12 developer productivity metrics you need to measure

Maximizing software developer productivity is crucial for businesses with engineering teams to maintain a competitive advantage in today’s fast-paced tech landscape. Engineering managers must track key metrics beyond simple measures like lines of code written or bug fix rate, focusing on a comprehensive set of indicators encompassing code quality, developer velocity, and the entire developer experience.

By leveraging industry-standard metrics, such as deployment frequency, change lead time, and code review quality metrics, companies can start to gain valuable insights into their software development process and identify areas for improvement.

These metrics, combined with qualitative feedback from developer surveys and input from engineering leaders, enable organizations to streamline their software delivery process, improve developer satisfaction, and align engineering efforts with key business outcomes.

We’ll explore 12 essential developer productivity metrics that leading tech companies like Uber, Etsy, and Google use to benchmark their engineering effectiveness and drive continuous improvement in their software engineering processes.

What is developer productivity?

Developer productivity encompasses the efficiency, effectiveness, and satisfaction of building software among developer productivity teams. It involves the speed at which code progresses from development to production, the quality and stability of delivered software, and the overall developer experience.

DevProd isn’t solely about individual metrics; it includes collaboration within dev teams, streamlined workflows, and utilizing proper tools and processes for efficient code review, deployment, and project management. High productivity enables developers to deliver high-quality features swiftly, tackle challenges effectively, and stay motivated within a nurturing engineering environment. To effectively measure developer productivity, frameworks such as SPACE consider factors like individual performance and team dynamics by blending quantitative metrics with qualitative insights.

Why should you measure developer productivity?

You must effectively measure developer productivity to understand and optimize your engineering organization’s operations.

Assess team-level efficiencies and developer experience to identify bottlenecks, inefficiencies, and areas for improvement within your engineering cultures. Use insights from quantitative measures like bug count, code snippets, and qualitative assessments from random samples to develop actionable strategies for enhancing productivity.

By factoring in the expense of developer happiness and considering difficult developers, you can refine workflows, improve developer satisfaction, and align efforts with business priorities. Early identification of obstacles to productivity enables better resource allocation, investment in practical developer tools, and overall enhancement of the software delivery process.

Employing developer survey programs provides valuable input for defining and measuring developer productivity. Surveys ensure that leaders and developers contribute to developing a comprehensive developer productivity list that drives continuous improvement and maintains a competitive edge. Ultimately, this holistic approach leads to faster feature delivery, higher-quality code, and a more motivated development team, all crucial to a successful business strategy.

Top developer productivity metrics

Change failure rate

The change failure rate metric assesses the percentage of production changes that lead to service degradation or outages, offering insight into testing and code review processes. For instance, in an e-commerce platform, a new payment gateway deployment might cause checkout issues, or an infrastructure update could inadvertently block traffic, leading to outages. By tracking the proportion of changes resulting in problems, teams can pinpoint areas where testing or review practices need improvement.

Monitoring this metric enables teams to refine their testing and deployment processes, reducing the frequency of service disruptions. Suppose you detect a high failure rate for a particular change type. In that case, individual teams can enhance automated testing, conduct more thorough code reviews, or implement safer deployment strategies like phased rollouts and feature toggles. Organizations can ensure reliable systems and an enhanced user experience by lowering the change failure rate.

• Who’s measuring: Lattice and Amplitude use variations of this single metric. Lattice measures the number of PagerDuty incidents divided by the number of deployments.
• Benefits: This metric provides insights into deployment stability and helps identify opportunities to deliver more reliable code.

Developer satisfaction score (NSAT/CSAT)

The developer satisfaction score is a vital developer productivity measurement, reflecting developers’ perceptions of tool quality, work environment, and daily processes.

In a tech startup, for example, outdated version control systems or cumbersome deployment processes can frustrate developers and impede their efficiencies. This score offers actionable insights into developers’ sense of empowerment, productivity, and overall satisfaction with their work environment.

Organizations can utilize this score to identify areas requiring improvement, such as updating tools, optimizing workflows, or nurturing a collaborative team culture. By tracking this metric over time, companies can gauge the effectiveness of interventions and initiatives to enhance developer productivity.

A high satisfaction score ultimately aligns with reduced turnover rates and fosters a positive work atmosphere conducive to innovation and high-quality software delivery. This holistic approach, incorporating objective metrics and qualitative measurements, enables organizations to invest in developer productivity effectively and drive continuous improvement efforts.

