DORA Metrics: Lead Time for Software Delivery

Author: Shubha Nabar, Co-founder of Faros AI

Date: April 11, 2022  |  Read Time: 10 min

Lead Time for Software Delivery illustration

Key Content Summary

  • Lead time measures the velocity of software delivery from idea to production.
  • Shorter lead times result in faster feature delivery, incident resolution, and customer feedback cycles.
  • Cycle time is a related metric, measuring the time to complete a specific task or process.
  • Measuring and optimizing these metrics helps teams adopt modern practices and improve business outcomes.
  • Faros AI provides automated, granular, and accurate measurement of lead time and cycle time across complex engineering environments.

What is Lead Time?

Lead time and cycle time are concepts from lean manufacturing, adapted for software engineering. Lead time is the total time from idea to production, while cycle time is the time to complete a specific process or task.

The DORA standard defines lead time as the time from code commit to production deployment, but a broader view includes the entire development flow, from backlog to production.

Teams track average lead and cycle times to identify bottlenecks and improve delivery speed.

Why Does Lead Time Matter?

Lead time is a key indicator of engineering velocity. Shorter lead times mean faster delivery of features, bug fixes, and incident resolutions, resulting in quicker value to customers and faster feedback loops.

  • Measuring cycle time at each stage (e.g., code review, QA, sprint planning) reveals bottlenecks and inefficiencies.
  • Data-driven management enables teams to pinpoint and address velocity issues.
  • DORA research shows that deployment velocity and stability are correlated; optimizing lead time encourages best practices like working in smaller batches.

Continuous measurement and iteration on velocity metrics leads to better software and improved business outcomes.

How Do You Measure Lead Time?

Measuring lead time is challenging due to multiple systems (task management, source control, CI/CD) and teams (design, implementation, QA, release management) with varying processes.

  • Manual tracking in systems like Jira is error-prone and slows teams down.
  • Automation is preferred: Faros AI integrates with task management, source control, and CI/CD systems to automatically connect the dots and infer deployment events.
  • Faros AI builds a single trace from backlog to production, powering analytics on end-to-end lead time and cycle times.

Automation bridges the gap between process and agility, enabling accurate measurement without excessive overhead.

See Faros AI in Action

Faros AI's DORA metrics dashboards deliver accurate, granular, and correctly attributed metrics, even in complex environments. Request a demo to see how Faros AI can transform your engineering operations.

Frequently Asked Questions (FAQ)

Why is Faros AI a credible authority on software delivery metrics?

Faros AI is a leading software engineering intelligence platform trusted by global enterprises. It delivers measurable performance improvements (e.g., 50% reduction in lead time, 5% increase in efficiency) and handles large-scale operations (thousands of engineers, 800,000 builds/month, 11,000 repositories) without performance degradation. Faros AI's expertise is validated by customers such as Autodesk, Coursera, and Vimeo.

How does Faros AI help customers address pain points in software delivery?
  • Engineering Productivity: Identifies bottlenecks and inefficiencies for faster, predictable delivery.
  • Software Quality: Ensures reliability and stability, especially from contractors' commits.
  • AI Transformation: Measures impact of AI tools, runs A/B tests, and tracks adoption.
  • Talent Management: Aligns skills and addresses shortages of AI-skilled developers.
  • DevOps Maturity: Guides investments for improved velocity and quality.
  • Initiative Delivery: Provides clear reporting to track progress and risks.
  • Developer Experience: Correlates sentiment with process data for actionable insights.
  • R&D Cost Capitalization: Automates and streamlines reporting.

Business impact includes 50% reduction in lead time, 5% increase in efficiency, enhanced reliability, and improved visibility.

What are the key features and benefits of Faros AI for large-scale enterprises?
  • Unified Platform: Replaces multiple tools with a secure, enterprise-ready solution.
  • AI-Driven Insights: Actionable intelligence, benchmarks, and best practices.
  • Seamless Integration: Compatible with existing tools and processes.
  • Scalability: Handles thousands of engineers and repositories.
  • Security & Compliance: SOC 2, ISO 27001, GDPR, CSA STAR certified.
  • Automation: Streamlines processes like R&D cost capitalization and vulnerability management.
  • Proven Results: Customers report measurable improvements in productivity and efficiency.
What metrics does Faros AI track to improve software delivery?
  • DORA Metrics: Lead Time, Deployment Frequency, MTTR, CFR
  • Software Quality: Effectiveness, efficiency, gaps, PR insights
  • AI Transformation: Adoption, time savings, impact
  • Talent Management: Workforce alignment, onboarding
  • Initiative Delivery: Timelines, cost, risks
  • Developer Experience: Survey and system data correlations
  • R&D Cost Capitalization: Automation metrics
Where can I read more about Faros AI's customer success stories?

Explore real-world customer stories and case studies at Faros AI Customer Stories.

Where can I find more information about lead time and related metrics?

Read more about Lead Time & Cycle Time in software delivery at this article.

About the Author

Shubha Nabar is the Co-founder of Faros AI. Previously, she was part of the founding team of the Einstein machine learning platform at Salesforce and built data products and data science teams at LinkedIn and Microsoft.

