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13 examples of Software Quality metrics and KPIs

What are Software Quality metrics?

Crafting the perfect Software Quality metrics can feel overwhelming, particularly when you're juggling daily responsibilities. That's why we've put together a collection of examples to spark your inspiration.

Copy these examples into your preferred app, or you can also use Tability to keep yourself accountable.

Find Software 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 Software Quality metrics and KPIs

Metrics for Software Engineering Research

  • 1. Defect Density

    Defect density measures the number of defects confirmed in the software during a specific period of development divided by the size of the software.

    What good looks like for this metric: Less than 1 defect per 1,000 lines of code

    Ideas to improve this metric
    • Implement peer code reviews
    • Conduct regular testing phases
    • Adopt test-driven development
    • Use static code analysis tools
    • Enhance developer training programmes
  • 2. Code Coverage

    Code coverage is the percentage of your code which is tested by automated tests.

    What good looks like for this metric: 80% - 90%

    Ideas to improve this metric
    • Review untested code sections
    • Invest in automated testing tools
    • Aim for high test case quality
    • Integrate continuous integration practices
    • Regularly refactor and simplify code
  • 3. Cycle Time

    Cycle time measures the time from when work begins on a feature until it's released to production.

    What good looks like for this metric: 1 - 5 days

    Ideas to improve this metric
    • Streamline build processes
    • Improve collaboration tools
    • Enhance team communication rituals
    • Limit work in progress (WIP)
    • Automate repetitive tasks
  • 4. Technical Debt

    Technical debt represents the implied cost of future rework caused by choosing an easy solution now instead of a better approach.

    What good looks like for this metric: Under 5% of total project cost

    Ideas to improve this metric
    • Regularly refactor existing code
    • Set priority levels for debt reduction
    • Maintain comprehensive documentation
    • Conduct technical debt assessments
    • Encourage practices to avoid accumulating debt
  • 5. Customer Satisfaction

    Customer satisfaction measures the level of contentment clients feel with the software, often gauged through surveys.

    What good looks like for this metric: Above 80% satisfaction rate

    Ideas to improve this metric
    • Gather feedback through surveys
    • Implement a user-centric design approach
    • Enhance customer support services
    • Ensure frequent updates and improvements
    • Analyse and respond to customer complaints

Metrics for Assessing software quality

  • 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

Metrics for Quality and Reliability

  • 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

Metrics for Tracking Quality of Code

  • 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

Metrics for Backend Developer Performance

  • 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

Metrics for Backend Developer Performance

  • 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

Metrics for Quality Assurance Performance

  • 1. Defect Density

    Defect density measures the number of defects found per size of the module or product, typically per thousand lines of code.

    What good looks like for this metric: 0.5 to 1.0 defects per 1,000 lines of code

    Ideas to improve this metric
    • Improve code review processes
    • Invest in training for the QA team
    • Enhance documentation and coding standards
    • Implement automated testing tools
    • Focus on early detection during development
  • 2. Test Case Effectiveness

    Test case effectiveness measures the percentage of test cases that result in the discovery of defects.

    What good looks like for this metric: 70% to 90%

    Ideas to improve this metric
    • Regularly update test cases based on past defects
    • Incorporate exploratory testing techniques
    • Enhance collaboration between QA and development teams
    • Use risk-based testing strategies
    • Implement comprehensive test case reviews
  • 3. Test Coverage

    Test coverage is the percentage of covered functionalities or code lines during the testing process.

    What good looks like for this metric: 70% to 80%

    Ideas to improve this metric
    • Increase automated test coverage
    • Regularly assess test suite effectiveness
    • Identify gaps in existing test cases
    • Refactor tests to cover untested areas
    • Adopt code coverage analysis tools
  • 4. Defect Resolution Time

    Defect resolution time tracks the average time taken to fix a reported defect and retest it.

