What are Reliability metrics? Finding the right Reliability 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 Reliability 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 Reliability metrics and KPIs 1. Response Time The time taken for a system to respond to a request, typically measured in milliseconds.
What good looks like for this metric: 100-200 ms
Ideas to improve this metric Optimise database queries Use efficient algorithms Implement caching strategies Scale infrastructure Minimise network latency 2. Error Rate The percentage of requests that result in errors, such as 4xx or 5xx HTTP status codes.
What good looks like for this metric: Less than 1%
Ideas to improve this metric Improve input validation Conduct thorough testing Use error monitoring tools Implement robust exception handling Optimize API endpoints 3. Request Per Second (RPS) The number of requests the server can handle per second.
What good looks like for this metric: 1000-5000 RPS
Ideas to improve this metric Use load balancing Optimise server performance Increase concurrency Implement rate limiting Scale vertically and horizontally 4. CPU Utilisation The percentage of CPU resources used by the backend server.
What good looks like for this metric: 50-70%
Ideas to improve this metric Profile and optimise code Distribute workloads evenly Scale infrastructure Use efficient data structures Reduce computational complexity 5. Memory Usage The amount of memory consumed by the backend server.
What good looks like for this metric: Less than 85% of total memory
Ideas to improve this metric Identify and fix memory leaks Optimise data storage Use garbage collection Implement memory caching Scale infrastructure
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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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Tracking your Reliability 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.
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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