The engineering KPIs that actually matter

Engineering KPIs give leaders signals about how their organization is progressing towards goals. Key performance indicators are typically used for reporting and can help track the overall efficiency, effectiveness, and impact of your engineering teams and software development systems.

But which ones should you track, and why are they important?

This guide delves into the types of engineering KPIs and when to use each. We focus on three categories of KPIs: business impact, system performance, and developer effectiveness. Then, we let you know our top 20 engineering KPIs to track. We also explore additional product metrics companies find valuable and how you can track these in an engineering KPI dashboard.

Table of Contents:

• What are engineering KPIs?
• Why track engineering KPIs
• Types of engineering key performance indicators
• 20 most important engineering KPIs
• How to start measuring engineering KPIs
• Why engineering KPIs matter for developer experience teams
• Using an engineering KPI dashboard
• Maximizing developer effectiveness with DX

What are engineering KPIs?

Engineering key performance Indicators (KPIs) are quantifiable metrics that tell engineering team leaders about the performance, operational efficiency, or impact of engineering. Each key performance indicator is often called a “North Star metric” and is typically used for reporting purposes or to help teams track progress along their strategy.

Why track engineering KPIs

There are two main reasons software development teams use KPIs:

Reporting

CEOs and executives often ask engineering leaders to report on metrics to understand the effectiveness of their investment. To do this, engineering team leaders use KPIs to represent their organization’s impact, quality, and effectiveness.

Tracking progress

To make sure your strategy is working, you should track its progress using KPIs. For example, a DevEx team might use KPIs to measure and improve developer experience by tracking speed, ease of delivery, quality, and satisfaction. KPIs help the team prioritize their goals and understand how their efforts are impacting the organization.

Types of engineering key performance indicators

We recommend three buckets of engineering KPIs to ensure clarity on what impact each metric has: business impact, system performance and developer effectiveness

Business impact KPIs

Business metrics KPIs measure the impact of software development efforts on the business. These include current and planned project roadmaps, business rationale and success metrics, and project status (e.g., delivered, on track, at-risk).

Business impact KPIs demonstrate how engineering aligns with business goals, justifies resource allocation, and assures stakeholders about the ROI of software engineering initiatives.

System performance KPIs

System performance KPIs are used to evaluate the health and performance of software systems. They measure important factors such as application latency, uptime, and the number of incidents.

These metrics are crucial as they provide insights into the reliability, speed, and user satisfaction of software systems and software development. Such insights are essential for ensuring the best possible software delivery performance, resulting in a superior user experience and business success.

Developer effectiveness KPIs

Developer effectiveness KPIs are tools to assess developer productivity. These metrics help us see the advantages of investing in software engineering talent and identify areas where developers and engineering managers can improve. If you have a team focused on development and product or developer experience, they track metrics in this category, like SPACE, DORA, and the DevEx framework.

To measure these KPIs, use a combination of qualitative (surveys) and quantitative (systems) data to measure the dimensions of developer productivity, including speed, ease of delivery, quality, and developer satisfaction.

20 most important engineering KPIs

There are hundreds of possible combinations of software development key results to track. Start with a metric or two in each category that can be easily measured, tied to activities, and iteratively improved over time.

Example business impact engineering KPIs:

Project ROI (return on investment):

• What it is: Measures the financial return generated by a project compared to its cost.
• Why it’s important: Demonstrates the economic value of engineering project success.
• How to calculate and measure: (Net Project Benefits / Project Costs) * 100

Time-to-market:

• What it is: Measures the time taken to develop and deliver a product or feature.
• Why it’s important: This metric indicates efficiency and responsiveness to market demands.
• How to calculate and measure: Release date of a feature - Start date of development

Feature adoption rate:

• What it is: Measures how quickly users adopt new features.
• Why it’s important: Indicates the relevance and acceptance of new functionalities.
• How to calculate and measure: (Number of users using the new feature / Total number of users) * 100

Project status alignment:

• What it is: Measures the alignment of project status with the planned roadmap.
• Why it’s important: Ensures that projects are on track and meeting business objectives.
• How to calculate and measure: (Number of projects on track / Total number of projects) * 100

Cost of delay:

• What it is: Measures the financial impact of delaying a project or feature release.
• Why it’s important: Helps prioritize tasks and minimize financial losses.
• How to calculate and measure: Estimated financial impact per unit of time.

