Understanding and improving lead time for changes

Taylor Bruneaux


Lead time for changes is a crucial metric in software development that improves overall team productivity and efficiency. This article will discuss the lead time for changes, how to calculate it, and how to use it effectively.

What is the lead time for changes?

Lead time for changes measures the average duration for a software change to move from check-in to successful deployment in your production environment.

Unlike productivity metrics that measure the quantity of work done in a change, such ascounting lines of code, lead time for changes evaluates the speed of the process. It measures how quickly a team can review, test, and deploy a change by averaging the time across multiple deployments. This assessment spans all stages of theDevOps pipeline, providing a clear picture of the team’s efficiency in delivering software updates.

Lead time for changes is one of the DORA (DevOps Research and Assessment) metrics, a set of four key metrics that measure software developer performance. These metrics gained popularity through the book Accelerate: The Science of Lean Software and DevOps.

Change lead time differs from another DORA metric, deployment frequency. While deployment frequency measures how often new features or bug fixes are shipped within a specified period, lead time for changes explicitly measures the efficiency of the development and deployment processes. Deployment frequency gauges overall productivity, whereas lead time for changes focuses on process efficiency.

Lead time for changes also differs from two other similar-sounding metrics:

  • Lead time measures the duration from a customer request to the start of feature development. Unlike lead time for changes, which focuses on the time from code check-in to deployment, lead time encompasses the initial stages of identifying and planning a feature.
  • Cycle time measures the duration it takes to deliver a change throughout the entire software development lifecycle (SDLC), from customer requests through design, development, and deployment. In contrast, lead time for changes focuses explicitly on the latter part of this process, from code check-in to deployment.

How to calculate lead time for changes

Knowing how to calculate lead time for changes is crucial in boosting your software delivery performance.

Simple steps to calculate lead time

  1. Pinpoint key stages: First, figure out your lead time’s start and end points. Typically, this starts when a developer works on a change (like adding a new feature or fixing a bug) and ends when the change is deployed in production.
  2. Collect data: Gather timestamps for each stage of the development process. Use tools likeJira,GitHub, orGitLab to track when the team creates a change request, starts development, finishes thecode review, and deploys the change.
  3. Do the math: Subtract the start and end times to find the total lead time.

Lead time for changes, for example

Let’s examine a specific example to illustrate how to calculate lead time for changes. Imagine your team receives a change request to add a new login feature to your application on May 1st at 9:00 AM.

  • Change request creation: May 1st, 9:00 AM
  • Development starts: The developer begins working on the feature right away. By May 1st, 6:00 PM, the initial code for the login feature is ready.
  • Code review: The code review process starts at 9:00 AM on May 2nd and is completed by noon the same day.
  • Testing: Testing begins immediately after the code review. The QA team tests the new login feature by checking functionality, security, and user interface compatibility. Testing is completed by May 3rd at 9:00 AM.
  • Change deployment: The team deploys the change on May 3rd at 3:00 PM.

Adding these intervals together gives you a total lead time of 54 hours—just over two days from the change request to being live in production. This detailed tracking helps your team understand the efficiency of your processes and identify any stages that may need improvement.

Handy tools to measure lead time

You don’t have to do all this manually. Several tools can help automate the tracking and calculation of lead time for changes:

  • Jira: This integrates with your development workflows to track creation and deployment changes.
  • GitHub/GitLab: Both offer built-in tracking for pull and merge requests, including timestamps for various stages.
  • CI/CD tools: Tools like Jenkins, CircleCI, and Azure DevOps monitor and report on deployment times.
  • Data Cloud: Data Cloud is a single source of truth for engineering metrics and reporting, measuring DORA metrics alongside more holistic DevEx metrics.

Benefits of measuring lead time for changes

Lead time for changes can give insights into your development processes and let you know if your team is encountering any blockers to production.

