request that says: "I've made these changes. Please review them before they become part of our main codebase."
The term comes from Git's distributed model. When you create a pull request, you're asking the repository maintainer to "pull" your changes into their branch. On GitHub, they're called pull requests. On GitLab, they're called merge requests. The concept is identical.
But a pull request is more than a Git mechanism. In practice, it serves as:
A code review trigger -- It signals to your team that work is ready for feedback. Without PRs, code review would require manual coordination and constant pinging.
A discussion forum -- Reviewers can comment on specific lines, ask questions, suggest alternatives, and debate trade-offs. The PR becomes a permanent record of why decisions were made.
A quality gate -- Before code reaches production, it must pass through human review, automated tests, and status checks. The PR is where all of these converge.
An audit trail -- Months later, when someone asks "why did we change the authentication flow?", the PR description and review comments tell the full story.
#How Pull Requests Actually Work
Here's the typical workflow, step by step:
#1. Branch From Main
A developer creates a new branch from the main branch. This isolates their work so they can make changes without affecting anyone else.
1git checkout -b feature/add-user-export
#2. Make Changes and Commit
The developer writes code, commits their changes, and pushes the branch to the remote repository.
1git add .
2git commit -m "Add CSV export for user data"
3git push origin feature/add-user-export
#3. Open the Pull Request
On GitHub (or your platform), the developer creates a pull request from their branch into main. They write a title and description explaining what changed and why.
#4. Automated Checks Run
CI/CD pipelines kick off automatically. Tests run, linters check formatting, security scanners look for vulnerabilities. These happen without human intervention.
#5. Code Review
One or more team members review the changes. They read the code, leave comments, ask questions, and either approve or request changes.
#6. Address Feedback
The author responds to comments, makes requested changes, and pushes new commits. The review cycle may repeat several times.
#7. Merge
Once approved and all checks pass, the PR gets merged into the main branch. The feature branch is typically deleted.
#8. Deploy
Depending on your deployment setup, merging to main may automatically deploy to staging or production.
#Why Pull Requests Exist
Pull requests solve problems that every growing engineering team encounters.
#Knowledge Sharing
Without pull requests, code knowledge concentrates in whoever wrote it. PRs force at least one other person to read and understand new code. Over time, this distributes knowledge across the team.
I worked on a team where the senior backend developer went on parental leave. The team barely missed a beat because every piece of their code had been reviewed by at least one other person who understood it.
#Quality Gates
Humans catch things that automated tools miss. A linter won't tell you that your approach creates a subtle race condition. A test suite won't flag that your new endpoint duplicates functionality that already exists elsewhere. Reviewers will.
Google's engineering practices documentation reports that code review catches approximately 15% of bugs that would otherwise reach production. That's not a silver bullet, but it's a significant safety net.
#Onboarding Acceleration
New developers learn faster through pull request feedback than almost any other mechanism. Every review comment is a micro-lesson about the codebase, the team's conventions, and software engineering in general.
#Accountability Without Blame
PRs create a record of who changed what and why, without the adversarial dynamics of post-mortem finger-pointing. When something breaks, you can trace it back to the specific change and understand the context around it.
#Anatomy of a Great Pull Request
Not all pull requests are created equal. Here's what separates a PR that gets reviewed in minutes from one that sits for days.
#Title
The title should describe the change concisely. It should make sense to someone scanning a list of PRs without opening any of them.
Good: "Add CSV export endpoint for user data" Bad: "Fix stuff" Bad: "WIP - trying something with the export feature, not sure if this works yet"
#Description
A good PR description answers three questions:
What changed? A brief summary of the changes. Not a line-by-line recap (reviewers can see the diff), but a high-level explanation.
Why? The motivation behind the change. Is this a bug fix? A new feature? A refactor? Link to the relevant ticket or issue.
How to test? Steps a reviewer can follow to verify the change works. This is especially important for UI changes or complex business logic.
#Scope
A PR should do one thing. "Add user export" is a good scope. "Add user export, refactor the database layer, and fix that CSS bug from last week" is not.
Small, focused PRs get reviewed faster, have fewer merge conflicts, and are easier to revert if something goes wrong.
