Lead time
What is lead time?
Lead time in software engineering is defined as the average time it takes for a feature or a piece of code to move from conception to being deployed in production. This metric starts from the moment a new feature request is made or a project is initiated, and it ends when the product or feature is fully operational and available to end-users. To calculate lead time, you measure the time between the initial concept for a feature and its deployment. You then average this time over multiple features or projects to get a sense of how long it typically takes for your engineering team to deliver updates to production.
Why is lead time important?
Predictability. Establishing predictable lead times can significantly enhance the planning and reliability of software development processes. Teams that can accurately forecast how long it will take to deliver features are better equipped to set realistic timelines and manage stakeholder expectations. This predictability helps in aligning project timelines with business objectives, ensuring that product launches and updates roll out as planned.
Efficiency. Shorter lead times are often indicative of a more efficient development process. By tracking and optimizing lead time, organizations can identify bottlenecks and inefficiencies within their development pipeline. This enables continuous improvement in processes, which can lead to faster turnaround times for feature development and deployment, ultimately contributing to a competitive advantage in the market.
Customer Satisfaction. Reducing lead time can have a direct impact on customer satisfaction. In today’s fast-paced market, users expect quick updates and new features that address their needs. Organizations that can deliver these rapidly are more likely to retain existing customers and attract new ones. Faster lead times mean quicker responses to market changes and customer demands, enhancing the overall user experience.
What are the limitations of lead time?
Does not measure quality. Lead time focuses on the duration from development start to product delivery, but it does not account for the quality of the output. A short lead time could result in poor-quality releases if not balanced with effective quality assurance practices. This could lead to higher bug rates, customer dissatisfaction, and increased costs related to fixing defects post-deployment.
Varies with complexity. Lead time can vary greatly depending on the complexity and scale of the project. Larger or more complex features naturally require more time to develop and test, which can skew the average lead time if not segmented or analyzed correctly. This makes it difficult to compare lead times across different teams or projects without considering the nature of the work involved.
External dependencies. Lead time is often influenced by factors outside the control of the development team, such as third-party integrations, hardware deliveries, or external approval processes. These dependencies can introduce delays that do not necessarily reflect the efficiency or capability of the engineering team, yet adversely affect the lead time metric.
Metrics related to lead time
Cycle time. Cycle time is closely related to lead time and represents the time it takes to complete work on a feature or task once it actually begins. It excludes the time spent in backlog or waiting for approval. Understanding both lead time and cycle time helps organizations identify where delays occur—whether in the initial stages before development begins or during the development process itself.
Deployment frequency. This metric indicates how often an organization successfully releases to production. High deployment frequency often correlates with shorter lead times, as it suggests that an organization is capable of developing, testing, and deploying smaller batches of changes more quickly and efficiently.
Change failure rate. While focusing on improving lead time, it’s important to monitor the change failure rate, which measures the percentage of deployments causing a failure in production. This metric is essential for ensuring that efforts to reduce lead time do not compromise the quality of the software being delivered. It helps balance the speed of delivery with reliability, providing a more holistic view of the development process efficiency.