Enhances predictability

Measuring change lead time helps teams better predict the time required to implement and deliver new features, leading to more accurate project timelines and planning.

Improves customer satisfaction

Organizations can deliver updates and features more consistently by understanding and optimizing lead time, resulting in a better customer experience and increased satisfaction.

Identifies process bottlenecks

Tracking lead time highlights areas where delays occur. This allows teams to identify and address bottlenecks in their development process, leading to smoother workflows.

Facilitates continuous improvement

Regularly measuring lead time provides valuable data for ongoing process improvements, helping teams to implement changes that enhance efficiency and quality over time.

Supports data-driven decision-making

Having concrete data on lead times empowers managers and stakeholders to make informed decisions about resource allocation, process adjustments, and strategic planning.

Interpreting and acting on lead time for changes data

Understanding lead time for changes involves comparing your current lead times to industry benchmarks or past performance. Industry benchmarks help you see how your team’s performance stacks up against others. These benchmarks vary by industry, project size, and technology stack, so finding relevant comparisons is crucial.

When comparing against past performance, look for trends over time. This analysis shows if your lead times are improving, staying the same, or worsening, helping you identify the impact of any process changes.

A consistently high lead time might signal inefficiencies in your development or deployment process. Several factors can contribute to high lead times:

  • Lengthy code reviews: If code reviews take an extended time, it can significantly delay the deployment process. Long code reviews could be due to a lack of reviewer availability, overly complex code changes, or unclear guidelines.
  • Inadequate automated testing: Insufficient or ineffective automated tests can slow down the process as manual testing becomes necessary, or undetected bugs require additional cycles to fix and retest.
  • Manual deployment procedures: Manual steps in the deployment process can introduce delays and increase the risk of human error. Automating deployments can streamline this stage and reduce lead times.
  • Complex integration processes: If integrating code changes with the existing codebase is complicated or time-consuming, it can lengthen lead times. Simplifying integration processes and ensuring good modular design can help mitigate this.
  • Need for more resources or capacity: Limited team capacity or insufficient resources can create bottlenecks, leading to increased lead times. This insufficiency might require hiring additional team members or optimizing resource allocation.

On the other hand, decreasing the average lead time over time indicates improvements in these areas:

  • Streamlined code review processes: Implementing more efficient review practices, such as defining clear guidelines, using code review tools, or conducting pair programming, can reduce review times.
  • Enhanced automated testing: Expanding automated test coverage and improving test quality ensures that code changes are validated quickly and reliably, reducing the need for manual intervention.
  • Automated deployment pipelines: Implementing continuous integration and continuous deployment (CI/CD) pipelines can automate the build, test, and deployment processes, significantly reducing lead times.
  • Simplified integration and modular design: Adopting practices that make code integration simpler and focusing on modular design can reduce the complexity of merging changes, speeding up the process.
  • Optimized resource management: Efficiently managing team resources and ensuring adequate capacity can prevent bottlenecks and keep the workflow smooth and efficient.

Common pitfalls and limitations

Misunderstanding the lead time definition

One common pitfall when interpreting the lead time for changes metric is misunderstanding its definition. Lead time for changes typically refers to the time from code commit to deployment in a production environment.

However, some may confuse it with cycle time, which includes the entire development process from the initial idea to the final deployment. This misunderstanding can lead to incorrect assessments of team performance and project timelines.

It’s crucial to ensure that everyone involved has a clear and consistent understanding of what the lead time for changes encompasses to avoid skewed metrics and misinformed decisions.

Focusing solely on speed

Another pitfall is focusing solely on reducing lead time without considering the impact on quality. While faster lead times are generally desirable, they should not come at the expense of code quality or stability.

Teams might rush through the development process to decrease lead times, increasing technical debt and potential production issues. Balancing speed and quality is essential, ensuring the processes support timely delivery and robust, maintainable code.

