What are Code Quality metrics? Finding the right Code Quality metrics can be daunting, especially when you're busy working on your day-to-day tasks. This is why we've curated a list of examples for your inspiration.
Copy these examples into your preferred tool, or adopt Tability to ensure you remain accountable.
Find Code Quality metrics with AI While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI metrics generator below to generate your own strategies.
Examples of Code Quality metrics and KPIs 1. Code Coverage Measures the percentage of your code that is covered by automated tests
What good looks like for this metric: 70%-90%
Ideas to improve this metric Increase unit tests Use code coverage tools Refactor complex code Implement test-driven development Conduct code reviews frequently 2. Code Complexity Assesses the complexity of the code using metrics like Cyclomatic Complexity
What good looks like for this metric: 1-10 (Lower is better)
Ideas to improve this metric Simplify conditional statements Refactor to smaller functions Reduce nested loops Use design patterns appropriately Perform regular code reviews 3. Technical Debt Measures the cost of additional work caused by choosing easy solutions now instead of better approaches
What good looks like for this metric: Less than 5%
Ideas to improve this metric Refactor code regularly Avoid quick fixes Ensure high-quality code reviews Update and follow coding standards Use static code analysis tools 4. Defect Density Calculates the number of defects per 1000 lines of code
What good looks like for this metric: Less than 1 defect/KLOC
Ideas to improve this metric Implement thorough testing Increase peer code reviews Enhance developer training Use static analysis tools Adopt continuous integration 5. Code Churn Measures the amount of code that is added, modified, or deleted over time
What good looks like for this metric: 10-20%
Ideas to improve this metric Stabilise project requirements Improve initial code quality Adopt pair programming Reduce unnecessary refactoring Enhance documentation
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1. defect density Defect density measures the number of defects per unit of software size, usually per thousand lines of code (KLOC)
What good looks like for this metric: 1-5 defects per KLOC
Ideas to improve this metric Improve code reviews Implement automated testing Enhance developer training Increase test coverage Use static code analysis 2. code coverage Code coverage measures the percentage of code that is executed by automated tests
What good looks like for this metric: 70-80%
Ideas to improve this metric Write more unit tests Implement integration testing Use better testing tools Collaborate closely with QA team Regularly refactor code for testability 3. mean time to resolve (MTTR) MTTR measures the average time taken to resolve a defect once it has been identified
What good looks like for this metric: Less than 8 hours
Ideas to improve this metric Streamline incident management process Automate triage tasks Improve defect prioritization Enhance developer expertise Implement rapid feedback loops 4. customer-reported defects This metric counts the number of defects reported by end users or customers
What good looks like for this metric: Less than 1 defect per month
Ideas to improve this metric Implement thorough user acceptance testing Conduct regular beta tests Enhance support and issue tracking Improve customer feedback channels Use user personas in development 5. code churn Code churn measures the amount of code changes over a period of time, indicating stability and code quality
What good looks like for this metric: 10-20%
Ideas to improve this metric Encourage smaller, iterative changes Implement continuous integration Use version control effectively Conduct regular code reviews Enhance change management processes
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1. Defect Density Measures the number of defects per unit size of the software, usually per thousand lines of code
What good looks like for this metric: 1-10 defects per KLOC
Ideas to improve this metric Implement code reviews Increase automated testing Enhance developer training Use static code analysis tools Adopt Test-Driven Development (TDD) 2. Mean Time to Failure (MTTF) Measures the average time between failures for a system or component during operation
What good looks like for this metric: Varies widely by industry and system type, generally higher is better
Ideas to improve this metric Conduct regular maintenance routines Implement rigorous testing cycles Enhance monitoring and alerting systems Utilise redundancy and failover mechanisms Improve codebase documentation 3. Customer-Reported Incidents Counts the number of issues or bugs reported by customers within a given period
What good looks like for this metric: Varies depending on product and customer base, generally lower is better
Ideas to improve this metric Engage in proactive customer support Release regular updates and patches Conduct user feedback sessions Improve user documentation Monitor and analyse incident trends 4. Code Coverage Indicates the percentage of the source code covered by automated tests
What good looks like for this metric: 70-90% code coverage