    What good looks like for this metric: 1 to 7 days

    Ideas to improve this metric
    • Prioritise defects based on severity and impact
    • Streamline communication between QA and development teams
    • Foster a proactive defect management approach
    • Implement a robust defect tracking tool
    • Provide clear instructions in defect reports
  • 5. Customer Reported Defects

    Customer reported defects measure the number of defects found by customers after release.

    What good looks like for this metric: 0.2% to 1% of total defects

    Ideas to improve this metric
    • Conduct thorough user acceptance testing
    • Involve customer feedback in the testing process
    • Implement rigorous pre-release testing
    • Regularly update the testing approach with customer insights
    • Establish a continuous feedback loop with end-users

Metrics for Software Feature Completeness

  • 1. Feature Implementation Ratio

    The ratio of implemented features to planned features.

    What good looks like for this metric: 80-90%

    Ideas to improve this metric
    • Prioritise features based on user impact
    • Allocate dedicated resources for feature development
    • Conduct regular progress reviews
    • Utilise agile methodologies for iteration
    • Ensure clear feature specifications
  • 2. User Acceptance Test Pass Rate

    Percentage of features passing user acceptance testing.

    What good looks like for this metric: 95%+

    Ideas to improve this metric
    • Enhance test case design
    • Involve users early in the testing process
    • Provide comprehensive user training
    • Utilise automated testing tools
    • Identify and fix defects promptly
  • 3. Bug Resolution Time

    Average time taken to resolve bugs during feature development.

    What good looks like for this metric: 24-48 hours

    Ideas to improve this metric
    • Implement a robust issue tracking system
    • Prioritise critical bugs
    • Conduct regular team stand-ups
    • Improve cross-functional collaboration
    • Establish a swift feedback loop
  • 4. Code Quality Index

    Assessment of code quality using a standard index or score.

    What good looks like for this metric: 75-85%

    Ideas to improve this metric
    • Conduct regular code reviews
    • Utilise static code analysis tools
    • Refactor code periodically
    • Strictly adhere to coding standards
    • Invest in developer training
  • 5. Feature Usage Frequency

    Frequency at which newly implemented features are used.

    What good looks like for this metric: 70%+ usage of released features

    Ideas to improve this metric
    • Enhance user interface design
    • Provide user guides or tutorials
    • Gather user feedback on new features
    • Offer feature usage incentives
    • Regularly monitor usage statistics

Metrics for AI in Assignment Rubrics

  • 1. Time Saved Creating Rubrics

    The amount of time saved when using AI compared to traditional methods for creating assignment and grading rubrics

    What good looks like for this metric: 20-30% time reduction

    Ideas to improve this metric
    • Automate repetitive tasks
    • Utilise AI suggestions for common criteria
    • Implement AI feedback loops
    • Train staff on AI tools
    • Streamline rubric creation processes
  • 2. Consistency of Grading

    The uniformity in applying grading standards when using AI-generated rubrics across different assignments and graders

    What good looks like for this metric: 90-95% consistency

    Ideas to improve this metric
    • Use AI for grading calibration
    • Standardise rubric templates
    • Provide grader training sessions
    • Incorporate peer reviews
    • Regularly update rubrics
  • 3. Accuracy of AI Suggestions

    The correctness and relevance of AI-generated rubric elements compared to expert-generated criteria

    What good looks like for this metric: 85-95% accuracy

    Ideas to improve this metric
    • Customise AI settings
    • Review AI outputs with experts
    • Incorporate machine learning feedback
    • Regularly update AI models
    • Collect user feedback
  • 4. User Satisfaction With Rubrics

    The level of satisfaction among educators and students with AI-created rubrics in terms of clarity and usefulness

    What good looks like for this metric: 70-80% satisfaction rate

    Ideas to improve this metric
    • Conduct satisfaction surveys
    • Gather and implement feedback
    • Offer training on rubric interpretation
    • Enhance user interface
    • Continuously update rubric features
  • 5. Overall Cost of Rubric Creation

    Total expenses saved by using AI tools over traditional methods for creating and managing rubrics

