Understanding lead time for changes in software development

Explore the significance of Lead Time for Changes in software development. Learn its significance, how to measure the metric, and strategies for faster deployments.

Understanding lead time for changes in software development

When you ask  software engineering team's their top priority, many will likely say it's the "speed of shipping new changes." But how can we be sure we're actually achieving this goal? That's where Lead Time comes into play.

Lead Time for Changes, one of the four DORA metrics, plays a pivotal role in determining the velocity of software delivery. In this blog will emphasize its significance, offer illustrative instances, underscore its advantages, delve into measurement techniques, differentiate it from the conventional lead time, and propose strategies for meaningful reduction.

What is the lead time for changes?

The lead time for changes serves as a stopwatch, allowing us to track the time for a code modification to move from the development stage to deployment in production. In simple terms, it measures how swiftly and efficiently code changes progress through the software development process. This period encompasses the basic coding and the various review, testing, and quality assurance stages. It's a metric that provides a holistic view of how efficiently the software development process operates. This time measurement gives us a good sense of the speed and efficiency of the software development process.

The benefits of reducing lead time for changes

Minimizing lead time for changes offers several compelling advantages:

Faster time-to-market:

Shorter lead times translate to quicker delivery of features, bug fixes, and enhancements. This accelerates the overall development cycle, allowing organizations to respond to market demands swiftly.

Improved collaboration:

Reducing lead time requires improved collaboration among developers, testers, and operations teams. This encourages better communication, knowledge sharing, and a more cohesive approach to development.

Enhanced quality:

Focusing on quicker feedback loops and faster deployments increases the emphasis on quality assurance and testing. As a result, issues are identified and resolved sooner in the development process.

Agility and flexibility:

A reduced lead time empowers organizations to adapt to changing requirements or market conditions more effectively. Agile development methodologies thrive on quick iterations and benefit significantly from shorter lead times.

How to measure lead time for changes

Measuring the lead time for changes in your software development process is crucial for enhancing efficiency and making your development pipeline more efficient. This metric quantifies the time it takes for a code change to transition from its initial commit to a state where it is deployable. To measure lead time for changes, follow this systematic approach, which includes a formula and detailed explanations:

Identify start and end points:

The first step in measuring change lead time is clearly defining the process's starting and ending points. The starting point is typically when a code change is committed to the version control system, often called the "code commit." On the other hand, the ending point is when the difference is in a deplorable state. It has successfully passed through all necessary stages, including code review, testing, and integration, and is ready for deployment.

Collect data:

To gather the data required for lead time measurement, leverage your version control systems (e.g., Git) and deployment pipelines. You need to track when the code change was committed and when it was deployed. This information can be extracted from the commit timestamps in your version control system and the deployment timestamps in your deployment pipelines.

Calculate lead time:

The lead time for a specific change can be calculated using the following formula:

Lead Time = Deployment Timestamp - Commit Timestamp

Here's a breakdown of the formula components:

  • Deployment timestamp: This is when the code change is successfully deployed and available in the production environment.
  • Commit timestamp: This is when the code change is committed to the version control system.

By subtracting the commit timestamp from the deployment timestamp, you obtain the lead time for that particular change. The result will be in time units, such as hours, minutes, or seconds.

Importance of Lead Time Measurement:

Measuring lead time for changes provides valuable insights into the efficiency of your development process. Longer lead times can indicate bottlenecks, inefficiencies, or challenges in your pipeline that need to be addressed. On the other hand, shorter lead times indicate an agile development process that can promptly adapt to changes and user feedback.

Interpreting the results:

When interpreting the calculated lead time, it's essential to consider the context of your development process. Analyze trends over time and compare lead times for different changes or features. If you notice that specific changes consistently have longer lead times, you can investigate the underlying reasons and take corrective actions.

Differentiating lead time for changes and lead Time

Lead time for changes:

Lead time for changes is a specialized metric within the software development lifecycle. It precisely measures the time it takes for a code change to move from its initial commitment stage to a state where it is ready for deployment. This metric is crucial for assessing the efficiency of the development process, including aspects like code review, testing, integration, and ultimately making the change available in the production environment. Lead time for changes is a critical indicator of how swiftly software modifications are transformed from development to production.

General lead time:

In contrast, general lead time is a broader concept that extends beyond software development. It refers to the duration between initiating a process and achieving the desired outcome. This concept applies in various industries and domains, encompassing manufacturing, service delivery, and supply chain management. For instance, in manufacturing, general lead time covers the time from order placement to final product delivery, considering all stages involved in production.

By distinguishing between lead time for changes and general lead time, we can see that the former is specific to software development. At the same time, the latter is a versatile metric applied across different industries. Simultaneously, the general lead time is a universal metric used in various industries. While the lead time for changes enhances software development workflows, the available lead time provides insights into process efficiency and identifies potential areas for improvement across different operations.

Reducing lead time for changes effectively

To reduce lead time for changes successfully, consider these strategies:


Implement automated testing, continuous integration, and continuous deployment pipelines. Automation expedites repetitive tasks, allowing developers to focus on code quality.

Smaller batches

Breaking down significant changes into smaller, manageable pieces reduces complexity and facilitates quicker reviews, testing, and deployment.


Parallelizing tasks, such as testing different components simultaneously, shortens the overall lead time by eliminating bottlenecks.

Continuous improvement

Regularly analyze your development pipeline for inefficiencies. Encourage a culture of continuous improvement and experiment with different approaches to identify what works best for your team.

Feedback loops

Promote quick feedback loops among team members. This helps in the early detection of issues and accelerates addressing them.

Cross-functional teams

Cross-functional teams comprising developers, testers, and operations personnel can collaboratively work on a change, reducing the time spent on handoffs between different groups.


Understanding the lead time for changes is vital in evaluating and enhancing your DevOps team's operational efficiency. This metric offers insights into your team's responsiveness to modifications and sheds light on their overall capabilities. When coupled with other pivotal metrics like Deployment Frequency, MTTR, and Change Failure Rate, this parameter equips your team with valuable insights to drive comprehensive engineering performance.

With DevDynamics, you can easily measure and analyze the lead time for project changes, helping you make more informed decisions.

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