Ideas to improve this metric Increase unit testing Use automated testing tools Adopt continuous integration practices Refactor legacy code Integrate end-to-end testing 5. Release Frequency Measures how often new releases are deployed to production
What good looks like for this metric: Depends on product and development cycle; frequently updated software is often more reliable
Ideas to improve this metric Adopt continuous delivery Automate deployment processes Improve release planning Reduce deployment complexity Engage in regular sprint retrospectives
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1. Code Quality Measures the standards of the code written by the developer using metrics like cyclomatic complexity, code churn, and code maintainability index
What good looks like for this metric: Maintainability index above 70
Ideas to improve this metric Conduct regular code reviews Utilise static code analysis tools Adopt coding standards and guidelines Refactor code regularly to reduce complexity Invest in continuous learning and training 2. Deployment Frequency Evaluates the frequency at which a developer releases code changes to production
What good looks like for this metric: Multiple releases per week
Ideas to improve this metric Automate deployment processes Use continuous integration and delivery pipelines Schedule regular release sessions Encourage modular code development Enhance collaboration with DevOps teams 3. Lead Time for Changes Measures the time taken from code commit to deployment in production, reflecting efficiency in development and delivery
What good looks like for this metric: Less than one day
Ideas to improve this metric Streamline the code review process Optimise testing procedures Improve communication across teams Automate build and testing workflows Implement parallel development tracks 4. Change Failure Rate Represents the proportion of deployments that result in a failure requiring a rollback or hotfix
What good looks like for this metric: Less than 15%
Ideas to improve this metric Implement thorough testing before deployment Decrease batch size of code changes Conduct post-implementation reviews Improve error monitoring and logging Enhance rollback procedures 5. System Downtime Assesses the total time that applications are non-operational due to code changes or failures attributed to backend systems
What good looks like for this metric: Less than 0.1% downtime
Ideas to improve this metric Invest in high availability infrastructure Enhance real-time monitoring systems Regularly test system resilience Implement effective incident response plans Improve software redundancy mechanisms
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1. Code Quality Measures the frequency and severity of bugs detected in the codebase.
What good looks like for this metric: Less than 10 bugs per 1000 lines of code
Ideas to improve this metric Implement regular code reviews Use static code analysis tools Provide training on best coding practices Encourage test-driven development Adopt a peer programming strategy 2. Deployment Frequency Tracks how often code changes are successfully deployed to production.
What good looks like for this metric: Deploy at least once a day
Ideas to improve this metric Automate the deployment pipeline Reduce bottlenecks in the process Regularly publish small, manageable changes Incentivise swift yet comprehensive testing Improve team communication and collaboration 3. Mean Time to Recovery (MTTR) Measures the average time taken to recover from a service failure.
What good looks like for this metric: Less than 1 hour
Ideas to improve this metric Develop a robust incident response plan Streamline rollback and recovery processes Use monitoring tools to detect issues early Conduct post-mortems and learn from failures Enhance system redundancy and fault tolerance 4. Test Coverage Represents the percentage of code which is tested by automated tests.
What good looks like for this metric: 70% to 90%
Ideas to improve this metric Implement continuous integration with testing Educate developers on writing effective tests Regularly update and refactor out-of-date tests Encourage a culture of writing tests Utilise behaviour-driven development techniques 5. API Response Time Measures the time taken for an API to respond to a request.
What good looks like for this metric: Less than 200ms
Ideas to improve this metric Optimize database queries Utilise caching effectively Reduce payload size Use load balancing techniques Profile and identify performance bottlenecks
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Tracking your Code Quality metrics Having a plan is one thing, sticking to it is another.
Don't fall into the set-and-forget trap. It is important to adopt a weekly check-in process to keep your strategy agile – otherwise this is nothing more than a reporting exercise.
A tool like Tability can also help you by combining AI and goal-setting to keep you on track.
More metrics recently published We have more examples to help you below.
Planning resources OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:
Tability's check-ins will save you hours and increase transparency
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