    What good looks like for this metric: 10-15% cost reduction

    Ideas to improve this metric
    • Analyse cost-benefit regularly
    • Leverage cloud-based AI solutions
    • Negotiate better software licensing
    • Train in-house AI experts
    • Integrate AI with existing systems

Metrics for Evaluating Test Performance

  • 1. Test Coverage

    Measures the percentage of the codebase tested by automated tests, calculated as (number of lines or code paths tested / total lines or code paths) * 100

    What good looks like for this metric: 70%-90% for well-tested code

    Ideas to improve this metric
    • Increase automation in testing
    • Refactor complex code to simplify testing
    • Utilise test-driven development
    • Regularly update and review test cases
    • Incorporate pair programming
  • 2. Defect Density

    Calculates the number of confirmed defects divided by the size of the software entity being measured, typically measured as defects per thousand lines of code

    What good looks like for this metric: Less than 1 bug per 1,000 lines

    Ideas to improve this metric
    • Conduct thorough code reviews
    • Implement static code analysis
    • Improve developer training
    • Use standard coding practices
    • Perform regular software audits
  • 3. Test Execution Time

    The duration taken to execute all test cases, calculated by summing up the time taken for all tests

    What good looks like for this metric: Shorter is better; aim for less than 30 minutes

    Ideas to improve this metric
    • Optimise test scripts
    • Use parallel testing
    • Remove redundant tests
    • Upgrade testing tools or infrastructure
    • Automate test environment setup
  • 4. Code Churn Rate

    Measures the amount of code change within a given period, calculated as the number of lines of code added, modified, or deleted

    What good looks like for this metric: 5%-10% considered manageable

    Ideas to improve this metric
    • Emphasise on quality over quantity in changes
    • Increase peer code reviews
    • Ensure clear and precise project scopes
    • Monitor team workload to avoid burnout
    • Provide comprehensive documentation
  • 5. User Reported Defects

    Counts the number of defects reported by users post-release, provides insights into the software's real-world performance

    What good looks like for this metric: Strive for zero, but less than 5% of total defects

    Ideas to improve this metric
    • Enhance pre-release testing
    • Gather detailed user feedback
    • Offer user training and resources
    • Implement beta testing
    • Regularly update with patches and fixes

Metrics for Feature Completeness

  • 1. Feature Completion Rate

    The percentage of features fully implemented and functional compared to the initial plan

    What good looks like for this metric: 80% to 100% during development cycle

    Ideas to improve this metric
    • Improve project management processes
    • Ensure clear feature specifications
    • Allocate adequate resources
    • Conduct regular progress reviews
    • Increase team collaboration
  • 2. Planned vs. Actual Features

    The ratio of features planned to features actually completed

    What good looks like for this metric: Equal or close to 1:1

    Ideas to improve this metric
    • Create realistic project plans
    • Regularly update feature lists
    • Adjust deadlines as needed
    • Align teams on priorities
    • Open channels for feedback
  • 3. Feature Review Score

    Average score from review sessions that evaluate feature completion and quality

    What good looks like for this metric: Scores above 8 out of 10

    Ideas to improve this metric
    • Provide detailed review criteria
    • Use peer review strategies
    • Incorporate customer feedback
    • Holistic testing methodologies
    • Re-evaluate low scoring features
  • 4. Feature Dependency Resolution Time

    Average time taken to resolve issues linked to feature dependencies

    What good looks like for this metric: Resolution time within 2 weeks

    Ideas to improve this metric
    • Map feature dependencies early
    • Optimize dependency workflow
    • Increase team communication
    • Utilise dependency management tools
    • Prioritize complex dependencies
  • 5. Change Request Frequency

    Number of changes requested post-initial feature specification

    What good looks like for this metric: Less than 10% of total features

    Ideas to improve this metric
    • Ensure initial feature clarity
    • Involve stakeholders early on
    • Implement change control processes
    • Clarify project scope
    • Encourage proactive team discussions

Metrics for Software Team Productivity

  • 1. Velocity

    Velocity measures the average amount of work a team completes during a sprint, typically calculated by story points.

