Code Quality Metrics: Tools, Challenges, and Best Practices

Your code is the foundation of everything you build. If it’s messy, inconsistent, and leads to technical debt, you’ll spend more time fixing problems than creating value. Poor code readability slows down your team; high code complexity makes even simple changes risky.

But when you track relevant metrics, you can spot issues before they become major roadblocks.

In this article, we'll show you how to improve software quality by focusing on key indicators that matter. You’ll learn to cut through the noise, improve your workflow, and build reliable software without unnecessary headaches.

Let’s get started.

What Is Code Quality?

Code quality measures how well your source code is written, structured, and maintained. High-quality code is readable, efficient, and easy to modify.

Good code meets code quality standards, reduces potential issues, and supports long-term development without unnecessary complexity. When your code is clean, your development team spends less time fixing bugs and more time building valuable features.

However, a study by Capers Jones analyzing over 12,000 software projects found that formal code reviews detected 60-65% of hidden defects, while informal reviews caught fewer than 50%. This means catching issues early through structured reviews and maintaining clean code can reduce errors before they reach production. Formal code reviews don’t catch 35-40% of hidden defects, so you need to constantly hone your code writing and review processes.

Here are the key properties that define code quality:

Readability

Your code should be easy to read and understand. If another developer looks at it and struggles to grasp it, the entire development process will slow down.

Besides, a study analyzing over 2.2 million lines of code found that better readability directly correlates with fewer defects. 

Here’s a side-by-side comparison of readable vs. unreadable code to highlight the importance of clarity and structure:

Unreadable Code (Hard to Understand)

python

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def c(p, t):

    s = 0

    for i in p:

        s += i

    return s + (s * t)

Why is this bad?

• Poor naming: Variable and function names are unclear (c, p, t, s).
• No documentation: There’s no explanation of what the function does.
• Hard to follow logic: The purpose of the calculation isn’t obvious.

Readable Code (Easy to Understand)

python

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def calculate_total_price(item_prices, tax_rate):

    """

    Calculate the total price including tax for a list of items.

    Parameters:

    item_prices (list of float): Prices of individual items.

    tax_rate (float): Tax rate as a decimal (e.g., 0.07 for 7% tax).

    Returns:

    float: Total price after tax.

    """

    subtotal = sum(item_prices)

    tax_amount = subtotal * tax_rate

    total_price = subtotal + tax_amount

    return total_price

Why is this better?

• Descriptive function name: calculate_total_price() explains what the function does.
• Clear variable names: item_prices, tax_rate, subtotal, and total_price improve clarity.
• Docstring included: The function properly explains parameters and return values.
• Logical structure: The calculation follows a straightforward, step-by-step approach.

Maintainability

Good code allows you to make changes without breaking functionality. If modifying one section causes unexpected failures elsewhere, your code base isn’t maintainable. Tracking complexity and keeping functions modular can help you avoid this problem.

Clear naming conventions, logical structure, and proper documentation also help make code more maintainable.

Example 1: Poor Naming Conventions

python

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def d(x, y):

    return x * y * 0.05

Why is this bad?

• The function name (d) is meaningless.
• Variables (x and y) do not indicate their purpose.
• The function's purpose isn't obvious without running it.

Example 2: Clear Naming Conventions

python

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def calculate_discount(price, discount_rate):

    """Calculate the discount amount based on price and discount rate."""

    return price * discount_rate

 Why is this better?

• The function name (calculate_discount()) tells precisely what it does.
• Variables (price, discount_rate) are descriptive.
• A docstring provides quick insight into the function's purpose.

Performance

Your code should run efficiently without consuming excessive resources. Poorly tuned algorithms, redundant operations, or unnecessary computations can slow execution.

Research on public datasets found that 74% of software couldn’t complete error-free when first executed. However, after an automatic code clean was applied, only 56% failed. That means clean, efficient code can prevent more errors.

Reliability

Reliable code consistently produces the expected outcome. If your software frequently crashes or behaves unpredictably, it impacts user trust and customer satisfaction. You can improve reliability by running unit tests, fixing defects early, and following good coding practices.

Speaking of good coding practices, let’s see:

What Are the Characteristics of Good Code?

Good code is clear, structured, and easy to modify without introducing new problems. If you or your team struggle to understand a piece of code, it’s a sign of bad code. On the other hand, well-written code helps you work faster, reduces errors, and improves overall software reliability.

Think about a simple function that calculates discounts. Debugging becomes frustrating if the logic is buried in complex code with unclear variable names and unnecessary conditions. But any developer can quickly understand and improve it if it’s well-structured with meaningful names and concise logic.