#Pull Request Size Matters
PR size is one of the strongest predictors of review quality. Google's research found that review effectiveness drops significantly as PR size increases. After about 400 lines of changes, reviewers start skimming instead of reading carefully.
Here's a practical categorization system for PR size:
Tiny (1-50 lines): Bug fixes, config changes, copy updates. These should take minutes to review.
Small (51-200 lines): A single feature addition, a targeted refactor, or a well-scoped improvement. The sweet spot for most PRs.
Medium (201-400 lines): Getting large but still manageable if the changes are cohesive. Expect longer review times.
Large (401-1000 lines): Reviewers will struggle to maintain attention. Consider splitting into smaller PRs.
Oversized (1000+ lines): Almost impossible to review thoroughly. These PRs are where bugs hide. Break them up.
Tools like Coderbuds automatically categorize every PR by size, making it immediately visible when a PR is too large for effective review. When you can see at a glance that your team's average PR size is creeping up, you can address the problem before it impacts quality.
The data is clear: teams that maintain smaller PR sizes have faster cycle times, fewer production bugs, and happier reviewers. Aim for the 100-300 line range as your default.
#The Pull Request Review Process
Reviewing code well is a skill that takes practice. Here's what experienced reviewers focus on.
#Correctness
Does the code actually do what the PR description says? This seems obvious, but it's the most common source of bugs. The developer intended X, the code does Y, and the reviewer assumed X without verifying.
#Architecture Fit
Does this change fit within the existing patterns of the codebase? If the rest of the application uses repository patterns, a new feature that makes raw database queries directly in the controller is a red flag.
#Edge Cases
What happens when the input is null? Empty? Extremely large? What if the external API is down? What if two users hit this endpoint simultaneously?
#Security
Is user input properly validated and sanitized? Are authorization checks in place? Could this change expose sensitive data in logs or error messages?
#Performance
Will this query scale? Is there an N+1 query problem? Are we loading more data than needed? Will this feature work with 10,000 users as well as it works with 10?
#Test Coverage
Are there tests for the new functionality? Do the tests actually verify the important behavior, or do they just check that the code runs without crashing?
#Readability
Will someone unfamiliar with this code understand it six months from now? Good code reads like prose. If the reviewer has to puzzle through what a function does, it needs better naming or structure.
#Common Pull Request Anti-Patterns
#Rubber Stamping
The reviewer clicks "Approve" without actually reading the code. This is the most dangerous anti-pattern because it provides a false sense of security. The team thinks code is being reviewed. It's not.
Signs of rubber stamping: approvals within seconds of opening a PR, no comments ever, "LGTM" on a 500-line change.
#The Mega PR
One developer works for two weeks in isolation, then drops a 3,000-line PR on the team. No one wants to review it. It sits for days. When someone finally does, they skim it because the context is too large to hold in working memory.
The fix: break work into incremental PRs. Ship the database migration first. Then the model changes. Then the API endpoint. Then the frontend. Each PR is reviewable in isolation.
#Review Purgatory
PRs sit unreviewed for days. The author moves on to other work. By the time feedback arrives, they've lost context and the merge conflicts have compounded. This is a cycle time killer.
Set a team SLA for code review. Four hours is aggressive but achievable. Twenty-four hours is reasonable for most teams. More than 48 hours is a problem.
#Nitpick Wars
Reviews that focus exclusively on style preferences—variable naming, whitespace, comment formatting—while ignoring actual logic and architecture. These comments should be handled by automated linters, not humans.
Configure your linter and formatter to handle style automatically. Free up human reviewers to focus on things that actually require human judgment.
#Stale PRs
PRs that have been open for weeks, accumulating merge conflicts and falling further behind the main branch. These are almost always a sign that the PR scope was too ambitious or the feature was deprioritized mid-flight.
Set a rule: if a PR hasn't been updated in a week, either close it or break it into smaller pieces.
#Pull Request Metrics That Matter
Measuring your PR process reveals bottlenecks you can't see with intuition alone.
#Cycle Time
Cycle time measures how long it takes from when a PR is opened until it's merged. This is the most actionable metric for engineering velocity. Elite teams merge PRs within hours. Average teams take days.
