Code quality—it’s something we all agree is important, but actually getting it right? That’s tough.
You fix one problem, and before you know it, another one shows up, like an unwanted guest who won’t leave.
The tricky part is that “good” code isn’t the same for everyone. What works great for one engineering team might be too complicated for another. A small engineering team trying to meet tight deadlines can’t focus on the same details as a big company building for the long term. It all depends on your engineering team, your goals, and the challenges you’re dealing with.
And here’s the truth: no codebase is perfect. Not Google’s, not Amazon’s—nobody’s. The goal isn’t perfection; it’s about making things better, one step at a time.
So in this blog, we’ll share ten simple ways to do just that. These tips are practical, easy to follow, and might even make you wonder, “Why didn’t we start doing this sooner?”
Let’s jump in.
So, How Can You Improve Code Quality?
After working with countless engineering teams, one thing has become clear: code quality rarely falls apart because of one catastrophic mistake. It’s more like gradual erosion—small, seemingly harmless decisions that pile up over time. A quick workaround here, a skipped code review there, and suddenly your team is battling tech debt that slows everything down.
The good news? Small, intentional improvements can reverse that trend. It’s not about rewriting your entire codebase or adopting every new tool under the sun. It’s about finding the areas where your engineering team can tighten things up, communicate better, and make decisions that leave the codebase in a stronger place than they found it.
The following strategies are about more than just the code itself. They focus on the people, habits, and systems behind the code—the real drivers of long-term quality.
Let’s break it down.
1. Define Clear Coding Standards
Every engineering team needs a shared understanding of what “good” code looks like. Coding standards help with that, but they only work if they’re realistic and easy to follow. Start with the basics: consistent naming conventions, formatting rules, and best practices for writing functions. Tools like linters can help automate these checks, so developers don’t have to worry about playing code police.
The real trick? Make your standards flexible. If your rules are too rigid, they’ll feel more like a burden than a guide. Allow room for exceptions when they make sense, and regularly revisit the rules as your engineering team grows or new technologies come into play. Think of coding standards as guardrails—they keep you on track but shouldn’t box you in.
2. Embrace Code Reviews as a Learning Tool
Code reviews aren’t just about catching bugs; they’re about learning and improving as an engineering team. A good review should feel like a conversation, not a critique. Ask questions like, “Why did you choose this approach?” or “How does this handle edge cases?” instead of just pointing out what’s wrong.
The most valuable reviews aren’t always the ones that find issues—they’re the ones that share context and make the team smarter. Don’t forget to balance thoroughness with speed. Too many back-and-forth comments can slow things down unnecessarily. Keep it focused, and always look for opportunities to help your teammates grow.
3. Use Static Analysis Tools (But Don’t Overdo It)
Static analysis tools are great for spotting potential problems early, but don’t let them overwhelm your engineering team. When we first started using one, it flagged everything—even the tiniest issues. Developers ended up spending more time arguing with the tool than improving the code.
The key is to customize the rules to match your team’s priorities. Focus on the types of issues that matter most to your project, and ignore the noise. These tools should save you time, not create more work.
4. Automate Testing—But Be Smart About It
Automated tests are a game-changer for improving code quality, but only if they’re done right. Writing tests for everything might sound like a good idea until you realize maintaining them is a full-time job. Focus on automating tests for the most critical parts of your codebase—core functionality, high-risk areas, and anything prone to frequent changes.
The sweet spot? Balance automated tests with exploratory, human-driven testing. Automation is perfect for catching known issues, but it won’t uncover unexpected problems or edge cases. A mix of both ensures your codebase stays strong without turning testing into a bottleneck.
5. Refactor Regularly, but with Purpose
Refactoring can feel like cleaning out your closet—it’s necessary, but it’s easy to overdo it. Resist the urge to refactor for the sake of it. Instead, refactor when it adds real value, like improving readability, reducing complexity, or addressing tech debt that’s slowing your engineering team down.
