8 Code Quality Metrics Every Engineering Team Should Track

Software that works today but breaks tomorrow slows every team down. Bugs pile up, changes become risky, and developers spend more time fixing problems than building new features. With AI-generated code in the picture, things have only gotten more complicated, with studies suggesting that roughly 12% of AI‑generated code contains identifiable security vulnerabilities.

Code quality matters. It’s about more than just clean code or personal preference: it affects how quickly teams can ship, how reliable their software is, and how easy it is to maintain. Low-quality software can be a major risk factor. Security breaches, endless bug fixes, and frustrated users are only some of the hidden costs of neglecting code quality.

The good news is that code quality can be measured. Metrics like complexity, duplication, maintainability, and test coverage help teams understand the health of their codebase.

In this article, we'll delve into the essential metrics of human-written and AI code quality, providing practical tips and examples to build software that's functional, sustainable, and a joy to work with.

What Is Code Quality?

Code quality is the degree to which a codebase meets the standards and expectations of its developers, users, and stakeholders.

A codebase with predominantly low-quality code can result in an inefficient use of resources and exposure to software attacks from malicious actors. The effects of low-quality code damage the reputation and trust of the software and its developers, ultimately leading to customer dissatisfaction and revenue loss.

On the other hand, a high-quality codebase prioritizes best practices, which helps promote the efficient use of resources. High-quality code is also easier to read, understand, and modify, reducing the likelihood of errors and making it more adaptable to change.

High code quality directly influences software reliability and performance, significantly impacting the end-user experience.

Investing in code quality is an investment in the long-term success of software.

How to Measure Code Quality: 7 Key Metrics

Code quality metrics help evaluate the overall health of a codebase by indicating whether it meets established quality standards. Monitoring code quality aids in spotting possible problems early on in the development process, avoiding them from developing into serious ones later.

Code quality analysis also helps enhance the codebase's overall structure, making collaboration easier for engineering teams. Here are seven essential metrics to track.

1. Cyclomatic Complexity Metrics

Cyclomatic complexity metrics measure how complex a program is based on the number of linearly independent paths (decision points). Simply said, it keeps track of how many decision paths your code contains.  

The more control flow (if/else) statements a method contains, the higher the code’s complexity value. Functions with a high cyclomatic complexity are more difficult to test and more likely to have defects.

The code snippet above calculates the maximum of three numbers by comparing them using if/else statements. The cyclomatic complexity of the code snippet above is five. There are four decision points (two if statements nested inside each other twice) and one additional path (the exit path after all comparisons).

As you will observe, having too many if/else statements makes the code unnecessarily complex and challenging to read. Also, changes in one of the conditionals can affect the logic of the rest of the code.

We can improve the code quality by reducing the cyclomatic complexity to one using the built-in Python max function, as shown below.

You can automate tracking and measuring cyclomatic complexity in your codebase using code analysis tools like Codacy. With Codacy, you can identify complex functions and refactor them for better readability and maintainability.

2. Code Churn Metrics

Code churn measures the code added, modified, or deleted over time. It indicates how stable or volatile the codebase is and how much effort teams spend maintaining or updating it.

High code churn indicates code that is constantly changing, which may indicate poor design choices, unclear requirements, or engineers not following best practices.

Tracking the code churn metric helps developers identify and address the root causes of frequent changes, improving overall code quality.

For example, consider this Git log of a project:

This project has relatively high code churn, with 460 lines added and 160 lines deleted across five commits. This stat may indicate that feature A was not well-planned, tested, or implemented, and that the project is unstable and prone to errors.

Tracking code churn and setting a baseline for your organization can help your team improve the development process by writing clearer user stories, writing unit tests, and performing code reviews before merging.

3. Code Coverage Metrics

Code coverage quantifies the percentage of your codebase covered by automated tests. It helps you assess how well your test suite covers your source code and identifies the areas that need more testing. Additionally, when code changes are made, code coverage reports help ensure that any modifications introduced do not unknowingly introduce new issues.

A code repository is considered to have high code coverage if the percentage of covered lines by automated tests exceeds a predefined threshold. Aim for near-complete test coverage (think 80% and above) for ultimate peace of mind.

There are several types of code coverage metrics, including function coverage, statement coverage, branch coverage, condition coverage, and line coverage. Codacy uses line coverage, which measures the percentage of lines of code that are covered by automated tests.

Generally, higher code coverage means greater confidence in your code's reliability and functionality, and a higher likelihood of identifying bugs before they reach production. Lower code coverage increases the risk of bugs and errors.

Some popular code coverage tools in 2026 include JaCoCo for Java, Istanbul/nyc for JavaScript and TypeScript, coverage.py for Python, Coverlet for .NET, and llvm‑cov/gcov for C and C++.

You can also use code coverage tools to generate reports and visualize the code coverage data, which can help you improve your testing strategy and prioritize your testing efforts.

4. Code Security Metrics

Code security metrics measure how resilient your codebase is to attacks and risks. It indicates how well the code protects the software's data and functionality from unauthorized access, modification, or destruction.

Code security is essential for software reliability, as it helps prevent breaches, errors, and failures that can compromise the integrity, availability, and confidentiality of the software and its users. Poor code security can result in severe consequences, such as data loss, reputation damage, legal liability, and other sorts of accidents.

High code security means that the code follows the best practices and standards for software security and secure coding, such as the OWASP Top 10, and that it is free of vulnerabilities and flaws, such as SQL injection, broken authentication, sensitive data exposure, cross-site scripting, and insecure deserialization,  that can be exploited by malicious actors.

You can employ automated code security tools to scan code for potential vulnerabilities early in the development process, generate reports, and visualize the security data, which can help you improve your security strategy and prioritize your security efforts.

