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Engineering metrics and KPIs

What are Engineering metrics?

Crafting the perfect Engineering 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 Engineering 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 Engineering metrics and KPIs

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
code-qualitydeployment-frequencybackend-developerdevops-engineersoftware-engineeringquality-assurance
Implement these metrics

Tracking your Engineering metrics

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

Having a good strategy is only half the effort. You'll increase significantly your chances of success if you commit to a weekly check-in process.

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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