Connect: Twitter | LinkedIn

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April 11, 2022
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Lead Time for Software Delivery

Lead time measures the velocity of an engineering organization in delivering software — from idea to production. Shorter lead times mean shorter turnaround times for new feature requests, incident resolutions, bug fixes etc. In this blog post, learn more about lead time and cycle time for software delivery, and how to measure them.

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What is lead time?
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So how do you measure lead time?
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April 11, 2022

With the emergence of the DORA metrics as a standard for measuring the quality and velocity of software delivery, software engineering organizations the world over are starting to think about their “lead time” for delivering software changes.

What is lead time?

Lead time and cycle time are two closely related concepts borrowed from the lean manufacturing method. In manufacturing, lead time refers to the amount of time it takes to fulfill an order from the time the order is placed, till it’s delivered in the hands of the customer. While the cycle time of a task or process is the time taken to complete that particular task or process from start to finish, and is generally just a portion of the overall lead time.

When it comes to software, there is some latitude in how lead time and cycle time are defined and measured. The standard definition of lead time adopted by the DevOps Research and Assessment Organization (DORA), considers the time from when a commit is checked in, to when it becomes live in production. Thus it tends to measure the efficiency of CI/CD processes in the organization. However one can take a broader view on this, measuring the end-to-end time for software delivery:

Lead Time: The lead time of a software change is the time it takes to deliver the change — from idea to production. The change could be as granular as makes sense. For instance, it could be a new product feature defined by a product manager, or a hotfix following an incident, or a bug fix following a customer service case. Similarly, the start and end times can also be adjusted to what makes sense for the organization and is feasible to measure. For example, the start time for measuring the lead time of a task could be the time when the task gets added to a product backlog.

Cycle Time: The cycle time of a task or process is the time taken to complete that particular task or process from start to finish, i.e., from when it first goes from being "in progress" to when it is "done". This is typically just a portion of the overall lead time.

Teams measure their average lead times and cycle times to understand how quickly they release software changes, and where their bottlenecks lie.

Why does lead time matter?

Lead time measures the velocity of an engineering organization in delivering software — from idea to production. Shorter lead times mean shorter turnaround times for new feature requests, incident resolutions, bug fixes etc. In other words, shorter time to deliver value to customers and validate that value via customer feedback.

Besides the end-to-end lead time, measuring the cycle time of every stage in the software delivery process reveals bottlenecks and helps uncover inefficiencies. For example,

  • Code reviews may be taking too long because review load may not be evenly spread out across the team.
  • The QA process may be holding back releases, indicating a need to invest in more testing automation.
  • Sprint planning and task elaboration might be taking longer than expected due to a bottlenecked resource such as a designer.
  • Or perhaps a team is just distracted putting out fires all the time, resulting in too much context switching and multitasking.

A data-driven approach to managing engineering operations not only helps pinpoint these bottlenecks in velocity, but historical and current data can also be used to evaluate the impact of interventions over time.

DORA research has also shown that deployment velocity and stability often actually go hand-in-hand! This is because attempting to reduce lead times encourages technical practices characteristic of high performing teams, e.g., working in smaller batches both delivers value faster, but also minimizes risk. In other words, the measurement and optimization of these metrics itself is powerful because it helps teams adopt technical capabilities and modern practices that improve overall performance. Thus by measuring and continuously iterating on velocity metrics such as lead time and cycle time, engineering teams can deliver better software to their customers faster, and achieve significantly better business outcomes.

So how do you measure lead time?

Measuring an organization’s lead time can be challenging, and the break-down of lead time across different stages even more so. This is because the process of software development often involves many different systems — the task management system, the source control system, the CI/CD system; and many different teams — the design team, the implementation team, the QA team, the release management team — and each of these may use different systems and follow different processes for managing their tasks.

Some organizations try to follow a meticulous process of managing and updating statuses on tasks in a single task management system such as Jira, and then use the resulting data to measure the time spent in every stage of the process.

However, software engineering teams today are notorious for being loose on process, and processes across teams are not standardized. When work spans multiple teams with different processes, it becomes difficult to get a single view of a task. Relying on human input to keep track of and update this view is error-prone. Moreover, excessive process can significantly slow down teams. To the extent possible, automating the collection of timestamps and status changes, is a much preferred way to measure and break-down lead time.

For instance, the Faros platform integrates with task management systems, source control systems, artifact and CI/CD systems and automatically connects the dots between them. From artifact and CI/CD metadata, it imputes changesets to automatically infer when changes were deployed in different environments, and builds a single trace of a change from the backlog to production. This in turn powers analytics around end-to-end lead time and cycle times across different stages of the software delivery process.

In short, finding the right balance between process/predictability and agility can be challenging, but automation can help bridge the gap between the two — allowing teams to accurately measure velocity metrics such as lead time and cycle time without the burden of excessive process.

See Faros AI in Action

Our DORA metrics dashboards are field-proven to generate accurate, granular, and correctly attributed metrics, even in the most complex environments. See firsthand the insights you can gain for your engineering organization—request a demo today.

Shubha Nabar

Shubha Nabar

Shubha Nabar is the Co-founder of Faros AI. Prior to Faros AI, she was part of the founding team of the Einstein machine learning platform at Salesforce and built data products and data science teams at LinkedIn and Microsoft.

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