One rule we’ve found helpful: if you touch code, leave it better than you found it. Even small changes, like renaming a variable or reorganizing logic, can make a big difference over time. And don’t forget to communicate with your engineering team—refactoring should never be a surprise in a code review.
6. Monitor Change Failure Rate to Spot Risky Areas
Another critical data-driven practice is monitoring your change failure rate (CFR)—the percentage of code changes that result in a rollback, bug, or hotfix. A high CFR often points to issues like insufficient testing, rushed changes, or unclear code.
For instance, if you notice repeated failures in one area of the codebase, it might be time to prioritize refactoring or add more rigorous test coverage. Pair this insight with developer feedback to understand whether the failures are process-related (e.g., rushed reviews) or code-related (e.g., fragile design).
Data like CFR doesn’t just help you fix what’s broken—it helps you identify patterns that can be proactively addressed.
7. Document for Clarity, Not Complexity
Documentation isn’t glamorous, but it’s a critical part of code quality. The trick is to document what matters without overwhelming your team. Focus on the “why” behind decisions, not just the “what.” For example, explain why you chose a particular approach or architecture instead of just describing how it works.
Good documentation doesn’t have to be long—it has to be useful. Whether it’s a simple comment in the code or a concise README, aim to make life easier for the next developer who touches that part of the system (even if that developer is future you).
8. Use Metrics to Guide Decisions, Not Just Measure Progress
Metrics like code coverage, cyclomatic complexity, and defect rates are valuable tools, but they’re not the end goal. The numbers should spark questions, not dictate decisions. For instance, high code coverage might look great on paper, but it’s meaningless if your tests aren’t catching real-world issues.
The real power of metrics lies in what they reveal. If cyclomatic complexity is climbing in a particular module, it’s a sign to dig deeper—Why is this happening? Is it due to rushed development or a poorly planned design? The goal isn’t to achieve a perfect score but to use metrics as a flashlight to highlight areas that need attention.
9. Know When to Prioritize Shipping Over Perfection
Sometimes, the best way to improve code quality is to recognize when “good enough” is enough. Not every piece of code needs to be pristine before it’s shipped. Striving for perfection can slow your team down and delay delivering value to users.
The trick is to make intentional trade-offs. If you need to ship something quickly, be clear about what’s being compromised and come back to address it later. Balance is everything—knowing when to push for higher quality and when to move forward is what separates great teams from ones stuck in endless loops.
10. Use Data to Improve Pull Request Practices
Metrics around pull request (PR) size, review time, and approval patterns can reveal a lot about your engineering team’s workflow. For example, PRs that are too large can slow down review cycles and lead to missed issues, while overly fast approvals might indicate reviews are being rushed.
Analyze data to find the sweet spot for PR size and review time. If reviewers consistently struggle with large PRs, encourage breaking changes into smaller, more digestible chunks. Likewise, track how long it takes for PRs to get picked up and ensure no one is overloaded with reviews.
Data-driven insights can help you establish healthier PR practices, reducing delays and improving the overall quality of changes.
Code Quality is Always Evolving
Great code doesn’t happen in isolation. It’s not just about perfectly formatted files or cleverly named variables. It’s about the practices around them—clear coding standards, thoughtful reviews, and smart use of data. Most importantly, it’s about the engineering team behind the code.
The reality is that no codebase is ever truly “done.” Even at companies like Google or Amazon, engineers face challenges every day. There’s legacy code that’s risky to touch, dependencies that fail at the worst moments, and last-minute fixes made under pressure. The key difference? These teams don’t try to eliminate chaos—they build systems and practices that help them manage it effectively.
The idea of a perfect codebase is a myth. But what really matters is having an engineering team that is flexible, resilient, and ready to adapt to whatever comes next - that’s where the true strength lies.
So, the question is: are you ready to build that kind of an engineering team? Because that’s where better code—and a better future for your engineering projects—begins.
