What Developer Productivity Metrics Should An Engineering Manager Really Track?

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You as well as I know that most resources on developer productivity out there don’t really talk about the metrics that really matter.

Lines of code as a productivity metric is one such number that makes our blood boil!

And yes blowing off your top whenever you hear someone mention lines of code as a good metric is totally okay, you have our permission to go break things the next time it happens.

Alright, enough chit chat. Let’s get to the meat of this article.

You agree when we say that data holds the power to supercharge your software engineering team, right?

So, to take your team to dev productivity paradise you must focus on measuring data points that fuel efficiency, customer & developer satisfaction, and be default alignment with the business vision.

Superficial Measurement is a Blunder

Before we look at the good stuff, let’s quickly spend a minute or two on the metrics that are popular yet totally not the right way in 99.9% situations.

Here are a few that you can choose to not affect you & your software delivery team.

  • Raw Lines of Code: Measuring this rewards unnecessary complexity and bloat. Lean, well-architected code often has a smaller footprint. Focus on quality, maintainability, and code impact on core objectives.
  • Isolated Bug Counts: A simple bug count, without insights into severity and origin can tell a poor story. Use this as a starting point, tie in analysis of root causes, resolution time, and business impact to get a 4k picture.
  • Code Velocity: Delivery velocity is vital, but if we sacrifice quality then all this is of no use. Imagine removing test suites to improve velocity. It’s a nightmare!
    Monitor metrics like defect rates, test coverage & feedback along with velocity.

Leverage DORA Metrics

There are a few things that the developer community agrees with when it comes to performance analysis and productivity.

DORA is definitely one of those.

The DevOps Research and Assessment (DORA) framework provides a powerful toolkit.

Although I’m going to explain the DORA framework in brief below, you should still take time to check it out in-depth too.

We’ve got an article explaining the 4+1 DORA metrics that you can check out if you want to dive a bit deeper.

You can track your DORA metrics with Middleware easily as well and get great insights right within the dashboard saving you hours of effort.

Its four key metrics zero in on areas crucial for high-performing engineering teams.

  • Deployment Frequency: Track successful releases to production. Higher frequency allows rapid iteration, quicker response, and smaller-batch changes to isolate errors much faster.
  • Lead Time for Changes: Time from code commit to production shows bottlenecks. You can identify issues in review, test suites, or even environmental problems that affect a on time delivery.
  • Change Failure Rate: The percentage of failed feature releases is a direct quality indicator. You should look for root causes to fine-tune testing, CI/CD, and ensure safe, smooth updates.
  • Mean Time to Recovery (MTTR): Software delivery is a field where there is a new fire to put out every single day, and fast recovery signals healthy incident response. Improve monitoring, alerting, and rollback protocols to optimize for resilience.

Match Data Points With Business Results

The code our team writes is supposed to achieve business goals and not exist in isolation. This means the context in which the software delivery team works matters a lot.
Engineering leaders must understand how to tie specific business outputs with developer productivity metrics so things stay on track and on plan.


Here are a few examples:

  • Goal: Boost User Adoption & Engagementsome text
    • Metrics: Track feature usage, A/B tests to validate features, deployment impact on adoption rates, feedback channels for qualitative context.
  • Goal: Improve Customer Delight Through Reliabilitysome text
    • Metrics: MTTR highlights responsiveness, error-free deployment percentages, user support ticket patterns related to outages, good CFR helps with user trust & satisfaction.
  • Goal: Become a Top Employer of Software Engineering Talentsome text
    • Metrics: Cycle time for candidate feedback (speed vs. quality of hires), internal NPS tracking developer experience, tech debt impacting code agility.

Be Like Water My Friend

This field evolves faster than the speed of light and that means not just the developers but engineering managers must keep evolving as well.

Flexibility & adaptability is the name of the game.

And this applies to data and metrics as well.

An agile approach is key here. Here are a few things to keep in mind:

  • Regular Reassessment: Are your metrics truly driving the insights you need? Are they tied to the product roadmap?
  • Prioritize Data Quality: Automated collection and clear sources keep data clean and quality high. Flawed data can misalign you and your team. Leveraging tools like Middleware can help build a reliable data source of truth.
  • Team-Wide Transparency: What good are insights if no one else is able to see and leverage them? Sharing dashboards, discussing progress etc helps build data-driven decision-making across the team.

Get Better Insights with The Right Tools

Don’t shy away from leveraging tools and platforms that give you access to quality insights about your team.

Yes yes we know, no one wants one more app, one more tool but then again the statement is invalid if the tool is good and fits perfectly with the needs!

  • Middleware: Goes beyond surface-level data, analyzing workflows and hidden processes in real-time for targeted optimization opportunities including but not limited to DORA metrics.
  • Integrated Analytics: You don’t have to start at a 100. Just start with your existing tool stack - Jira, GitHub, and most development tools hold basic metrics with customization options. These might not be as in depth as Middleware but they are definitely going to be a good starting point.
  • Custom Surveys: Build a practice around leveraging specific team experience points that hard metrics might miss. Anonymity helps push honest feedback, leverage it.

TL;DR

Embrace the DORA framework, link data to objectives, and be open to adaptation. 

With a proper plan your software delivery team becomes a powerful engine for great product outcomes that align with the core business vision. Not Kidding.

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