Good code also minimizes code duplication and keeps your project clean and efficient. When the same logic appears in multiple places, making updates becomes risky because you might fix a bug in one area but miss it elsewhere.

Regular code refactoring helps maintain consistency, while code quality checks catch hidden issues before they become an even bigger problem. Focusing on readability, structure, and maintainability allows you to write code that supports long-term success.

The Dangers of Low Code Quality

Low-quality code slows you down, increases costs, and creates endless frustration. Your project might suffer if your engineering team constantly fixes bugs instead of building new features. Messy, inconsistent code turns minor updates into significant risks, delays releases, and adds unnecessary stress.

Imagine inheriting a project filled with legacy code that lacks documentation. Every change feels like stepping into a minefield that forces you to spend hours deciphering outdated logic. This is what happens when code quality is ignored.

Here’s how it impacts your software development lifecycle:

• You’ll deal with more bugs: Messy code leads to more crashes, unpredictable behavior, and security risks. Instead of building new features, you’ll spend time fixing the same issues repeatedly.
• Fixing code will cost you more: You add to your long-term costs every time you push out sloppy code. The more time you spend fixing past mistakes, the less time you have for actual progress.
• Your software will be less reliable: If your app crashes or behaves unpredictably, users will leave. Poor quality means more frustration for you and lower trust from your customers.
• You’ll struggle to work efficiently: Spaghetti code and missing documentation make it harder for you and your team to collaborate. Onboarding new developers is going to be a nightmare.
• Releases will take longer: Instead of shipping features fast, you’ll waste time debugging. Bad quality code slows everything down and delays your product’s success.
• Technical debt will keep piling up: Quick, messy fixes might seem like shortcuts, but they only worsen things. Eventually, every update will feel like untangling a giant knot.

How to Review Code Quality

Reviewing code quality is essential to maintaining an efficient software development process and a maintainable codebase. Without regular checks, you risk introducing bugs, security vulnerabilities, and technical debt that can slow down your team.

A 2020 survey by SmartBear found that code reviews are the most effective way to improve software code quality, according to 24% of respondents. Unit tests ranked second at 20% and continuous integration third at 12%. Functional testing barely got 7% of votes, and static analysis was below 5%. 

Using the right review strategies, you can write cleaner, more reliable code while speeding up development. Here’s what we recommend to catch issues early and improve long-term stability.

Automated Quality Checks

Automated tools scan your code for errors, security flaws, and adherence to coding standards. These tools quickly detect code smells, syntax issues, and performance bottlenecks without manual input.

• Pros: You can instantly analyze large amounts of code and maintain consistency across your team.
• Cons: Automation doesn’t catch logic errors, and false positives can slow you down.

Despite their advantages, 71% of companies prefer manual reviews over automated ones, typically because they are unfamiliar with code quality tools. However, using both approaches together yields the best results.

Manual Reviews

Manual code reviews allow developers to check for logic errors, structure, and readability. Unlike automated tools, human reviewers understand the project context, making it easier to catch code duplication, poor design choices, and inefficient logic.

• Pros: Reviews encourage collaboration and knowledge sharing while identifying complex bugs.
• Cons: They can be time-consuming, potentially prone to errors, and thus subjective, depending on the reviewer’s experience.

However, well-done code reviews reduce code churn, improve team collaboration, and create a codebase that’s easier to maintain over time.

Static Analysis

Static analysis evaluates code without executing it and helps you catch issues early. Research shows that focusing on static analysis leads to early and cost-effective defect removal, which reduces both defect density and overall development effort.

• Pros: It identifies security vulnerabilities, type mismatches, and inefficient control flow before execution.
• Cons: It won’t catch runtime errors, and results sometimes need manual validation.

Integrating static code analysis into your workflow ensures you catch hidden problems before they escalate, making your software more stable and secure. To learn how this can help your team work better together, make sure to check out our post on aligning your development team with static analysis.

Key Questions to Ask When Reviewing Code

To assess whether your code is in good shape, ask yourself these questions:

1. Can someone new to the project read and understand this code easily?
2. If the original developer left, would the team still be able to modify and extend it?
3. Does this code follow best practices for code maintainability?
4. Is the code efficient for performance and scalability?
5. Has the code been tested for reliability and security?
6. Is there clear, updated documentation to support future development?
7. Is code refactoring happening regularly to prevent technical debt from piling up?