Ignoring context and variability

Ignoring the context and variability in lead times can also lead to misinterpretations. Lead times can vary significantly depending on the type of changes, the complexity of the project, and external factors such as regulatory requirements or dependencies on other teams.

Comparing lead times across different projects or teams without accounting for these variables can create unfair assessments and unrealistic expectations. It’s essential to consider the broader context and the specific circumstances surrounding each project to draw meaningful conclusions from lead time metrics.

Neglecting to identify and address bottlenecks

Another common issue is identifying and addressing bottlenecks in the development process. Lead time for changes is a valuable metric for pinpointing areas where delays occur, such as in code review, testing, or deployment.

However, simply measuring lead time without analyzing the underlying causes of delays will not lead to improvements. Teams should use lead time metrics as a starting point to investigate and address bottlenecks, optimizing their processes for more efficient and effective delivery.

Overlooking team collaboration and communication

Finally, overlooking the role of team collaboration and communication can undermine the effectiveness of lead time metrics. Efficient lead times are often the result of solid teamwork, clear communication, and well-defined processes.

Best practices for leveraging lead time for changes

Here are a few actions you can take to get the most use out of lead time for change as a productivity metric:

Combine with other DORA metrics

While valuable, lead time for changes only gives you a single view of your overall development process. Use it together with the other three metrics to have a more comprehensive understanding of your software delivery process.

Deployment frequency

Deployment frequency measures how often a team deploys code to production. Lead time for changes, the duration from code commit to deployment, often correlates with deployment frequency. Shorter lead times typically enable more frequent deployments, allowing quicker feedback and iterative improvements.

Change failure rate

Change failure rate indicates the percentage of deployments causing failures in production. While speeding up delivery, it’s crucial to maintain a low change failure rate. A short lead time can lead to frequent releases, but if changes fail often, the benefits are negated. Adequate lead time management includes rigorous testing and automated processes to minimize failures and ensure stability.

Mean time to recovery (MTTR)

Mean time to recovery (MTTR) measures how quickly a team can recover from a failure. In failure cases, a low lead time aids in rapid recovery by enabling swift deployment of fixes. A speedy MTTR minimizes downtime and improves overall resilience. Teams with short lead times can iterate quickly on issues, ensuring that teams resolve problems and make improvements without significant delays.

Align lead time for change goals with broader business objectives

Connect lead time for changes to the central KPIs of the organization. If the goal is to increase revenue, show how reduced lead time cuts time to market.

This connection clarifies for development teams how their work impacts the company’s bottom line. For senior leadership, it highlights the dev team’s contributions to quarterly goals.

Connect improved lead time for changes back to developer experience.

Measuring change lead time and other DORA metrics improves thedeveloper experience by identifying inefficiencies and streamlining workflows. Change lead time measures the duration from code commit to deployment. By tracking this, DevOps teams can find and fix bottlenecks, leading to a faster and smoother software delivery process, whichboosts developer productivity and satisfaction.

DORA metrics provide a comprehensive view of software delivery performance. Frequent deployments signify an agile process, while a low MTTR ensures quick issue resolution, minimizing downtime and frustration. Monitoring change failure rates helps maintain high-quality standards, highlighting deployment stability and reliability. These metrics foster a culture of continuous improvement, empowering teams to refine their practices continually.

Implementing these metrics enhances team efficiency and morale by creating a transparent and responsive development environment. Developers understand how their contributions impact the delivery process. This visibility and accountability celebrate successes and foster learning, driving individual and team growth.

As one of the core four key metrics in DevOps, lead time for changes can enable you to identify and eliminate bottlenecks in the software production process. Applying this DevOps metric consistently and correctly can result in a better developer experience, increased developer productivity, and reduced time to market for new features. Engineering leaders can leverage these insights to improve DevOps performance and streamline the deployment pipeline. This approach fosters continuous delivery and supports the overall success of the development team.

May 22, 2024

Get started

Want to explore more?

See the DX platform in action.

Get a demo