• Who’s measuring: LinkedIn and Chime use quarterly surveys to gauge satisfaction across tools and workflows.
• Benefits: Understanding developer sentiment provides critical feedback that can lead to tool and process improvements, fostering a happier and more productive developer community.

Time to restore service

Time to restore service is a critical metric in the definition of developer productivity, reflecting the efficiency of developers in handling production issues and maintaining seamless service operations. This metric measures the time from issue detection to full resolution and assesses an organization’s incident response effectiveness. It underscores the importance of minimizing downtime and mitigating the impact on end users, ensuring uninterrupted service delivery.

For example, if an online banking platform encounters a transaction processing outage due to a critical system error, a proficient incident response team swiftly identifies the root cause, implements a temporary fix, and initiates long-term solutions. By consistently maintaining a low time to restore service, the banking platform reduces disruptions for customers, preserves trust, and safeguards its reputation.

Monitoring and enhancing this metric benchmarks engineering productivity and fosters continuous improvement in incident response processes. By investing in engineering systems that prioritize rapid issue identification and resolution, organizations can realize real benefits such as improved deploy count, reduced P0 count, increased merge frequency, and a smoother development experience. Maintaining a low time to restore service offers significant benefits by ensuring resilient service delivery and enhancing overall customer satisfaction.

• Who’s measuring: Atlassian and GitLab track this as the time an incident remains open until resolution.
• Benefits: A shorter time to restore service minimizes business impact and enhances customer satisfaction scores.

Code reviewer response time

This metric assesses reviewers’ time to respond to code changes or pull requests. It measures the efficiency of the code review process by tracking the duration between submitting a request and receiving initial feedback. A shorter response time indicates a streamlined review workflow, fostering faster development cycles and quicker delivery of new features or bug fixes.

For instance, a SaaS company notices that its average response time to pull requests exceeds two days, slowing code to production time and causing developers to be frustrated. After recognizing this delay, it implemented a policy requiring dedicated review slots and assigned clear reviewer responsibilities. As a result, response times significantly decrease, accelerating the feedback loop and enabling faster, higher-quality software releases.

Monitoring this metric helps teams improve their review process, ensuring a timely response and keeping development workflows efficient.

• Who’s measuring: Google and LinkedIn prioritize this metric to assess the speed of their review cycles.
• Benefits: Quick feedback loops improve developer productivity and speed up the delivery of high-quality code.

Ease of delivery

Ease of delivery is a qualitative measure of how developers perceive their ability to deliver code efficiently within their current workflows. It gauges how intuitive, streamlined, and manageable the development process feels, reflecting how easily developers can navigate tools, workflows, and team collaboration. A high ease of delivery score signifies that developers feel empowered to work effectively without unnecessary obstacles, leading to faster and smoother releases.

For example, in a gaming software company, developers may find their ability to deliver new game features hampered by cumbersome build tools or a lack of automated testing, resulting in frequent delays.

By introducing a more intuitive build system and increasing test automation coverage, the company can simplify the workflow and give developers a smoother delivery pipeline. This improvement translates into more consistent releases and happier teams, reinforcing how valuable an optimized development environment is for overall productivity. Monitoring this measure allows organizations to continually refine their workflows, making the delivery process more accessible and enjoyable for developers.

• Who’s measuring: Amplitude, GoodRx, and Postman focus on ease of delivery in their developer experience surveys.
• Benefits: This metric identifies bottlenecks and obstacles, enabling teams to streamline workflows and reduce cognitive load.

Deployment frequency

Deployment frequency measures how often software developers deploy new features or updates to production. This metric strongly indicates an organization’s ability to deliver value to customers swiftly and consistently. Frequent deployments demonstrate a team’s agility, highlighting a culture of continuous improvement and rapid response to customer needs or market changes.

For instance, a fintech company might find its deployment frequency low due to manual testing and complex approval processes, which cause delays in releasing new features. By shifting to automated testing and implementing a more streamlined approval workflow, the team increases its deployment frequency, releasing updates weekly instead of monthly. This rapid deployment cadence enables them to deliver new features and fixes much more quickly, staying competitive and meeting customer expectations. Monitoring deployment frequency helps teams identify bottlenecks, refine their processes, and consistently deliver high-quality software updates.

• Who’s measuring: Google and GitLab use deployment frequency as a key performance indicator of their continuous delivery processes.
• Benefits: Increasing deployment frequency indicates adequate code quality and collaboration between engineering teams, ensuring faster delivery of business value.

