Your engineering org is hard at work delivering new features and hitting their targets on time. But you’ve recently heard devs saying their code is stuck, and you’re unsure where. 

You need data-driven insights into what your teams are working on so you can see the bottlenecks and make an impact. This will help your devs to ship higher quality software, faster. 

That’s where measuring software engineering productivity comes into play. Doing so will give you a clear overview of your teams’ processes, so you can identify where things need to improve and how. 

In this article, we’ll define productivity in engineering and how you can measure and improve it to help your engineering organization stay consistent and on top of its game.

Table of Contents

What Is Engineering Productivity?

Engineering productivity measures and optimizes the software engineering process and improves workflow efficiency. To do that, it focuses on three key goals:

  1. Identifying problems within your engineering process
  2. Reducing the time your engineering teams need to deliver new features
  3. Allowing for experimentation with different processes, techniques, and tools to continuously improve your engineering process

By focusing on these three goals, you’ll be able to improve your productivity in engineering. This means you’ll also boost the quality and speed at which you produce software with the same resources at your disposal. 

Engineering productivity measures and optimizes the software engineering process and improves workflow efficiency.

Why Is Measuring Engineering Productivity Important?

As your teams mature, your execs will likely expect them to ramp up the delivery of new software and features. They’ll also want you to get the momentum going to improve the company’s bottom line.

But if you begin without measuring software engineering productivity, you can hinder teams’ progress. Teams that aren’t working efficiently might look like they’re getting a lot of work done, but it won’t be high-quality work, and technical debt will pile up. And we all know tech debt is a productivity killer, because it results in:

  • More bug fixes for your teams to tackle
  • Lower code quality and design
  • Harder-to-debug code
  • Decreased overall happiness and job satisfaction


When you track key software engineering productivity metrics to spot and remove bottlenecks, your workflows will be more efficient and productive. This means your org will be able to do more without compromising quality. 

How Does Engineering Productivity Work?

Determining productivity in engineering starts with measuring data. When you measure important productivity metrics in software engineering, you can paint a better picture of how your teams perform and spot any issues along the way.

Once you’ve established a baseline of how your engineering org is functioning, you can compare your metrics with data from other top engineering orgs. LinearB’s research team studied nearly 2000 dev teams and compiled benchmarks you can use to help your teams reach elite status with more productive and efficient workflows. 

Pro Tip: Don’t push team members to improve every metric at once. Instead, focus on one metric at a time to foster healthy growth. 😉

Engineering benchmarks chartWant to learn more about being an elite engineering team? Check out this blog detailing our engineering benchmarks study and methodology.

How NOT to Measure Productivity in Software Engineering Teams

A quick internet search on measuring software engineering productivity will present you with varying methods, tools, and metrics. It can be time-consuming to sift through them and determine which engineering productivity metrics will add real value to your org.

To help you navigate all that noise, we’ve identified three of the most common mistakes to avoid making when you measure engineering productivity. 

1. Don’t Prioritize Speed

Some engineering organizations use velocity to estimate how much work their teams can do within a given timeframe. But developer velocity only means work is getting done — it doesn’t distinguish between planned and unplanned work or whether team members are working on the most important tasks. 

When measuring engineering productivity, don’t get stuck thinking the only thing that matters is how fast your teams get their work done. If this is all you focus on, you may end up releasing software that hasn’t been properly built or tested or isn’t actually fit for purpose.

Agile Velocity: The most dangerous metric for software developersFind out why agile velocity is the most dangerous metric for software developers. Look at 12 alternative metrics instead.

2. Don’t Measure Individual Productivity 

Many engineering organizations believe devs who complete lots of story points in a sprint are productive. But how one developer works differs from another, and you can’t hold junior devs to the same standard as senior devs. 

If you measure employee productivity individually, you can end up with data that doesn’t tell you what the real issues in your workflows or processes are. Instead, track productivity as a team dynamic to find the real bottlenecks clogging your pipelines.

3. Don’t Focus on Output-Based Metrics 

Output-based metrics only quantify the amount of work your teams do. So focusing on metrics like lines of code won’t help you understand what your teams are working on or whether their work aligns with business needs. 

Instead, focus on measurements that actually give you data on productivity. These metrics will help you know when you need to make changes and where to make them rather than leading you into a false belief that your teams are on track.

