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Every software project needs code quality metrics to go by. Yet many teams use vanity KPIs or chase numbers that don’t reflect your product’s reliability, maintainability, efficiency, or security.
Our article focuses on real indicators that can help measure code quality. We will also share tips on how to assess code quality with these metrics and shed some light on common mistakes.
What Are Code Quality Metrics?
Code quality metrics are measurements that represent how “healthy” or “good” your code is.
Of course, good code quality is subjective to your standards. However, most companies aspire to write code that is understandable, consistent, bug-free, updatable, testable, reusable, well-documented, and secure.
Code is continuously written, rewritten, and reused, making software code quality assessment critical during the software development lifecycle.
Why developers should care about code quality metrics
Clear benchmarks help them enforce certain standards and quality controls. This is vital for teams working on any project, as it helps:
Uncover issues early and fix them before they affect other parts of the codebase.- Focus on key areas for improvement instead of spreading effort thin.
- Reduce the accumulation of technical debt by showing which parts of the code slow down or break the application.
- Make the code easier to understand and, therefore, debug and extend.
- Create a shared way for teams to gauge quality and remove guesswork.
- Keep the code-writing habits consistent across development teams.
- Verify that the new code is stable and secure enough for production.
- Gain objective data for refactoring, rewriting, and other software optimizations.
- Help explain quality issues to project managers and stakeholders.
17 Key Code Quality Metrics You Should Measure
You should measure code quality with quantitative and qualitative indicators. This section goes through these code quality metrics with examples.
Quantitative code quality metrics
Quantitative quality metrics are numeric signals that show the code’s performance, security, and maintainability. For example, how many bugs are open, what percentage of the lines are duplicated, and how many tests run successfully.
1. Cyclomatic complexity
Cyclomatic complexity shows how many decision paths your code takes, such as ‘if’ statements and loops. Teams rely on these code metrics to decide how much testing or cleanup a feature needs. Higher cyclomatic complexity values usually mean the code is harder to read, change, and test, while lower values point to simpler logic that behaves more predictably.
| Metrics | Meaning |
|---|---|
| Decision points | Decision choices that appear in the logic |
| Logical paths | How many outcomes can the program follow? |
| Testing baseline | Number of tests needed to verify all paths |
2. Code duplication
Duplication measures how much of your codebase has copy-pasted code or logic. You use this metric to check code quality for unnecessary complexity that can accumulate technical debt. Too much duplication increases maintenance work and complicates updates, since one fix must be applied in many places.
| Metrics | Meaning |
|---|---|
| Exact duplicates | Identical code that appears multiple times |
| Partial duplicates | Slightly varying flagged blocks with the same logic or purpose |
| Refactoring targets | Functions or modules that should be consolidated into shared functions |
3. Dead code
Dead code refers to the percentage of the code that doesn’t have a purpose: unused functions, outdated logic, and other leftover lines. Keeping dead code makes the codebase harder to navigate and may even introduce some risks to the product.
| Metrics | Meaning |
|---|---|
| Unused functions | Values and features that never execute |
| Obsolete logic | Logic that cannot run |
| Hidden risks | Inactive sections that could introduce errors if triggered |
4. Code coverage
Code coverage shows how much of your code runs during automated tests. Higher coverage usually means you test more paths and spot more defects, while low coverage means large areas of code require more assessments later. Ideally, you want the coverage as high as possible to release a high-quality product.
| Metrics | Meaning |
|---|---|
| Line coverage | Percentage of lines run during tests |
| Function coverage | Share of tested functions or methods |
| Branch coverage | Quantity of exercised decision outcomes (such as true and false paths) |
5. Bug density
Bug density measures how many defects appear for a given amount of code. Unlike the flat number of bugs, these coding metrics adjust for how much code you have in the module. You use bug density to compare releases, decide where to invest engineering time, or identify developers that fail to meet quality criteria.
| Metrics | Meaning |
|---|---|
| Bugs per size unit | Number of defects per set amount of code |
| Hotspot modules | Files or components that trigger issues |
| Trend over time | Bug density comparison across versions (to see if code quality improves or declines) |
6. Unit test pass rate
Unit test pass rate shows how many of your units run tests successfully. It’s like a quick health check for your source code quality and development skills. A high pass rate usually means recent changes reflect what the tests expect, while a low rate signals fresh defects or regressions that need attention.
| Metrics | Meaning |
|---|---|
| Overall pass rate | Share of all unit tests that succeed |
| Module pass rate | Rate of passed tests per module |
| Trend over time | Comparison of pass rates across releases |
7. Halstead complexity
Halstead complexity counts symbols in the code to estimate code complexity. It looks at how often your code uses operators (unique symbols, such as plus or equals) and operands (variable names or constants). Code with higher Halstead scores is harder to read, test, and modify. Development teams use these metrics to highlight risky functions for subsequent optimization.
| Metrics | Meaning |
|---|---|
| Distinct symbols | Total information content of the code |
| Token usage | Number of times symbols appear repeatedly |
| Derived effort index | Single value indicating the likely maintenance effort |
8. Function points
Function points represent how much business functionality your software delivers. This code review metric counts user-facing interactions. When used in conjunction with other metrics for code quality, you can scope new projects, estimate budgets, and assess the productivity output.
| Metrics | Meaning |
|---|---|
| Total function points | Functional size of the application |
| Function points per module | Functional parts per module |
| Productivity per point | Number of function points a team delivers over time |
9. WMFP
Weighted Micro Function Points (WMFP) measures how much backend work your code performs. Unlike the function points metric, it focuses on the internal technical effort that the user doesn’t see.