Lead time for changes

Lead time for changes measures the speed at which a code change moves from a developer’s workspace to production. This metric provides insight into the efficiency of the entire development pipeline, from initial code writing to testing, review, and final deployment. A shorter lead time signifies an optimized workflow, enabling teams to bring customers new features or bug fixes swiftly.

For instance, a health-tech company realizes its lead time is prolonged due to lengthy manual testing and cumbersome review procedures, delaying its response to market demands. The company significantly reduces the time required to move changes from development to deployment by introducing automated testing and streamlining its code review process. This improvement allows them to adapt to emerging health regulations and user needs quickly, providing innovative solutions while maintaining high quality. Tracking lead time for changes helps organizations uncover and address bottlenecks in their delivery pipeline, leading to faster development cycles and a more responsive service.

• Who’s measuring: GitLab and Atlassian measure the median time for changes to reach production.
• Benefits: Shorter lead times enable a faster feedback cycle and reveal efficiencies in the development environment.

Developer build time

Developer build time measures how long developers wait for their local builds to complete. It reflects the efficiency of local build processes and directly impacts developer productivity and satisfaction. Prolonged build times can disrupt workflow, leading to frustration and a slower development pace, whereas shorter build times enable faster feedback loops and more productive coding sessions.

For example, a media streaming company notices developers often wait over 20 minutes for their local builds due to outdated tools and excessive dependencies. After analyzing the root cause, they improve build configurations, remove unnecessary dependencies, and upgrade their tooling. As a result, their average build time drops to less than five minutes, significantly reducing delays and keeping developers focused on their tasks. This metric is measured by timing the build process on developer machines from initiation to completion. Monitoring Developer Build Time helps teams identify inefficiencies, allowing them to optimize their tools and processes to maximize productivity.

Who’s measuring:

• LinkedIn uses this metric to measure wall-clock time for builds invoked manually by developers.
• Benefits: By reducing build times, developers can focus more on coding and less on waiting, ultimately increasing productivity.

Time to first and tenth PR

Time to first and tenth PR measures the ramp-up time for new developers by tracking how long it takes them to submit their first and tenth pull requests. Measuring the time to the first PR reflects how quickly a new developer can familiarize themselves with the team’s tools, codebase, and workflows to make their initial contribution. The time to the tenth PR is also crucial because it indicates the consistency and speed with which new hires can contribute once the initial onboarding period is over, showing how well they’ve integrated into the development environment.

Here’s an example:

A software consultancy firm notices that new engineers submit their first pull requests (PRs) quickly but struggle with subsequent ones. This inconsistency causes issues with code review metrics and potentially degrades service. It also creates bottlenecks during reviews.

To address this, the firm improves onboarding training and documentation and assigns dedicated mentors. Consequently, the time to the first PR stays fast, and the time to later PRs, like the tenth, decreases significantly.

By tracking PRs per developer and code review metrics, organizations can identify onboarding issues and assess ongoing developer productivity. This metric helps developers become valuable contributors faster, maintaining a consistently high level of service.

• Who’s measuring: Peloton leverages this metric to refine onboarding and assess the effectiveness of its Tech Enablement team.
• Benefits: These measurements reveal the onboarding effectiveness and highlight ways to improve new developer engagement.

Weekly time loss

Weekly time loss measures the percentage of individual-level developer time lost due to various impediments, such as outdated tools, unclear requirements, excessive meetings, or frequent context switching. It quantifies the impact of these obstacles on productive coding and job satisfaction.

For instance, an AI startup finds that developers lose a lot of time due to redundant meetings and inefficient build processes. To address this issue, they streamline meetings and invest in automated build tools, resulting in happier developers and increased productivity.

This metric is usually assessed through surveys or time-tracking tools, comparing productive work hours with total work time. Organizations can refine workflows, eliminate barriers, and create an environment that promotes efficient software delivery by incorporating developer input and monitoring weekly time loss.

• Who’s measuring: GoodRx and Postman measure this to track the impact of workflow improvements.
• Benefits: Reducing average time loss translates to more productive developers, improving overall software development team output.

Experiment velocity

Experiment velocity measures how quickly teams engage in developer activity, reflecting their learning agility and innovation ability. It assesses the speed at which teams test hypotheses, gather feedback, and adjust strategies. A higher experiment velocity indicates a more adaptable organization that can efficiently deliver innovations in response to customer needs and market changes.