4 Best Productivity Metrics in Software Engineering

Metrics can make or break how you measure software engineering productivity. We’ve rounded up the four best software engineering productivity metrics that empower you to take action with insights into when and where your workflows need work. 

1. Merge Frequency

Your org’s merge frequency is the total number of pull or merge requests within a period of time. A high merge frequency indicates a healthy software delivery pipeline without PR review bottlenecks that frustrate developers and impact productivity. 

LinearB’s gitStream tool can help with this. Our tool makes the PR process more efficient by automating merge approvals for small and low-risk PRs while routing larger, more complex PRs to appropriate reviewers. This prevents the PR review process from getting stuck, ensuring your teams continuously merge.  

Want to learn how to implement custom dev workflow automation to cut code review time by up to 40%? Watch this hands-on workshop to set up the free gitStream tool to automatically:

:white_check_mark: Classify and route pull requests
:white_check_mark: Add estimated review time to PRs
:white_check_mark: Apply custom rules

gitStream Workshop: Getting started with workflow automation

gitStream product lead, Ofer Afias, will take you through a live demo of the three core functions when getting started with gitStream workflow automation to scale your team efficiently and improve your developer experience.

2. Cycle Time

Measuring your cycle times shows you where your teams are falling behind and where issues exist in your process. Shorter cycle times mean your processes are optimized and efficient. This can result in increased revenue and higher customer retention rates. Your devs will also be happier with a more efficient process. 

LinearB’s engineering productivity platform tracks cycle time across the four segments of the engineering workflow: coding, pickup, review, and deploy times. You can use this information to determine exactly where you need to make changes in the software development process. 

cycle time benchmarks

3. Planning Accuracy

Planning accuracy measures the amount of work completed in an iteration compared to the amount of work planned. Low planning accuracy indicates you might not have a clear idea of what your teams are working on. This will make it difficult to measure their productivity. We did the math and found out that the average accuracy of nearly 2000 teams is below 50%.

With LinearB, your engineering leaders can use the Project Delivery Tracker to view a planning accuracy breakdown chart. This will help them keep track of extra work added during a sprint, which contributes to inaccurate planning accuracy. With all your teams’ work — planned and unplanned — tracked, you’ll be able to improve planning accuracy over time.

planning accuracy over timeUsing LinearB’s Project Delivery Tracker can help you visualize unplanned work your team is doing each sprint and improve your planning accuracy. Book a demo today!

4. Resource Allocation

Resource allocation measures what your teams are working on and how much each dev is working. It’s an important metric because it helps you understand how your resources are being put to work, whether you have enough resources to complete your projects, and whether your resources are working on the most important projects. 

With LinearB’s Resource Allocation Dashboard, you can view your initiatives, full-time employee effort, and delivery status in a single location. Armed with this data, you can make changes where you notice issues negatively affecting productivity and your business goals. 

Finally, when you focus on the right software engineering productivity metrics, you’ll be able to guide your teams into a new golden age of development, centered around three key pillars: developer experience, engineering tempo, and business alignment. 

Check out this discussion with LinearB CEO, Ori Keren, where he talks about these new standards in measuring engineering productivity:


How to Improve Engineering Productivity

To improve your org’s engineering productivity, you’ll need accurate data on your teams and workflows. With this data, you’ll be able to detect and unblock process bottlenecks, clearing the road for your developers to work efficiently and productively.

The best platform for that is LinearB, built specifically to help make teams as efficient and productive as possible. You can also use our engineering benchmarks to see how your teams compare to other elite orgs and figure out where you need to make changes to improve.

And with our WorkerB bot and Team Goals, you can course-correct devs and improve your benchmarked metrics. WorkerB helps your teams reach their targets by automating tons of manual tasks and giving context to PRs, speeding up the review process. Team Goals then tracks your teams’ progress in reaching these targets over a sprint, week, or month so you can celebrate their wins and encourage continuous improvement.

Finally, with our workflow automation tool gitStream, you can empower your teams to streamline their PR processes. gitStream enables your teams to automate their PR routing and use policy-as-code to ensure small changes get merged quickly, while larger reviews can go to code review. And when your processes work smoothly, your org will achieve true productivity in engineering. 

We know that improving engineering efficiency is important. Learn more about our four-step recipe for improvement.