> For the complete documentation index, see [llms.txt](https://docs.multitudes.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.multitudes.com/metrics-and-definitions/process-metrics/flow-of-work/editing-time.md).

# Editing Time

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Note: this metric is **only shown at a team level**, not an individual level.
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![Editing Time graph](https://cdn.prod.website-files.com/610c8a14b4df1ae46b1a13a3/640697ca8fabd66171401973_Editing%20Time.jpg)

**What it is:** This metric shows how long it takes to make revisions after feedback has been received.

**Why it matters:** As a measure of back-and-forth between the code author and those who are reviewing the code, `Editing Time` is important for understanding bottle-necks in `Change Lead Time`. A high `Editing Time` could mean that the team needs to improve how they scope work, the received feedback is confusing, the PRs being created are large, or there are other distractions preventing fast iteration. A low `Editing Time` indicates that the team is able to quickly action feedback once it has been received.

**How we calculate it:** We measure the number of hours from the **first human response to the last commit** on the PR, i.e. the back-and-forth editing time. If there were no commits after the first human response, `Editing Time` is `null`, and the time until the merge shows up as [`Merge Wait Time`](/metrics-and-definitions/process-metrics/flow-of-work/merge-wait-time.md) instead. See [here](/metrics-and-definitions/process-metrics/flow-of-work/change-lead-time.md#additional-calculation-notes-for-change-lead-time-and-subsets) for some additional calculation notes that apply from `Change Lead Time`.

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**What good looks like**

We recommend that `Editing Time` be under **11 hours**. 

This threshold is based on an internal analysis conducted by Multitudes in Q4 2026 across 40,000 PRs from a diverse range of customers and comparing against the SPACE and DORA research. We've intentionally focused on a recent subset of data because practices are changing so quickly now with AI.
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