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Quality Assurance Engineers metrics and KPIs

What are Quality Assurance Engineers metrics?

Finding the right Quality Assurance Engineers 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.

You can copy these examples into your preferred app, or alternatively, use Tability to stay accountable.

Find Quality Assurance Engineers 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 Quality Assurance Engineers metrics and KPIs

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
defect-densitycode-coveragesoftware-developersquality-assurance-engineersdevelopment-teamsupport-team
Implement these metrics

Tracking your Quality Assurance Engineers 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.

Tability Insights DashboardTability's check-ins will save you hours and increase transparency

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:

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