Code Quality Metrics

Tracking metrics for code quality helps you spot issues before they become significant problems. But if you try to follow every metric available, you’ll drown in data instead of improving your workflow. Some metrics focus too much on numbers, making you chase perfect scores instead of writing better code.

Axify takes a different approach. Instead of overwhelming you with dozens of metrics, it gives you big-picture insights and trends. While Axify doesn’t pinpoint or fix code quality issues directly, these trends highlight problems and risks in your software development life cycle. As such, you can use trend insights based on key metrics to consider potential solutions and where to implement them.

Big-Picture Software Code Quality Metrics

These metrics give you a high-level view of your team’s software quality and delivery performance. If these metrics show poor results, you can work to improve them and get better speed and quality. Therefore, you’re promoting better engineering practices.

1. DORA Metrics (For Delivery and Code Quality)

DORA metrics help you measure how efficiently and reliably your team delivers software. These focus on speed and indicate code maintainability and stability. Here are the four DORA metrics that you can track with Axify:

• Lead Time for Changes: This tracks the time from a change request in the dev environment to the feature entering production. A long lead time for changes suggests bottlenecks, review delays, or slow testing processes. Axify breaks the SDLC into sub-phases so you can see exactly where the slowdown happens.
• Deployment Frequency Measures how frequently code is deployed to the production environment. Infrequent deployments might indicate poor-quality or unstable code.
• Change Failure Rate: Tracks how many deployments cause failures in production. A high failure rate means your code isn’t production-ready, possibly due to rushed work or skipped tests.
• Failed Deployment Recovery Time or Mean Time to Recovery (MTTR): Measures how fast you fix failures. If your recovery time is long, your debugging process might be inefficient.

DORA metrics challenge your team to find the right balance between moving fast and maintaining high-quality code.

2. Pull Request Cycle Time

Your pull request (PR) cycle time shows how long it takes a PR to merge from the first commit on the branch. If this process drags on, it means reviews are slow or your team is stuck in a loop of back-and-forth changes.

Axify provides actionable insights into PR cycle time, helping you determine whether your review process is efficient or creates unnecessary delays.

3. Work in Progress (WIP) & PR Review Time

Too much work in progress (WIP) could hurt code quality. If your team juggles too many tasks, they might rush reviews or overlook quality issues. Axify helps you track WIP levels to prevent overload before it affects quality.

Similarly, PR review time is a key indicator. If reviews take too long, your team might struggle with bottlenecks in the review process. The most likely causes can be:

• High cognitive complexity: Reviewers need extra time to understand convoluted code.
• Large PR size: Bigger PRs take longer to review, increasing review fatigue.
• Lack of reviewer availability: Team members may be overloaded or have unclear review priorities.
• Poor documentation/comments: If the PR lacks context, reviewers may struggle to understand the changes.

What to fix? Encourage smaller PRs, improve documentation, and ensure review workloads are balanced.

4. PR Size as a Quality Indicator

Large PRs typically lead to lower-quality reviews. Reviewers are likelier to miss issues in a massive code block than in a small, focused PR. There’s even a saying:

So, encourage smaller, well-scoped PRs. They make reviews more thorough, reduce cognitive load, and help catch defects earlier—leading to higher-quality code and faster merges.

Granular Code Quality Metrics

While big-picture metrics help you track overall trends, granular code quality metrics give you a different look into your codebase’s structure, complexity, and maintainability.

These detailed metrics help you pinpoint specific issues and improve individual aspects of your code. However, tracking too many can overwhelm your team and slow down decision-making.

5. Cyclomatic Complexity

Cyclomatic complexity measures how many independent execution paths exist in your code. The more paths, the harder your code is to test, debug, and maintain.

Tom McCabe, who introduced this metric, outlined risk levels based on complexity scores:

• 1–10: Simple code with low risk
• 11–20: Moderately complex, requiring more testing
• 21–50: High complexity, difficult to maintain
• 50+: Extremely complex and nearly untestable

If your functions consistently score above 20, you might need to simplify your logic, break large functions into smaller ones, or refactor them to improve code readability.

6. Maintainability Index

This index rates how easy your code is maintained on a scale of 0 to 100. A higher score means the code is easier to update and extend. 

Studies show that up to 70% of a software project’s time and resources go into maintenance. So, the more maintainable your code is, the more resources you save.

If your maintainability score is low, you’re likely spending too much effort fixing past code instead of building new features. Regular code refactoring can help improve this score. To learn more about this, check out our guide on how to justify a refactor to see when and why refactoring is a wise investment.

