Engineering Productivity Metrics You Must Know

Vidas Vasiliauskas · Updated on July 22, 2025

Every engineering leader wants their team to deliver high-quality software quickly. But you probably already know this! Yet, a report from Stripe shows that developers only spend about 55% of their time on real development. The rest goes to meetings, maintenance, and fixing previous shortcuts. Plus, 57% of engineers think they could be working more efficiently.

One way you can improve engineering productivity metrics is to measure the productivity of your engineering team. Measuring engineering productivity may seem complex, but it becomes manageable with the right metrics and tools.

Stay tuned as we cover:

What is engineering productivity
Why you should measure engineering productivity
What are the most effective engineering metrics to improve team productivity?
Developer productivity measurement tools
Common pitfalls when measuring engineering productivity

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What is Engineering Productivity?

Engineering productivity evaluates teams’ engineering efficiency in producing quality software. It looks at the speed of development, the quality of code, and how resources are managed. To boost productivity, teams pinpoint and fix bottlenecks, embrace new technologies, and improve teamwork.

Why Should You Measure Engineering Productivity?

If you’re still in two minds about whether you should or shouldn’t measure engineering productivity at your company, let us share some important statistics with you.

According to the 2021 State of DevOps Report, high-performing teams deploy code 46 times more frequently than their lower-performing counterparts. The same report has found that the fastest teams have a lead time for changes that is 2,555 times quicker than their counterparts.

And get this: Elite teams experience a change failure rate seven times lower than their less efficient peers.

If that doesn’t confirm the importance of measuring engineering productivity, what if we tell you that addressing technical debt and improving developer tools can enhance productivity by up to 42%?

These statistics sum up: Engineering productivity can lead to quicker market delivery, an exceptional customer experience, and increased revenue. At the same time, you’ll optimize costs and adapt to market demands faster.

That’s why it’s so important for all engineering leaders, including scrum masters and project managers, to track engineering productivity within their teams.

Useful Engineering Metrics Tracked Within Organizations

This section’ll examine some of the most important engineering metrics your organization should track. We’ll explain what each of the metrics signifies and offer some tips for tracking them.

It depends on how often you track these metrics. Some should be tracked in real time, and others during regular check-ins.

With that out of the way, let’s dive in.

Lead and cycle time

Cycle time, measured using Kanban metrics, refers to when work begins on a task and it is completed. This metric is instrumental in assessing the effectiveness of a team’s workflow. Lead time for changes refers to when code is initially committed to a version control system to when it is successfully deployed to production.

How to track it: Use project management tools that let you record task start and end times. You can also track the time it takes to complete tasks, which will allow your team to detect inefficiencies and make necessary adjustments to speed up processes.

One tool you can use is Teamhood. Teamhood allows you to automatically calculate lead and cycle times for each task by using its online Kanban board. This feature provides a clear overview of the average time taken from when a task appears in the backlog to its delivery (lead time), and the time spent actively working on it (cycle time).

Change Failure Rate

Change failure rate is the percentage of deployments that fail in production, which leads to incidents like outages, rollbacks, or hotfixes. It’s a key indicator of the reliability of your release process and code quality.

How to track it: To track this metric, you should monitor and record the outcomes of each deployment, categorizing them as successes or failures. Analyzing these results over time allows you to calculate the failure rate. Lower rates indicate more stable and reliable deployments, while higher rates may signal the need for improvements in testing, development practices, or deployment procedures.

Mean Time to Restore (MTTR)

Mean Time to Restore (MTTR) measures how your team can resolve a production failure and restore service to its normal operating condition. It’s a critical metric for assessing your team’s response effectiveness to incidents and outages.

How to track it: To track this, you log the time an incident occurs and the moment service is fully restored. This data is then analyzed to determine the average recovery time. Faster restoration times are indicative of a team’s strong incident management capabilities and resilience, while longer times may highlight areas for improvement in your response strategies or infrastructure robustness.

Deployment Frequency

Deployment frequency is measured by how often your team deploys code to production. It reflects the agility and efficiency of your release process. When you deploy frequently, it suggests your workflow is streamlined and effective, which allows for quick feature updates and bug fixes.

How to track it: You can track this metric by using deployment tools that log each time code is deployed. By keeping an eye on the frequency of deployments, you gain insights into your team’s productivity and their capacity to quickly adapt to market changes or customer requirements.

Code Churn

Code churn refers to the percentage of a developer’s own code that has been modified or deleted within a recent period after its initial submission. It indicates the code’s stability and the engineering efficiency of the development process. High churn rates may suggest indecision, unclear requirements, or technical debt being addressed.

How to track it: To track code churn, use version control systems to compare the amount of code changed, added, or removed against the total codebase over time. This metric helps identify areas where the development process can be improved.

Merge Frequency

Merge frequency refers to how often code changes are merged into the main branch of a version control system. This metric indicates the pace of a team’s integration and delivery practices.

How to track it: Employ version control tools to monitor how often code is merged within a set timeframe. Analyzing these merge events enables teams to evaluate their continuous integration process and pinpoint ways to improve teamwork and shorten the time needed for integration.

