Engineering Productivity Metrics: A Guide to Boosting Team Performance

You're a software engineer. You're in the trenches. You're writing code. And you're probably feeling a little bit like a hamster on a wheel. All that hard work, and you're not sure if you're making any progress.

But don't worry, you're not alone. This is a common problem for software engineers. It's hard to know if you're being productive when you're just heads-down coding. And it's even harder to measure your progress when you're on a team.

That's where 'engineering productivity metrics' come in. Engineering productivity metrics are a set of metrics that can be used to measure the productivity of your team.

These metrics can help you to identify bottlenecks, track progress, and improve your team's overall performance.

Why are engineering productivity metrics important?

Imagine you're trying to build a house. You have a team of workers, but you don't know how to measure their progress.

You might be able to tell if they're working hard, but you don't know if they're working efficiently.

Are they wasting time on tasks that could be automated? Are they spending too much time on one task and neglecting others?

Without metrics, you're just flying blind.

The same goes for software engineering. Engineering productivity metrics give you the data you need to make informed decisions about your team's performance.

What are some common engineering productivity metrics?

There are many different engineering productivity metrics that you can track. Here are a few of the most common ones.

Cycle time is the amount of time it takes to complete a task, from the moment work begins to the moment the task is completed.

For example, the cycle time for a pull request is the time between when the pull request is created and when it is merged.

Lead time is the amount of time it takes to deliver a feature or fix a bug. This is essentially the time between the moment a task is requested and when it is delivered to the customer.

Throughput is the rate at which work is completed. This is essentially a measure of how many tasks are completed in a given period of time.

Deployment frequency is the number of times that code is deployed to production in a given period of time.

Mean Time to Restore (MTTR)

MTTR is the average time it takes to restore a system to operational status after a failure.