How to Improve Code Quality with Code Review, Tests, and Automation

Improving code quality means creating a process where small changes, objective reviews, reliable tests, and automation before merge reduce bugs, rework, and technical risk in the team’s day-to-day work.

It sounds simple when written like that. In practice, this is where many teams get stuck.

Almost every team agrees that they want code that is easier to maintain. Few people want to work in a system where any small change requires understanding several parts of the codebase, review takes days, and the same bugs keep coming back in similar areas.

Even so, code quality often becomes a vague conversation. People talk about clean code, best practices, standards, architecture. All of that can help, of course.

But none of that solves much if the team’s real flow keeps pushing large PRs, superficial review, and late fixes.

The point I always come back to is this: code quality improves when the normal delivery process helps people write, review, and change code with less risk.

What is code quality?

Code quality is what allows a system to be understood, changed, reviewed, tested, and maintained without every change becoming too big of a risk.

In practice, quality code helps the team evolve the product with more confidence and less fear of breaking important behavior.

In day-to-day work, code quality shows up in very concrete signs:

• the code is easy for other people on the team to understand
• important behaviors have useful tests
• review does not depend on context that only one person knows
• basic problems are found before merge
• critical parts can be changed safely
• maintenance does not get harder with every new delivery

This definition is more useful than measuring quality only by style, formatting, or test coverage.

Those signals help, but they do not tell the whole story. What matters is whether the team can understand, review, test, and change the system safely.

What hurts code quality

A drop in quality almost never starts with a big and obvious problem. Most of the time, it declines through small day-to-day decisions.

A module becomes hard to understand, but still works. A test starts failing once in a while, so someone starts running only part of the suite. A PR mixes business logic with refactoring and structural changes. Review starts focusing on variable names because the bigger risks require too much context. After a few months, changing certain parts of the system becomes expensive.

When that happens, the problem enters the delivery rhythm. The team takes longer to review, spends more time fixing regressions, and starts avoiding important areas of the codebase.

Little by little, every change starts to feel riskier than it should.

That is a good sign that the team needs to look less at quality as a talking point and more at how code is written, reviewed, and taken to merge.

How to diagnose code quality problems?

Before changing tools or processes, the first step is to understand where quality is creating friction in the delivery flow. Some symptoms are easy to recognize.

team signal	what it may indicate	first action
PRs take days to get reviewed	the change is too large or the context is poorly explained	break PRs down and improve the change description
the same bugs come back in the same area	weak tests or design that is hard to change	protect the critical flow before refactoring
review discusses mostly style	missing automation or weak review criteria	move lint and formatting to CI and create a checklist
no one wants to touch a module	technical debt with real impact on the flow	map recent changes and start with small refactors
the pipeline passes, but bugs reach production	tests cover too little important behavior	review the suite based on the flows that break most often
repeated comments appear in every PR	a team rule still depends on manual attention	automate the check or document the criterion in review

This kind of diagnosis helps because it makes the conversation more concrete. It becomes easier to understand where quality is creating friction in the flow, increasing rework, or hiding risks.

Start with the delivery flow

If the goal is to improve code quality, the first place to look is the team’s workflow.

A few questions help a lot:

• are PRs small enough to be reviewed carefully?
• does the team have clarity on what should be evaluated in code review?
• are basic problems caught by automated checks before human review?
• do tests give confidence to change the code?
• do the areas that create the most rework show up in prioritization conversations?

If the answer is no to several of these questions, another page in the style guide is unlikely to solve it. The team needs to adjust the path a change goes through before reaching production.

Small PRs improve review

Large PRs make everything harder.

The problem is cognitive. After a few hundred lines, reviewers start losing context. It becomes harder to separate important decisions from small changes, and the real risk can be hidden among files that changed for different reasons.

One of the most practical ways to improve code quality is to reduce the size of changes. This may sound basic, but it changes the level of review a lot.

In practice, it helps to separate the work better:

• open PRs that solve one clear part of the problem
• avoid mixing broad refactoring with sensitive functional changes
• explain the context of the change in the PR itself
• break larger deliveries into steps that still make sense on their own

For example: a PR changes the discount rule, renames files, and refactors a class used in checkout.

