Engineering

Engineering Productivity Metrics Every Leader Should Monitor

Mikas Lukoševičius ·

Aspiring marketing specialist, eager to deliver quality + efficiency to your project management needs. 2024 - Present Junior marketing specialist at Teamhood

engineering productivity metrics

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. Although you might think measuring engineering productivity is challenging, we’re here to tell you that it’s definitely achievable. And how? By using the right metrics and insights. 

Stay tuned as we cover:

  • What is engineering productivity
  • Why you should measure engineering productivity
  • Useful engineering metrics you should track
  • Developer productivity measurement tools 
  • Common pitfalls when measuring engineering productivity 

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What is Engineering Productivity?

Engineering productivity evaluates the engineering efficiency of teams 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 that of 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%?

Summing up these statistics, it’s clear that 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

In this section, we’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. 

When it comes to how often you should track these metrics, it would depend. 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

Measured using Kanban metrics, cycle time refers to the span from the moment work begins on a task to its completion. This metric is instrumental in assessing the effectiveness of a team’s workflow. Lead time for changes refers to the duration from 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 the start and end times of tasks. You can track how long tasks take to complete, 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).

engineering productivity
Lead and cycle times viewed in Teamhood Kanban board

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 the speed at which 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.

engineering productivity
Gantt chart in software engineering

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 many times 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.

engineering productivity
Optimal pull request size graph (Source)

Code Review Time

Code review time is the duration it takes from the moment 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 the distribution and management of 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. 

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Resource Allocation in Teamhood

Common Pitfalls Associated With Engineering Metrics

Many engineering leaders fall into the trap of tracking the wrong engineering metrics. These metrics can distort the true picture of productivity and team dynamics and can only cause more setbacks than progress. 

To help you avoid this negative scenario, in the following section, we will explore some common traps you may come across 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 end up dealing with bad code and technical debt. For instance, let’s say your team is under pressure 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, which can then lead to customer complaints and a rushed patch-up job.

Measuring Individual Productivity 

Measuring individual productivity is another pitfall you want to avoid. Why? Because 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 really 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.

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 choosing the right metrics and using the best tools, you’ll find areas for improvement and push your team to new heights of efficiency and creativity.

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.

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Frequently asked questions

  • What are DORA metrics? 

    DORA metrics, developed by the DevOps Research and Assessment group, are four key performance indicators used to assess the effectiveness of software development and delivery processes. These metrics are Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Time to Restore Service. They provide insight into a team’s development and operational health.

  • What are some engineering productivity examples?

    Engineering productivity examples include automating build and deployment processes to reduce manual work, refactoring code to improve performance, enforcing code reviews to enhance quality, and adopting agile methodologies for flexible project management. These practices streamline workflows, increase output quality, and boost engineering efficiency.

  • What are some alternative metrics that focus on quality, effectiveness, and predictability? 

    Alternative metrics that emphasize quality, effectiveness, and predictability include Bug Rate, which tracks post-release issues, Customer Satisfaction scores reflecting user feedback on software quality, Team Velocity measuring work completed in a sprint for predictability, and Technical Debt Ratio which tracks long-term sustainability.

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