Example system performance KPIs:

Application latency:

• What it is: Measures the time delay between a user action and system response.
• Why it’s important: Affects user experience; a lower latency metric is generally preferred.
• How to calculate and measure: Average response time for user requests.

Uptime/availability:

• What it is: This software engineering KPI measures the percentage of time the system is operational.
• Why it’s important: Ensures continuous service and minimizes disruptions.
• How to calculate and measure: (Total uptime / Total time) * 100

Incident resolution time:

• What it is: Measures the time taken to resolve system incidents.
• Why it’s important: Reflects the system’s stability and responsiveness to issues.
• How to calculate and measure: To measure incident resolution time in development, begin by clearly defining incidents and implementing a system for logging and tracking this metric, like Jira. Categorize incidents by severity, record start and end timestamps, and include communication, analysis, resolution, verification, and documentation times. Calculate the total resolution time to gauge the effort involved. Regularly monitor trends to identify areas for improvement, fostering a continuous improvement mindset within the software development process.

Scalability index:

• What it is: Measures the system’s ability to handle increased load.
• Why it’s important: An improving scalabilty metric indicates preparedness for future growth.
• How to calculate and measure: To measure a scalability index, identify key performance indicators (KPIs) such as response time, throughput, or resource utilization that are indicative of scalability. Conduct performance testing under varying loads or user levels, and record the corresponding values of the chosen KPIs. Analyze the data to determine how well the system scales, considering factors like linear scalability or saturation points. Calculate a scalability index based on the observed performance metrics, providing a quantitative measure of the system’s ability to handle increasing workloads or user demands. Regularly revisit and update these measurements to ensure ongoing scalability assessment.

Error rate, or change failure rate:

• What it is: Measures the percentage of errors or failures in system operations.
• Why it’s important: Reflects the system’s reliability and quality.
• How to calculate and measure: (Number of errors / Total number of operations) * 100

Throughput:

• What it is: Measures the rate at which a software development system processes requests or transactions.
• Why it’s important: Indicates the system’s capacity and efficiency.
• How to calculate and measure: (Number of processed requests / Time)

Network latency:

• What it is: Measures the time it takes for data to travel across a network.
• Why it’s important: Influences communication speed between system components.
• How to calculate and measure: Average time for data transfer.

Capacity planning accuracy

• What it is: Measures how accurately the system’s capacity needs are predicted.
• Why it’s important: An improving capacity planning metric ensures optimal resource allocation and performance.
• How to calculate and measure: (Actual resource usage / Predicted resource usage) * 100

Example developer effectiveness KPIs

Developer satisfaction:

• What it is: The overall contentment and happiness of a software development team within the organization.
• Why it’s important: A high developer satisfaction metric is correlated with increased productivity, better code quality, and reduced turnover.
• How to measure: Regularly conduct surveys or use tools to collect feedback from software developers, asking them to rate their ‘customer satisfaction’ on a scale.

Perceived productivity:

• What it is: The perceived efficiency and effectiveness of developers in completing their tasks.
• Why it’s important: This metric provides insight into the developers’ perspective on their own productivity, which can impact overall engineering team performance.
• How to measure: Periodically survey each team member to gauge their perception of their productivity levels or utilize project management tools to track task completion rates.

Deployment lead time:

• What it is: The lead time it takes to move code from software development to production.
• Why it’s important: Shorter deployment lead time enables faster releases, quicker response to market changes, and increased agility.
• How to measure: Calculate the lead time it takes for code changes to go from commit to production deployment, tracking each stage in the deployment pipeline.

Mean time to recovery (MTTR):

• What it is: The average time taken to restore service after a failure or system outage.
• Why it’s important: A lower MTTR metric indicates efficient incident response, minimizing downtime and maintaining a reliable system.
• How to measure: Monitor incidents and record the time it takes from the detection of an issue to its resolution, then calculate the average metric over a specific period.

Code churn:

• What it is: The frequency with which code is added, modified, or deleted in a codebase.
• Why it’s important: Excessive code churn can indicate instability, decreased code quality, and increased potential for bugs.
• How to measure: Use version control systems to track changes and analyze the frequency of code commits and their impact on the codebase.