By keeping track of your code’s security scope, you can ensure that your software is secure and compliant with security requirements and regulations. 

5. Code Documentation Metrics

Code documentation metrics measure the amount and quality of the documentation accompanying the code. It indicates how well-documented the code is and how easy it is for other developers and users to understand and use.

Writing code documentation is a key part of software development, as it makes code clearer, easier to maintain, and more collaborative among team members. Code documentation explains the logic, functionality, and usage of the code, which is important for comprehending, updating, and enhancing software projects.

High code documentation (>80%) means the code has consistent comments and comprehensive, up-to-date documentation that covers its purpose, functionality, and usage. This can significantly enhance the project’s understandability and maintainability, as well as facilitate knowledge transfer and troubleshooting.

On the other hand, low- or zero-code documentation means the code lacks sufficient comments and documentation that explain its logic and design. This can lead to several problems, such as:

• Difficulty in reading and comprehending the code, especially if it is complex or poorly written
  
  
• Greater risk of bugs or unexpected behavior due to hidden dependencies or unclear logic
  
  
• Reduced productivity and efficiency as developers may spend more time trying to figure out the code than working on new features or improvements
  
  
• Collaboration challenges when team members interpret the code differently or struggle to reuse it
  
  
• Knowledge loss when developers leave without documenting their work, leaving behind difficult-to-maintain code

Static code analysis tools like Codacy can help track methods and classes without the correct comment annotations, helping you identify areas that need better comments.
Codacy also provides code quality metrics and suggestions to improve your code documentation and readability. By using Codacy, you can ensure your code is well-documented and follows best practices.

6. Code Duplication Metrics

This metric measures the amount of code that is repeated or copied across different parts of the codebase. It indicates how well your codebase follows the DRY (Don't Repeat Yourself) principle and how efficient it is. High code duplication means the code has many redundant or unnecessary parts, increasing the code size, complexity, and maintenance effort. 

Example:

Before:

After:

7. Code Bug Issues or Defect Metrics

Code bug issues metrics measure the number of bugs or defects found in code per unit of code size (e.g., lines of code or function points). It indicates how reliable and error-free the code is.

Issues checked by this metric include:

• Code style: Code formatting and syntax problems, such as variable name style and enforcing the use of brackets and quotation marks
  
  
• Error-prone: Code that may hide bugs and language keywords that should be used with caution, such as the operator == in JavaScript
  
  
• Performance: Code that can have performance problems
  
  
• Compatibility: Mainly for frontend code, compatibility problems across different browser versions
  
  
• Unused code: Unused variables and methods
  
  
• Security: All security problems

A high code bug density indicates that the code contains many bugs or defects, affecting its functionality, performance, and security. By tracking code bug density, developers can monitor code quality and prioritize the issues that need to be resolved.

8. AI Code Quality Metrics

When developers use AI to build an ever-growing share of your codebase, engineering leaders need new ways to measure and govern the code their teams produce. Traditional code quality metrics, like those we have seen so far, remain important, but AI code introduces additional dimensions of risk and observability.

AI code quality metrics include:

• AI policy compliance and risk exposure: Measures how well AI-generated code adheres to security policies, passes checks, and avoids introducing vulnerabilities or unsafe dependencies. Codacy handles this with the AI Risk Hub.
  
  
• Review coverage of AI code: The extent to which AI code has been scanned, flagged, or auto-fixed. It guarantees the uniform application of both automated and human checks. For timely AI code reviews, see Codacy’s AI Reviewer.
  
  
• Complexity and maintainability: The difficulty in reading and maintaining AI code due to duplication, verbose logic, or poor structure. It helps prevent technical debt accumulation as AI use grows.
  
  
• AI-specific security risks: Potential vulnerabilities, risky hard-coded secrets, invisible code injections, or unreviewed dependencies introduced by AI code. It reduces the risk of breaches or license violations. For this, Codacy offers AI Policies.
  
  
• Policy compliance enforcement: The degree to which AI code adheres to internal and regulatory standards. It minimizes compliance gaps and guarantees evidence that is audit-ready.
  
  
• Test coverage of AI code: Share of AI code that is covered by automated tests. It keeps regressions from happening and guarantees production readiness.

Codacy provides engineering leaders with a cohesive picture of code health by combining these AI-specific metrics with conventional quality, security, and coverage metrics.

Track Your Code Quality with Codacy

Understanding your code’s quality is indispensable, but what really makes metrics actionable is automating their measurement and integrating AI code insights as a first-class consideration. In the age of AI, tracking AI code quality is essential for engineering leaders who want a complete picture of their repositories.

Codacy supports engineering leaders in tracking both traditional (issues, complexity, duplication, and code coverage) and AI-specific dimensions in a single unified dashboard, providing a complete picture of code health across the repositories built by their teams.

The platform evaluates branches and files based on issues, complexity, duplication, and test coverage, while also incorporating AI-related insights such as visibility into how that code is reviewed and flagged under policy, maintainability signals, and AI-related security risks. These insights are surfaced directly in IDEs and pull requests, where developers receive actionable feedback and apply guardrails to AI-generated code. Regardless of whether code is created by humans or AI tools, teams can identify hidden risks early, avoid technical debt, and ensure internal policy compliance by tracking them together.

Each repository receives a clear A-F grade calculated from these metrics, which provides a quick view of overall quality and highlights areas that need attention. AI-informed trends show how human and AI contributions affect the codebase over time, assisting teams in prioritizing fixes, suggesting improvements for functions that would benefit from refactoring, and upholding standards without delaying delivery.

Instead of piecing together reports from multiple tools, you can see your team’s metrics in one dashboard with ad-hoc fix suggestions, coverage reports, and AI code review insights all in one place. Book a demo to see Codacy in action.