7. Code Duplication

Duplicate code happens when the same logic appears in multiple places. While some duplication might be unavoidable, excessive copies make your code more challenging to maintain. If a bug exists in one place, it likely exists elsewhere, increasing the likelihood of errors.

Research shows that duplicated code is modified less frequently than non-duplicate code. The implication is that, while duplication doesn’t immediately make maintenance harder (since it's not often changed), it still poses long-term risks—because when changes are eventually needed, you might forget to update all instances, leading to inconsistencies and bugs. Using code analysis tools can help you detect and remove unnecessary duplication.

8. Code Churn Count

Code churn count tracks how often you rewrite or delete code shortly after writing it. While some churn is normal—especially during refactoring or iteration—excessive churn may indicate unclear requirements, design flaws, or second-guessing. If a file is frequently modified, investigating its design or the clarity of requirements can help stabilize the development process.

Monitoring churn allows you to identify areas that need better planning or more structured reviews.

9. Technical Debt

Technical debt is the hidden cost of quick fixes and shortcuts. The technical debt ratio represents the effort required to fix a messy codebase versus writing it correctly from the start.

Technical Debt Ratio = [Remediation Cost / Development Cost] X 100

A study found that engineers spend 33% of their time dealing with technical debt – time that could be better spent on new development. The more debt you accumulate, the more challenging future updates become. Addressing technical debt early prevents it from snowballing into a costly problem.

10. Test Coverage

Test coverage measures the percentage of your code that runs during testing, including unit, integration, functional, and end-to-end tests. Higher coverage means fewer untested areas, which can reduce the risk of errors. 

However, higher coverage doesn’t guarantee quality.

Even 100% coverage won’t catch every bug if your tests aren’t well-written. Instead of chasing a perfect percentage, you should focus on writing meaningful tests that catch real issues.

11. Customer-Reported Bugs

This metric tracks the number of defects users find after release. A high number suggests weak testing, poor validation, or rushed development. Keeping this number low means catching most problems in the software development life cycle before deployment.

A survey found that 88% of users will abandon their apps if they find bugs and glitches. So, fewer customer-reported bugs mean better customer expectations and trust in your software. This also saves your team time, as fixing issues post-release is typically more expensive.

12. User Satisfaction

All code quality efforts ultimately boil down to user experience. None of the metrics matter if your software is slow, buggy, or frustrating to use. User satisfaction is a key indicator of whether your code delivers real value.

Tips to Improve Code Quality

Improving code quality isn’t about chasing perfection but making your code easier to read, maintain, and scale. Small, consistent improvements help you write better code while reducing bugs and frustration. Here are some code quality metrics examples or tips to help you:

• Use S.P.I.D.R.: Have you ever worked on a user story so large that breaking it down felt like reading Atlas Shrugged? Keeping tasks and user stories small, independent, and well-scoped makes them easier to develop, review, and complete efficiently.

IMG: spidr methodology for splitting user stories

ALT: spidr methodology for splitting user stories and improve code quality

• Let automation do the heavy lifting: You don’t have to catch every code smell on your own. Automated tools can flag issues before they become real problems so you can focus on writing better code.
• Pair up for better code: Two sets of eyes are always better than one. Pair or mob programming helps you spot mistakes, improve logic, and share knowledge across your team. That’s why AI pair programming has become increasingly used lately.
• Refactor before it’s too late: If you avoid messy code today, you’ll regret it tomorrow. Regular code refactoring keeps your codebase clean and maintainable.
• Fix bugs first; ship features second: A zero-bug policy keeps your team from stacking bugs on top of unfinished work. Solve them early to save yourself headaches later (or decide not to fix them and simply discard them instead).
• Catch issues before they spread: Starting QA early prevents last-minute fire drills. The earlier you test, the fewer surprises you’ll face in production.
• Swarm on critical issues: When things break, don’t tackle them alone. Get the team together and solve significant problems faster with a collaborative "swarming" approach.

But even the best practices won’t help if you don’t have the right tools to track and improve your work. That takes us to our next point.

Improve Code Quality with Axify

Improving code quality means identifying slowdowns and maintaining clarity, consistency, and long-term maintainability. 

Axify helps you track key software delivery metrics like DORA, PR cycle time, and WIP, providing visibility into bottlenecks and trends that may contribute to inefficiencies. 

By analyzing trends across development workflows, teams can pinpoint patterns that correlate with potential quality issues and address them proactively. Instead of sifting through scattered reports, Axify delivers actionable insights that help optimize processes. 

Book a demo today and see how Axify enables smarter decision-making for a more efficient development lifecycle.