To measure these source code quality metrics, break the software into smaller operations. Then, assign each operation a weight based on complexity. This gives you clearer insight into hidden complexity that simple line counts or basic function points might miss.
| Metrics | Meaning |
|---|---|
| Total points | Totals the micro operations of the code |
| Per module | Number of operations per module |
| Refactoring priority score | Scores the likelihood of optimization |
10. Code churn
Code churn measures how much your codebase changes within a given time window. Every commit adds, removes, or rewrites portions of logic. This code review metric tracks these modifications, helping to point to unclear requirements, fragile design, or ongoing troubleshooting. Spikes in churn rates can indicate rushed fixes or messy architecture that forces repeat edits.
| Metrics | Meaning |
|---|---|
| Changed lines count | Number of added, deleted, or replaced code lines |
| Weekly churn volume | The total changes made in a week |
| Churn to defect ratio | Links churn levels to bug reports (to find fragile areas) |
11. LOC
Lines of code (LOC) simplify counting how many lines of source code your team has written. It doesn’t show if the code is good, but it helps see how big the system has become. Teams use LOC together with other metrics to judge whether the feature or module size meets the value it delivers and whether parts of the codebase grow faster than they should.
| Metrics | Meaning |
|---|---|
| Project size | Counts all lines of codebase |
| LOC per module | Line growth for different functions or modules |
| Change over time | New code introduced with releases |
Qualitative code quality metrics
Qualitative quality metrics are human observations and judgments. These include subjective opinions on whether the code is logical, understandable, stable, reusable, and secure.
12. Readability
Readability shows how easily another developer can make sense of the code. It requires adherence to coding standards and practices. Readable code directly influences its quality by reducing the risk of mistakes, speeding up code quality assessment, and improving handoffs between team members.
| Metrics | Meaning |
|---|---|
| Naming | Clarity of naming conventions |
| Structure | Formatting, indentation, and natural flow of sequence |
| Annotations | Explainability of decisions and edge cases |
13. Documentation
Documentation should capture the necessary knowledge needed to work on the code, explaining what the system does, how its pieces connect, and how to change it safely. You use documentation quality checks when preparing releases, maintaining older modules, or aligning work between teams.
| Metrics | Meaning |
|---|---|
| Clear purpose | Defines the goals of each module or function |
| Relevance | How often is the documentation updated |
| Helpful examples | Availability of usage samples and visual diagrams that expand on the knowledge |
14. Reliability
Reliability metrics for the source code tell how consistently the software does what it promises. It includes an assessment of how code behaves under normal load, edge cases, and minor faults. You should focus on these quality metrics all the time, especially in customer-facing applications and business-critical features.
| Metrics | Meaning |
|---|---|
| Incident frequency | How many failures happen over time |
| Error rate | Percentage of failed operations out of total operations |
| Uptime level | The time the service stays available and responsive |
| Recovery | How quickly the system recovers after incidents |
15. Performance
Performance metrics tell you how fast the system runs and how it consumes resources. Efficient code finishes work quickly with a minimal amount of memory, processing, and storage.
| Metrics | Meaning |
|---|---|
| Response time | Time to finish a request |
| Throughput | Number of operations the system can complete per second |
| Consumption | CPU, memory, storage, and token consumption for each unit of work |
16. Portability and reusability
Portability shows how easily your code runs on different machines, platforms, hosting models, and virtual environments. You should care about portability if you want to program in different operating systems or run software in several regions without massive reworking.
Reusability is a similar code quality measurement that shows if you can use the same logic to power multiple features. It usually requires code to be modular, expose stable interfaces, and have minimal dependencies.
| Metrics | Meaning |
|---|---|
| Container readiness | How easily the service runs inside containers |
| Dependency isolation | Whether libraries and tools can be swapped with minimal changes |
| Environment coverage | Compares code quality metrics across different platforms and setups |
17. Security
Cybersecurity metrics demonstrate your codebase’s protection against attacks. A secure system validates every input, encrypts sensitive data, stores secrets in vaults, and follows other principles, like the least privilege. You should focus on security throughout the product’s lifecycle, making it one of the most important code quality assessments.
| Metrics | Meaning |
|---|---|
| Access controls | Extent of restrictions for each role, permissions, and secrets |
| Security review coverage | Share of code paths passing through SAST, DAST, and similar checks |
| Time to patch issues | The time it takes to identify, fix, and deploy security patches |
But that’s not all. Knowing the software code quality metrics is only the starting point. You need a way to collect and apply them.
