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Lead Time for Changes

Definition: The time it takes from code being committed (or a work item being started) to that change being deployed in production. Essentially, how long do code changes sit in the system before reaching users. Sometimes measured as commit-to-deploy or pull request merge to deploy.

What it tells you: Lead Time is a direct measure of cycle time efficiency. Short lead times mean features/bug fixes move quickly through the pipeline, indicating smooth handoffs between dev, test, deploy stages. It reflects process health: code integration, testing, and release processes. Shorter LT usually implies more agile teams and can correlate with faster feedback loops.

What it misses or risks: A very short lead time could mean your changes are small and pipeline is automated – good – or it could mean you’re skipping steps (bad). Also, lead time doesn’t capture planning or code writing time; a team might spend weeks designing and coding, but if they merge and deploy in a day, Lead time looks short. So it’s not whole “idea to prod” time, just code integration to prod. Comparing lead times between teams can be tricky if definitions differ (e.g., one measures from first commit, another from PR creation).

Common misinterpretations: Don’t confuse lead time with “time to value” for end-users. It’s possible to have a feature toggle in code that’s deployed quickly (short LT) but business doesn’t release it to users until a marketing event, which is outside engineering’s scope. Also, a long lead time might be by design for bundling releases (though that’s less common nowadays). If mis-measured, teams might think they’re doing fine when they deploy quickly once code is merged – ignoring perhaps a huge backlog before merge. It’s crucial to define the start point of lead time consistently.

Improvement strategies:

  • Streamline code review and approval: If waiting for approvals is a big chunk, establish SLA for reviews or use pair programming to reduce review overhead.
  • Automated testing: Ensure your test suite (unit/integration) is robust and fast. Long test cycles or manual QA can balloon lead time. Invest in parallelizing tests or using cloud test services.
  • Continuous Integration practices: Commit small, commit often. This reduces the merge pain and integration bugs that can stall releases.
  • Value Stream Mapping: Map out every step from code commit to deploy (CI steps, QA, staging, etc.) and look for waste. Perhaps there’s an unnecessary manual staging sign-off that can be automated or removed.
  • Improve build times: Sometimes long lead time is simply due to slow build or artifact creation processes. Build caching or more build agents can help.