For instance, a travel booking company that enhances user experience monitors experiment velocity while experimenting with new search and filtering features. By implementing a streamlined framework for conducting A/B tests and collecting user feedback, the team can iterate on features quickly, releasing weekly improvements. This agile approach enables them to stay responsive to evolving travel trends and customer preferences.

Count the number of experiments completed within a defined timeframe, such as per sprint or quarter, to track experiment velocity. By monitoring this metric, organizations can cultivate a culture of learning and innovation, promote developer satisfaction, and mitigate excessive code churn.

• Who’s measuring: Etsy tracks the number of experiments started and stopped with a positive hit rate.
• Benefits: This metric encourages rapid learning and feature development while maintaining alignment with business goals.

Adoption rate

The adoption rate measures the percentage of developers actively using a specific tool or process within the intended user base. It evaluates how well a tool or process integrates into the development workflow and its acceptance among developers. A higher adoption rate indicates the tool is user-friendly, valuable, and effectively meets the team’s needs.

For example, a financial software company introducing a new code review tool to foster collaboration and improve code quality discovered that only 60% of developers were using it, potentially impairing service. The lower adoption rate could be attributed to inadequate training and unclear guidelines. To address this, the company invests in comprehensive training sessions and detailed documentation, resulting in a surge in usage to nearly 100% within two months.

The adoption rate is calculated by comparing the number of active tool users to the total expected users. By monitoring this metric, organizations can identify and address barriers to tool utilization, ensuring efficient software development and mitigating the risk of service outages.

• Who’s measuring: Spotify, DoorDash, and Uber track adoption rates for their internal tools and standards.
• Benefits: Understanding adoption rates ensures that investment in developer productivity tools aligns with team needs.

How to measure and improve developer productivity

Developer productivity is a complex, multifaceted concept that extends beyond simply quantifying the code produced or the number of bugs fixed. It involves a deep understanding of the efficiency and effectiveness of individual developers and teams within the context of project management tools, company culture, and engineering leadership. Measuring and improving team and individual productivity requires a holistic approach that combines quantitative metrics, such as code quality and velocity, with qualitative metrics, including developer satisfaction and collaboration.

By considering objective data and subjective insights, organizations can comprehensively understand their development processes and make informed decisions to optimize productivity and foster a thriving engineering environment.

Combining quantitative and qualitative metrics

To fully understand the developer experience, combine quantitative metrics that provide insights into developer performance with qualitative measures. Frameworks like DORA (DevOps Research and Assessment) and SPACE metrics (Satisfaction, Performance, Activity, Communication, and Efficiency) offer structured approaches to measuring developer productivity. These frameworks incorporate a range of metrics, including cycle time, deployment frequency, and lead time for changes, as well as qualitative factors such as job satisfaction and collaboration.

The role of surveys

Surveys play a crucial role in gathering qualitative data about developer productivity. By conducting regular surveys, organizations can assess factors like developer happiness, team dynamics, and perceived obstacles to productivity. This qualitative information complements the quantitative metrics, providing a more nuanced understanding of the factors influencing some types of developer productivity.

Strategies for improving developer productivity

To improve developer productivity, organizations should create an environment that supports efficient workflows and fosters collaboration. Strategies can involve:

1. Investing in modern engineering tools and systems
2. Optimizing development processes
3. Promoting a culture of continuous improvement
4. Providing developers with sufficient focus time
5. Minimizing distractions
6. Encouraging open communication

Leveraging real-time data and metrics

Engineering leaders should leverage real-time dev productivity metrics metrics to identify areas for improvement and make data-driven decisions. By monitoring key performance indicators and regularly reviewing productivity metrics, leaders can:

• Identify bottlenecks
• Optimize resource allocation
• Ensure that projects remain on track

Measuring and enhancing developer productivity is vital for companies to remain competitive in the tech industry. In herexcellent summary of developer productivity metrics, DX’s CTO Laura Tacho reminds us that “focusing on outcomes over outputs is crucial. Metrics should align with the company’s goals and the individual’s role, avoiding the pitfalls of misguided measurements.”

Organizations can better understand their development processes using quantitative and qualitative metrics, avoid over-indexing on the wrong metrics, and identify improvement areas. Conducting comprehensive surveys, investing in modern tools, streamlining workflows, and promoting a culture of continuous improvement are key strategies to boost developer productivity. As leaders use real-time data to make informed decisions, they can address issues, optimize resources, and keep projects on track. By prioritizing developer productivity, companies can deliver quality software more quickly, increase developer satisfaction, and align engineering efforts with business objectives.