How to Measure Code Quality Metrics: Best Practices
To get trustworthy indicators and interpret them properly, you should use the right tools and practices.
- Tie metrics to actions: Set thresholds and standards for your application performance, so any deviations always trigger code audits.
- Start with the problem: If unsure where and how to check code quality, choose problems you want to remediate and focus on metrics that support this goal.
- Define owners for source code metrics: Each key code quality indicator should have a responsible stakeholder.
- Let tools handle the noise: Configure linters and formatters to handle small issues (like style fixes), so humans can concentrate on critical parts, such as logic and design.
- Standardize review checklists: To teach your teams how to evaluate code quality efficiently, create review checklists and guidance for interpreting metrics.
- Use generative AI smartly: According to the 2024 Accelerate State of DevOps Report, AI tools help increase code quality by 3.4% (among other benefits). However, these tools decrease delivery throughput and stability if not double-checked.
- Mark AI-generated changes: Tagging commits produced or edited by generative AI will help understand what to double-check during software code quality assessment.
- Benchmark against other apps: Compare your numbers against external data to see if your quality metrics represent industry standards.
Now, even this isn’t all there is to know about code quality metrics. They only work if you don’t misunderstand or overuse them.
Common Mistakes When Tracking Code Quality Metrics
Metrics become misleading when they are irrelevant, misinterpreted, or vague. Let’s see how you can avoid the most common tracking and evaluation mistakes.
| Mistake | Solutions |
|---|---|
| Focusing on vanity metrics instead of those that reflect code health | Remove low-value indicators in favor of code review metrics that influence your software quality and critical development decisions |
| Averaging data across the codebase and missing system-wide patterns | Break reports down by repository, bounded context, or team, and then compare metrics together on the same timeline to look for spikes |
| Relying too heavily on static analysis warnings and automatically approved pull requests | Pair surface-level checks by your continuous integration pipeline with deeper inspections for architectural decisions, internal logic, and production signals |
| Using metrics to score individual developers instead of system health | Focus on team-wide or codebase-level outcomes (such as reliability, maintainability, and stability) instead of forcing developers to pursue performance targets |
| Keeping outdated thresholds that don’t match the system | Review and update thresholds regularly to match the current architecture, tooling, team size, and workflows |
| Not updating software testing rules and review steps after incidents | Update review prompts, test files and rules, and quality gates after significant incidents to prevent recurring problems |
Quality metrics can still mean vastly different things, depending on who’s looking at them.
Code Quality Metrics in Different Contexts
Some indicators exist because a client, board member, or regulator expects to see them in a report. For example, they may insist on tracking test method case counts, pass rates, or defect removals. In regulated sectors, you may also have metrics that are fixed in service agreements.
The problem is that some of these indicators, on their own, are too shallow to steer design quality. Others can be harmful because they push teams to focus on numbers and inflate counts, hiding actual negative results.
To track metrics proposed by the higher-ups, but in a way that doesn’t hurt production, it’s necessary to:
- Link each metric to the decision it supports.
- Present the same code quality metrics to different stakeholders (technical leaders, product owners, etc.).
- Label metrics by purpose (for example, for business reports and for DevOps teams).
- Add notes on what each metric does not show, so stakeholders do not overreach in their conclusions.
- Propose removing metrics that reward number-chasing and delay necessary work.
Not every team can focus on the right metrics. And that’s why it makes sense to bring an experienced partner.
DevCom Can Help You Improve Code Quality
Metrics only help when they reflect the actual state of your codebase. When they become vague or misaligned, this may mislead your team and stakeholders.
A source code review can separate real data from noise and teach your team to do the same. It can also reveal hidden risks in architecture, testing, and release pipelines.
If you want stronger stability, maintainability, reliability, and security, reach out to DevCom.
FAQs
To assess code quality, you should collect valuable metrics that reveal your software’s health (stability, maintainability, performance, security vulnerabilities, and more). Tie every metric to an action and assign owners to each indicator. You should use AI tools to catch some mistakes, but tag AI-generated commits for double-checking.
No single metric defines code quality. You need to track a mix of quantitative and qualitative signals. Useful code quality metrics examples include cyclomatic complexity for logic clarity, code duplication for maintainability index, dead code for risk reduction, code coverage for test strength, bug density for defect patterns, and reliability for runtime health.
Track quality continuously to detect issues before they spread through the codebase. Automated tools can run on every commit and pull request, while engineers should review patterns during regular sprints. Schedule deeper audits at least once quarterly to reassess architecture, refactor risks, and test coverage.
AI tools can improve documentation, code clarity, and review speed, but they also lower delivery throughput and stability if unchecked. That’s why you should combine these tools with human oversight and guardrails.