Even with CI passing, the review becomes difficult because it mixes different risks. Validating the new discount rule is one thing. Reviewing a structural change in a sensitive part of the flow is another.

The better path would be to separate it: one PR for the rule, another for the structural cleanup, making clear in each one which flows were tested.

A smaller PR does not guarantee quality by itself. But it greatly increases the chance that someone will review it carefully.

How code review improves code quality

Many teams do code review every day and still let important problems slip through. Usually, this happens because each person reviews with a different yardstick.

One person looks at style. Another tries to redesign the solution. Another looks for bugs. Another just approves because the pipeline passed. The result is an inconsistent review, sometimes exhausting, sometimes shallow.

A good review needs clear criteria. It does not need to be bureaucratic for the team, but it needs to answer questions that actually protect the system:

• does the change solve the right problem?
• is the changed behavior clear for whoever will maintain this code later?
• is the merge risk acceptable?
• do the tests cover the main impact?
• is there any side effect in a sensitive area?
• does the new code make an already hard-to-maintain area worse?

This kind of question makes review more objective. The team starts discussing risk, clarity, maintenance, and impact, instead of focusing only on opinion or individual taste.

For me, that is where code review starts to truly improve code quality.

If the team does not have that alignment yet, the first step can be to create a code review checklist.

Use automation to remove repetitive work from review

Reviewers should not spend energy pointing out formatting, imports out of order, basic lint errors, or broken builds. This kind of problem needs to show up before review.

When the team automates repetitive checks, code review has less noise. Review can focus on what truly requires context: business rules, risk, architecture, maintenance, and the impact of the change.

Some checks usually make sense before merge:

• lint and formatting
• static analysis
• automated tests
• build validation
• basic security checks
• simple rules the team has already decided to follow

This also reduces friction in the team. When repetitive details are handled before review, the conversation between people is free to focus on what really needs attention.

Why test coverage is not enough

Tests show up in almost every conversation about code quality, and for good reason. The problem is that high coverage does not mean much if the tests do not protect the most important behaviors in the system.

The most useful question is: does this test suite help the team change the code with more confidence?

If the answer is yes, the suite is doing its job. If the answer is no, the team may be maintaining too many tests in unimportant places and too few tests in flows that break often.

In practice, good tests help protect:

• critical product flows
• business rules that change often
• areas where regressions have happened before
• contracts between parts of the system
• points that need safety for refactoring

Not every part of the system needs the same type of test.

Some changes need unit tests. Others need integration tests. In certain cases, one well-chosen end-to-end test is worth more than dozens of tests that only validate internal details.

Refactoring cannot always be left for later

When refactoring is always left for later, the code collects the debt at the worst possible moment. Usually when the team needs to ship quickly in an area that was already hard to change.

Refactoring does not need to happen only in large separate projects. It can enter the normal flow when the team:

• cleans up small pieces during related changes
• reduces duplication that is already getting in the way
• improves names and boundaries while the change is still clear
• isolates parts of the code that break often
• records larger debt when it does not fit in the current PR

Refactoring works best when maintenance enters the team’s normal work.

If it depends on “having extra time,” it will probably always be left for later.

Code quality metrics worth tracking

Code quality becomes abstract when no one can show where it improves or gets worse day to day.

You do not need to turn everything into a metric, but some signals help the team move away from generic perceptions and discuss more concrete problems.

I would look at things like:

• average review time by type of change
• amount of rework after review
• recurring bugs by system area
• PRs that often get stuck
• parts of the code that almost no one wants to touch
• repeated comments in code review
• test failures the team has learned to ignore
• complexity in files that change every week
• vulnerabilities or code smells found before merge

These signals do not tell the whole story, but they help prioritize.

When the team knows where bugs repeat, where review takes longer, and where rework appears often, the conversation about quality becomes much more concrete.

Did code quality get harder with AI?

AI does not make code worse by definition. What it changes is the speed and volume of code reaching pull requests.

This detail changes the team’s work a lot. If before the bottleneck was writing the first version of a change, now it is reviewing and validating what was generated.

A recent GitLab report showed this movement clearly: 78% of organizations said developers are writing and committing code faster with AI, but 85% said the bottleneck has shifted from writing code to reviewing and validating it. This is exactly the point where code quality starts to depend more on the process than on each dev’s individual speed.