Pull Request Size

Pull request size measures the average volume of code changes submitted for review in a pull request. Smaller, more manageable pull requests are often associated with faster review times, easier understanding, and fewer errors.

How to track it: To track this metric, you can use tools integrated with your version control system to aggregate the total lines of code (LOC) added, modified, or deleted across pull requests over a period. Analyzing this data helps maintain an optimal size for pull requests.

Code Review Time

Code review time is the duration from when a pull request is opened until it is fully reviewed and approved. This metric is important for understanding the engineering efficiency of your team’s code review process. Shorter review times can indicate a streamlined workflow, whereas longer times may point to bottlenecks.

How to track it: To track code review time, use project management tools or features within your version control system to record the timestamp when a pull request is made and when it receives final approval.

Resource Allocation

Resource allocation refers to allocating tasks and responsibilities among team members so that each individual’s skills and capacities are used effectively for project success. It’s about matching the right people with the right tasks while balancing workloads to prevent burnout and underutilization.

How to track it: To track resource allocation and monitor workload management, you can use project management tools that offer visibility into who is working on what and their current workload. These tools often allow managers to assign tasks, set priorities, and monitor progress.

Common Pitfalls Associated With Engineering Metrics

Many engineering leaders mistakenly prioritize the wrong metrics, such as focusing solely on speed or individual output, which can obscure underlying issues like technical debt or teamwork inefficiencies. These metrics can distort the accurate picture of productivity and team dynamics and can only cause more setbacks than progress.

To help you avoid this negative scenario, the following section will explore some common traps you may encounter in measuring engineering productivity and highlight how they can lead to misguided efforts and outcomes.

Prioritizing Speed

If you focus too much on speed, you’ll deal with bad code and technical debt. For instance, let’s say your team is pressured to release a new feature quickly, so they skip essential steps like code reviews or comprehensive testing. This rush can result in a feature that’s full of bugs, leading to customer complaints and a rushed patch-up job.

Measuring Individual Productivity

Measuring individual productivity is another pitfall you want to avoid. It can promote a competitive environment and undermine teamwork and collaboration.

Let’s say there’s a developer on your team who spends extra time mentoring teammates but whose code output is lower because of this. Although their contribution is critical to the team’s success, traditional productivity metrics say they’re one of the weakest team members.

Focusing on Output-based Metrics

Relying too much on output-based metrics like lines of code overlooks the complexity and quality of work. Consider a scenario where one of the developers produces thousands of lines of code per month, but most of their code is redundant and inefficient. This is a metric you want to skip measuring because innovative solutions require fewer lines but more thought.

Not Balancing Objective Versus Subjective Measurements of Developer Productivity

Not balancing objective and subjective measures of developer productivity can distort understanding. Imagine if you only counted the number of tasks completed (objective data) without considering how hard the tasks were or how they affected the team (subjective feedback). This approach can give you a misleading idea of how productive your team is, and you’ll miss the full story of their work.

Developer Productivity Measurement Tools

Throughout this article, we mentioned how you can measure engineering productivity by using a variety of tools. In this section, we’ll introduce some of the most commonly used and highly regarded tools in this domain so that you can choose the ones that best fit your needs. Learn more about Project Management Tools for Software Developers.

Code Analysis Tools

SonarQube: Analyzes and visualizes code quality.
Codacy: Automates code reviews and monitors code quality.
Coverity: Identifies vulnerabilities in the codebase.

Project Management Tools

Teamhood: Assists leaders in managing various engineering departments and offers the clarity and collaboration needed for strategic planning and operational management within their teams.
Jira: Tracks and manages software development projects.
Trello: Organizes tasks on a Kanban board for better visibility.
Asana: Helps teams coordinate and manage workflow and projects.

Version Control Systems

GitLab: Offers source code management and CI/CD tools.
GitHub: Hosts code and supports version control and collaboration.
Bitbucket: Provides Git repository management and collaboration tools.

Issue-tracking Systems

Teamhood: Seamlessly unite your team’s efforts, conquering tasks and tracking progress with precision.
Jira Software: Manages software development tasks and bug tracking.
Bugzilla: Tracks bugs and issues within a development project.
Redmine: A flexible project management web application with issue tracking.

Time Tracking Tools

Teamhood: Teamhood is one of the best time-tracking software for engineers. Here, you can track or manually enter the time spent on each task, review and edit time logs, and leave comments on specific time logs when needed.
Toggl: Tracks time spent on projects and tasks with detailed reporting.
Harvest: Offers time tracking with invoicing and reporting features.
Clockify: A free time tracker and timesheet app for teams.

Integrated Development Environments (IDEs)

Visual Studio Code: Provides built-in support for version control and debugging.
IntelliJ IDEA: Offers coding assistance and productivity features.
Eclipse: An IDE that supports a variety of programming languages and tools.

Conclusion

We hope this deep dive into engineering productivity has shed some light on how to boost your team’s performance. By identifying bottlenecks and optimizing workflows, your team can reduce lead times by 30% and deploy changes faster, directly impacting customer satisfaction and ROI.

Keep in mind that improving productivity is a never-ending journey. It’s all about staying alert to any setbacks along the way and being ready to try out new ideas. With the insights from this article, you’re well on your way to leading your team to achieve and even exceed your project and company goals.