Code review efficiency:

• What it is: This performance metric measures effectiveness and speed of the code review process.
• Why it’s important: Efficient code reviews contribute to faster development cycles, improved code quality, and knowledge sharing among engineering team members.
• How to measure: Track the time taken for code reviews, the number of iterations, and the percentage of issues identified and resolved in each review.

Test coverage:

• What it is: The percentage of codebase covered by automated tests.
• Why it’s important: A high test coverage helps identify and prevent defects early in the development process, improving overall software quality.
• How to measure: Calculate the percentage of code covered by automated tests using code analysis tools and test coverage reports.

How to start measuring engineering KPIs

For organizations to enhance their performance by effectively utilizing engineering key performance indicators (KPIs), they should:

Define software development goals

Start by defining your goals or strategy at a high level without specific measurement references. If the goal is to improve developer productivity, then we can work backward from that goal to select KPIs to help us understand whether we’re making improvements.

Combine qualitative and quantitative data for a complete view

For each of the three categories of software engineering key performance indicators, leaders should combine qualitative and quantitative data for a more complete view. This is especially true for developer effectiveness: a common misconception is that metrics from tools like GitHub and Jira alone can provide a complete view of developer productivity.

However, combining quantitative and qualitative engineering metrics, a mixed-method approach is necessary to gain comprehensive insights into code quality, collaboration, and software engineering team culture.

Regularly monitor and report

Establish a routine for monitoring KPIs and sharing reports with stakeholders to ensure transparency and alignment.

Why engineering KPIs matter for developer experience teams

Companies that prioritize developer experience, infrastructure, or platform engineering require engineering KPIs to ensure that they deliver value. These specialized areas demand a profound comprehension of the complexities involved in designing, managing, and quantifying technical systems that support a company’s software development organization.

Here’s why engineering key performance indicators are particularly important for developer experience teams.

Engineering KPIs provide insight into what developers need

Software engineering metrics encompass not only the quantity of output but also the developer experience within a company. The helpfulness of instructions (documentation), the ease of onboarding for new hires, and the overall satisfaction of developers are all factors that can be measured. Both quantitative and qualitative engineering metrics can be improved through initiatives aimed at enhancing the developer experience.

Developer experience teams are able to see the impact of the numbers

Moving beyond using metrics as mere benchmarks, developer experience leaders can understand the underlying reasons behind the success. Instead of just observing that an engineering metric is improving or declining, they are able to analyze why it is moving that way, and how it can be improved. This enables them to make informed decisions tailored to the specific requirements of their team and foster an environment conducive to innovation and success.

DevEx aims to connect real-world impact with engineering key performance metrics

Developer Experience strategically links concrete, real-world impact with engineering KPIs. Instead of just chasing numbers, DevEx digs into the stories behind the data, ensuring that metrics aren’t just abstract benchmarks.

For instance, if the code deployment frequency increases, DevEx examines whether it results in a happier development team or potential burnout. This approach turns KPIs into practical insights, shaping a workplace that not only hits performance targets but also nurtures healthy and sustainable work. In connecting the dots between numbers and outcomes, DevEx ensures that the engineering team not only performs well on paper but also experiences positive day-to-day impacts.

Using an engineering KPI dashboard

To gain a comprehensive understanding of their development processes, most organizations use an engineering KPI dashboard to monitor their selected metrics’ progress using data analytics.

A proficient dashboard not only uses data science to align and predict future metrics but also provides actionable insights for improved decision-making. When evaluating dashboard options, focus on those that offer a comprehensive approach, encompassing both qualitative and quantitative measures.

A robust solution should not only present effective KPIs but also guide in pinpointing valuable insight for enhancing developer productivity. Look for features that facilitate real-time data capture and periodic assessments, ensuring a dynamic and responsive understanding of your engineering landscape, with clear next stesps for an engineering leader or engineering manager.

DX, a developer intelligence platform conceived by the visionaries behind DORA and SPACE, combines qualitative and quantitative measures, enabling developer productivity leaders to identify key opportunities and translate them into tangible outcomes.

Maximizing developer effectiveness with DX

Engineering organizations use DX to implement a holistic measurement program to understand and improve developer effectiveness, one of the three types of engineering KPIs.

DX’s core measurement framework, DX25, captures both drivers and KPIs so leaders can track progress along their strategy and diagnose exactly what factors are affecting speed, quality, or satisfaction.

Request a demo to learn how DX can transform your organization and help you maximize developer effectiveness.