The problem is not only when AI generates something clearly wrong.

The harder part is when the code seems to work, passes some tests, but introduces a poor design decision, a security flaw, an unnecessary dependency, or a rule that does not fit the rest of the system.

In practice, this means teams that use AI to write code need to pay even more attention to what happens before merge:

• PRs with clear context about what was generated or changed
• review focused on behavior, risk, and maintenance
• tests that cover the flows affected by the change
• static analysis and security checks in the pipeline
• human decision-making in the points where product and architecture context matter

AI can help a lot in development. But the more code it helps produce, the more important it becomes to have a reliable process for reviewing that code. Quality stops depending only on who wrote the change and starts depending on the system that validates the change before it enters the codebase.

When it makes sense to use a code quality tool or service

A code quality tool makes sense when the team already feels that manual review alone cannot handle the volume, repetition, or risk of changes.

Some signs are very clear:

• PRs pile up because few reviewers have enough context
• repetitive comments appear in almost every review
• bugs pass through review even with experienced people looking
• the team uses AI to generate code, but still does not have a good validation layer
• each squad applies different criteria to approve changes
• security, performance, or maintenance problems appear too late

In this scenario, static analysis, CI, tests, and AI review do not compete with each other. They complement each other.

Lint and build catch the basics. Tests protect important behaviors. Static analysis finds known patterns. And AI helps anticipate feedback in the pull request, calling attention to risks that deserve more careful review.

The care point is not turning the tool into another source of noise. If it comments too much, misses context, or enforces a rule the team does not use, devs start ignoring it. The best use is when the tool reinforces the team’s real criteria and reduces repetitive manual work.

This is where a tool like Kodus can help. It works in the pull request as a feedback layer before merge, finds risks that could go unnoticed, reduces repetitive comments, and gives reviewers more context, without replacing the technical decision of the people who maintain the system.

A practical path to start

If the team wants to improve code quality without creating a complex initiative, I would start small.

• agree on which problems code review needs to catch
• reduce the size of the PRs that are hardest to review
• automate repetitive checks before merge
• review whether the tests protect the flows that matter most
• map the areas that create more bugs, rework, or fear of change
• include small refactors in the team’s normal work
• test an AI layer in review to anticipate feedback

This kind of improvement usually works better when it shows up in the daily flow. If it becomes a side project, it is easy to lose priority in the first busier week.

Common questions about code quality

What is code quality?

Code quality is the ability of a system to be understood, changed, tested, and maintained with acceptable risk. Quality code allows the team to evolve the product without constant fear of breaking something important.

How do you improve code quality in a team?

The most practical path is to improve the delivery flow. Work with smaller PRs, define clear code review criteria, automate basic checks, write tests that protect important behavior, and treat refactoring as part of the normal work.

Does code review improve code quality?

Yes, when review has criteria.

A good code review helps find behavior, risk, clarity, and maintenance problems before merge. Without focus, review can become just a discussion about how each person would write the code.

Which metrics help track code quality?

Useful metrics include review time, rework after merge, recurring bugs by area, complexity in frequently changed files, ignored test failures, and repeated comments in PRs. The value is in using these signals to prioritize improvements, not in measuring everything just for the sake of measuring.

Do AI tools help with code quality?

They help when they reduce noise and anticipate feedback in the PR.

A tool like Kodus, for example, can point out problematic patterns, check team rules, and highlight risks before merge. Even so, the final decision still needs to stay with the team, because technical context and business context still matter.

Quality appears in the way the team works

Improving code quality depends on the set of choices the team repeats every week.

Smaller PRs, review with criteria, useful tests, automation before merge, and frequent refactoring help improve code quality day to day.

AI can also help, as long as it enters as support for the review flow, not as another source of noise.

If your team already reviews PRs every day but still lets risk slip through, it is worth looking at what happens before merge. Kodus helps anticipate feedback in the pull request, reduce repetitive comments, and give reviewers more context.

When these pieces start working together, quality stops depending only on each person’s individual attention.

It becomes part of the normal path between writing a change, reviewing it with criteria, and taking that change to production.