What are Qa Engineer metrics? Finding the right Qa Engineer 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 Qa Engineer 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 Qa Engineer metrics and KPIs 1. Release Frequency Measures the number of releases over a specific period. Indicates how quickly updates are being deployed.
What good looks like for this metric: 1-2 releases per month
Ideas to improve this metric Automate deployment processes Implement continuous integration/continuous deployment practices Invest in developer training Regularly review and optimise code Deploy smaller, incremental updates 2. Lead Time for Changes The average time it takes from code commitment to production release. Reflects the efficiency of the development pipeline.
What good looks like for this metric: Less than one week
Ideas to improve this metric Streamline workflow processes Use automated testing tools Enhance code review efficiency Implement Kanban or Agile methodologies Identify and eliminate bottlenecks 3. Change Failure Rate Percentage of releases that cause a failure in production. Indicates the reliability of releases.
What good looks like for this metric: Less than 15%
Ideas to improve this metric Increase testing coverage Conduct thorough code reviews Implement feature flags Improve rollback procedures Provide better training for developers 4. Mean Time to Recovery (MTTR) Average time taken to recover from a failure. Reflects the team's ability to handle incidents.
What good looks like for this metric: Less than one hour
Ideas to improve this metric Establish clear incident response protocols Automate recovery processes Enhance monitoring and alerts Regularly conduct disaster recovery drills Analyse incidents post-mortem to prevent recurrence 5. Number of Bugs Found Post-Release The count of bugs discovered by users post-release. Indicates the quality of software before deployment.
What good looks like for this metric: Fewer than 5 bugs per release
Ideas to improve this metric Enhance pre-release testing Implement user acceptance testing Increase use of beta testing Utilise static code analysis tools Improve requirement gathering and planning
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1. Page Load Time The time it takes for a web page to fully load from the moment the user requests it
What good looks like for this metric: 2 to 3 seconds
Ideas to improve this metric Optimise images and use proper formats Minimise CSS and JavaScript files Enable browser caching Use Content Delivery Networks (CDNs) Reduce server response time 2. Time to First Byte (TTFB) The time it takes for the user's browser to receive the first byte of page content from the server
What good looks like for this metric: Less than 200 milliseconds
Ideas to improve this metric Use faster hosting Optimise server configurations Use a CDN Minimise server workloads with caching Reduce DNS lookup times 3. First Contentful Paint (FCP) The time from when the page starts loading to when any part of the page's content is rendered on the screen
What good looks like for this metric: Less than 1.8 seconds
Ideas to improve this metric Defer non-critical JavaScript Reduce the size of render-blocking resources Prioritise visible content Optimise fonts and text rendering Minimise main-thread work 4. JavaScript Error Rate The percentage of user sessions that encounter JavaScript errors on the site
What good looks like for this metric: Less than 1%
Ideas to improve this metric Thoroughly test code before deployment Use error tracking tools Handle exceptions properly in the code Keep third-party scripts updated Perform regular code reviews 5. User Satisfaction (Apdex) Score A metric that measures user satisfaction based on response times, calculated as the ratio of satisfactory response times to total response times
What good looks like for this metric: 0.8 or higher
Ideas to improve this metric Monitor and analyse performance regularly Focus on optimising high-traffic pages Implement user feedback mechanisms Ensure responsive design principles are followed Prioritise backend performance improvement
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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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Tracking your Qa Engineer 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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