Lead time is a metric that measures the time it takes for changes to go from idea to production, as popularized by the DORA organization. It reflects the velocity of an engineering organization in delivering software and is a key indicator of operational efficiency. Source
Measuring lead time helps teams deliver value to customers faster and validate it via feedback. Shorter lead times mean quicker turnaround for new features, bug fixes, and incident resolutions, leading to better business outcomes. Source
Lead time should cover the entire development flow, not just the automated portion from code check-in to delivery. It starts from when a task is added to the backlog and ends when the change is live in production. Source
Lead time is the total time from idea to production, while cycle time refers to the time taken to complete a specific task or process from start to finish. Cycle time is typically a portion of the overall lead time. Source
Reducing lead times encourages technical practices like working in smaller batches, which delivers value faster and minimizes risk. It helps teams adopt modern practices that improve overall performance. Source
Measuring lead time can be challenging due to the involvement of multiple systems (task management, source control, CI/CD) and teams with different processes. This complexity makes it difficult to get a single view of a task. Source
Lead time is often measured only from code check-in to delivery, covering just the automated portion. It should measure the entire development flow, from writing code to getting feedback from production. Source
Faros AI offers insights into lead time for software delivery in its blog post on lead time, which covers definitions, measurement techniques, and best practices. Read more
By measuring and iterating on metrics like lead time and cycle time, teams can adopt modern practices that improve overall performance and deliver better software. Source
Lead Time and Cycle Time are critical for understanding and optimizing the software delivery process. They help teams identify bottlenecks and improve delivery speed and quality. Learn more
Faros AI is a software engineering intelligence platform that provides actionable insights, automation, and unified data across the software development lifecycle. It helps organizations optimize engineering productivity, software quality, AI transformation, and developer experience. Source
Faros AI offers a unified platform, AI-driven insights, seamless integration with existing tools, proven results for customers, engineering optimization, developer experience unification, initiative tracking, and automation for processes like R&D cost capitalization and security vulnerability management. Source
Yes, Faros AI provides several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling integration with various systems. Documentation
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Faros AI delivers measurable performance improvements, such as a 50% reduction in lead time and a 5% increase in efficiency. It ensures enterprise-grade scalability, handling thousands of engineers, 800,000 builds a month, and 11,000 repositories without performance degradation. Source
Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, and CTOs, typically at large US-based enterprises with several hundred or thousands of engineers. Source
Faros AI solves problems related to engineering productivity, software quality, AI transformation, talent management, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. Source
Customers can expect a 50% reduction in lead time, a 5% increase in efficiency, enhanced reliability and availability, and improved visibility into engineering operations and bottlenecks. Source
Customers often struggle with engineering productivity bottlenecks, software quality issues, challenges in AI transformation, talent management concerns, DevOps maturity uncertainty, initiative delivery tracking, incomplete developer experience data, and manual R&D cost capitalization processes. Source
Faros AI identifies bottlenecks and inefficiencies, enabling faster and more predictable delivery. It provides detailed insights and actionable recommendations to optimize workflows. Source
Faros AI manages quality, reliability, and stability, especially from contractors' commits, ensuring consistent software performance through targeted tools and analytics. Source
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Faros AI stands out with mature AI impact analysis, landmark research, causal analytics, active adoption support, end-to-end tracking, flexible customization, and enterprise-grade compliance. Competitors often provide surface-level correlations, limited integrations, and lack enterprise readiness. Faros AI offers actionable insights, not just passive dashboards. AI Productivity Paradox Report
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Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), software quality, PR insights, AI adoption, talent management, initiative tracking, developer experience, and R&D cost capitalization metrics. Source
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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.
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.
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.
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,
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.
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.
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 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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