    What good looks like for this metric: 40-60 story points per sprint

    Ideas to improve this metric
    • Break down tasks into smaller units
    • Improve sprint planning accuracy
    • Conduct regular retrospectives
    • Improve skills through training
    • Use more efficient tools
  • 2. Cycle Time

    Cycle time is the total time from the beginning to the end of a process, measuring how quickly a team can deliver features.

    What good looks like for this metric: 1-2 weeks

    Ideas to improve this metric
    • Optimize workflows to remove bottlenecks
    • Implement automated testing
    • Limit work in progress
    • Conduct frequent code reviews
    • Improve team collaboration
  • 3. Code Quality

    Code quality measures how easy and bug-free the code is, typically tracked through defect rates and code reviews.

    What good looks like for this metric: Less than 5% defect rate

    Ideas to improve this metric
    • Implement continuous integration
    • Use static code analysis tools
    • Conduct code reviews regularly
    • Promote pair programming
    • Standardise coding practices
  • 4. Deployment Frequency

    Deployment frequency is the rate at which a team deploys or releases software, indicating the team’s ability to deliver updates.

    What good looks like for this metric: Weekly deployments

    Ideas to improve this metric
    • Automate deployment processes
    • Use feature flags
    • Ensure robust testing environments
    • Implement continuous delivery strategies
    • Train team on deployment procedures
  • 5. Customer Satisfaction

    Customer satisfaction measures how pleased customers are with the software, often tracked using Net Promoter Score (NPS) or customer feedback.

    What good looks like for this metric: NPS score of 50+

    Ideas to improve this metric
    • Gather regular customer feedback
    • Incorporate user-friendly features
    • Ensure high software reliability
    • Implement better customer support
    • Regularly update software documentation

Metrics for Reprocessed Plastic Recycling

  • 1. Annual Sales Volume

    The total quantity of plastic products sold within a year

    What good looks like for this metric: 10,000 MT in 2025, increasing to 50,000 MT by 2035

    Ideas to improve this metric
    • Expand market reach through marketing
    • Increase product quality to boost sales
    • Enhance sales team training and incentives
    • Identify and target key industries needing plastic
    • Collaborate with international partners
  • 2. Production Yield

    The percentage of produced items that meet quality standards

    What good looks like for this metric: 95% in 2025, aiming for 99% by 2035

    Ideas to improve this metric
    • Implement quality checks at each production phase
    • Invest in modern machinery and technology
    • Train employees on quality control processes
    • Conduct regular maintenance on equipment
    • Incorporate lean manufacturing practices
  • 3. Customer Retention Rate

    The percentage of customers who continue to buy over time

    What good looks like for this metric: 80% in 2025, increasing to 95% by 2035

    Ideas to improve this metric
    • Enhance customer service and support
    • Implement a loyalty program
    • Regularly seek customer feedback for improvements
    • Offer personalized deals and discounts
    • Ensure high product quality and consistency
  • 4. Cost per Metric Tonne (MT)

    The cost incurred to produce one metric tonne of plastic

    What good looks like for this metric: 10% reduction by 2026, aiming for 20% reduction by 2035

    Ideas to improve this metric
    • Streamline procurement processes
    • Negotiate better deals with suppliers
    • Optimize production scheduling for efficiency
    • Minimize waste during production
    • Utilize energy-efficient machinery
  • 5. Training Hours per Employee

    The average number of hours each employee spends in training annually

    What good looks like for this metric: 20 hours in 2025, increasing to 60 hours by 2035

    Ideas to improve this metric
    • Develop a comprehensive training calendar
    • Encourage online and external training sessions
    • Introduce mentorship programs
    • Link training to career development plans
    • Utilize technology for training modules

Tracking your Software Quality metrics

Having a plan is one thing, sticking to it is another.

Setting good strategies is only the first challenge. The hard part is to avoid distractions and make sure that you commit to the plan. A simple weekly ritual will greatly increase the chances of success.

A tool like Tability can also help you by combining AI and goal-setting to keep you on track.

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Planning resources

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