Time in status chart

Time in status chart

The Time in Status Chart shows how much time issues spend in each workflow status (the Time in status metric), from active work to waiting stages. It helps teams pinpoint bottlenecks, track how work flows through different statuses, and evaluate how efficiently tasks move across the process.

It helps you answer questions such as:

  • How long do issues stay in each workflow status?

  • How much total time is spent in active work vs. waiting stages (e.g., waiting for a customer input)?

  • Which statuses or groups (e.g., In Progress, Ready for Test) accumulate the most time?

  • How does the time in status vary between sprints or releases?

  • Are recent workflow or process changes reducing waiting time?

  • Which specific tasks contribute most to the time spent in each status?

How to read the chart

All settings shown in the chart are configurable.

In the example below, the chart displays the total time work items spent in each active status over the last six sprints (1️⃣) in absolute values (2️⃣). Each stacked bar represents the sum of time issues stayed In Progress, In Code Review, and In Testing (3️⃣). The chart shows that most time is consistently spent in In Progress (4️⃣), indicating that active work occupies nearly half of each sprint’s total cycle.

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Configure the chart to include a Waiting group alongside the In Progress group to distinguish between active work and blocked time. The Ready for Test status belongs to the Waiting group and appears in a separate color (5️⃣).

In this setup, you can see that the Waiting group accounts for most of the total time. This reveals that bottlenecks occur not during active work but while tasks are waiting for the next stage, such as QA or release validation.

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You can switch the chart to the Average view (6️⃣), showing how long, on average, an issue spends in each workflow status per sprint or interval. The lines represent mean values for the In Progress (7️⃣) and Waiting (8️⃣) groups. This view helps teams monitor trends in average waiting time and verify whether recent process changes are improving flow efficiency.

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