When cycle time creeps up, something is wrong: reviews are taking too long, PRs are too large, or there are too many blocking dependencies.
#Review Turnaround Time
How long does it take from when a review is requested to when the first meaningful feedback arrives? This is often the biggest bottleneck in the PR process.
If your median review turnaround is over 24 hours, your developers are spending significant time context-switching between writing code and addressing review feedback.
#PR Throughput
How many PRs is each developer merging per week? This isn't about maximizing volume—it's about detecting problems. If throughput suddenly drops, it could signal a blocker, a morale issue, or a shift to overly large PRs.
#Review Coverage
What percentage of PRs get at least one meaningful review? In theory, it should be 100%. In practice, some PRs get rubber-stamped or merged without review via emergency bypass.
Platforms like Coderbuds track these metrics automatically, giving engineering leaders visibility into their team's PR process without requiring manual data collection. When you can see that review turnaround spiked from 6 hours to 18 hours last week, you can investigate and fix the bottleneck before it compounds.
#Automating Pull Request Workflows
Manual processes don't scale. As your team grows, automation becomes essential.
#CI/CD Integration
Every PR should trigger automated tests, linting, and security scans. If these checks fail, the PR cannot be merged. This catches the easy stuff so reviewers can focus on the hard stuff.
#Branch Protection Rules
Configure your repository to require:
- At least one approving review before merge
- All status checks passing
- Branch up-to-date with the base branch
These rules prevent accidental merges of unreviewed or broken code.
#Automated Code Review
AI-powered code review tools can augment human reviewers by catching common issues automatically. They're not a replacement for human review, but they're an excellent first pass that catches security vulnerabilities, performance issues, and convention violations.
Tools like Coderbuds automatically review every PR and post AI-generated feedback as comments. This means reviewers start their review with common issues already flagged, letting them focus on architecture, logic, and design decisions.
#CODEOWNERS
GitHub's CODEOWNERS file automatically assigns reviewers based on which files are changed. This ensures the right people review the right code without manual coordination.
1# Backend team reviews all API changes
2/app/Http/Controllers/ @backend-team
3
4# Frontend team reviews Vue components
5/resources/js/ @frontend-team
6
7# DevOps reviews infrastructure
8/docker/ @devops-team
#PR Templates
A standardized PR template ensures developers provide the context reviewers need. Include sections for what changed, why, how to test, and a checklist of common quality checks.
#Pull Requests and DORA Metrics
Pull requests directly influence two of the four DORA metrics that Google uses to measure engineering performance.
#Deployment Frequency
Teams that merge small, frequent PRs deploy more often than teams that batch large changes. Each merged PR is a potential deployment. Smaller PRs mean more frequent, less risky deployments.
#Lead Time for Changes
Lead time measures the time from code commit to production deployment. A significant portion of lead time is spent in the PR process—waiting for review, addressing feedback, and waiting for approval. Reducing PR cycle time directly reduces lead time.
The connection between PR practices and DORA metrics makes the PR process a high-leverage improvement area. Teams that improve their PR workflow often see cascading improvements across all four DORA metrics.
#Getting Started: Practical Takeaways
If you take nothing else from this guide, implement these five practices:
1. Set a PR size target. Aim for under 300 lines. Make it a team norm, not a hard rule. When someone opens a large PR, ask "can we break this up?" instead of rejecting it.
2. Establish a review SLA. Agree as a team on a turnaround target. Start with 24 hours. Once that's consistent, try to bring it down to 4-8 hours.
3. Automate the boring stuff. Configure linters, formatters, and automated tests to run on every PR. Don't waste human attention on things machines can check.
4. Write better PR descriptions. Spend two minutes explaining what changed and why. This saves reviewers ten minutes of trying to figure it out from the diff.
5. Measure your PR process. You can't improve what you don't measure. Track cycle time, review turnaround, and PR size. Look at the trends weekly.
Pull requests are deceptively simple. Open a PR, get a review, merge the code. But the difference between teams that treat PRs as a formality and teams that treat them as a core engineering practice is enormous. The former ship bugs. The latter ship confidence.
Your PR process is your engineering culture made visible. Make it count.
