Screenshot: Control Chart
- Issue details: See the cycle time for an issue.
- Time scale: Select a time period to display on the control chart.
- Zoom in: Highlight an area of the chart to focus on a specific time period.
- Refine report: Select the columns and filters you want data for.
Viewing the Control Chart
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- Click Reports, then select Control Chart.
- Configure the chart as desired. The screenshot at the top of this page highlights the controls that you can use to configure the Control Chart.
If you are using Internet Explorer 8, the Control Chart will not work.
Printing the Control Chart
Understanding the Control Chart
Before you start using the Control Chart, you should get to know how it works. The following questions and answers cover the key functionalities of the Control Chart:
Cycle time is the time spent working on an issue — typically, the time taken from when work begins on an issue to when work is completed, but it also includes any other time spent working on the issue. For example, if an issue is reopened, worked on, and completed again, then the time for this extra work is added to the cycle time.
Lead time is similar to cycle time, but is the time taken from when an issue is logged (not when work begins) until work is completed on that issue.
The statuses used to calculate cycle time depend on the workflow you're using for your project. You should configure the Control Chart to include the statuses that represent the time spent working on an issue. Note, the Control Chart will attempt to select these statuses automatically.
For example, if you are using the '
Tip: you can also configure the Control Chart to show lead time data instead of cycle time data. Just select the statuses that represent the time spent on an issue; from the time it is raised until work was completed.
The rolling average (blue line on the chart) is issue-based, not time-based. For every issue shown on the chart, the rolling average (at that point in time) is calculated by taking the issue itself, X issues before the issue and X issues after the issue, then averaging their cycle times. 20% of the total issues displayed (always an odd number and a minimum of 5 issues) is used in the calculation.
For example, in the screenshot below, at the point of time where an issue (green dot) is shown, the rolling average is calculated as follows:
If the Timeframe is reduced to 'Past two weeks', the number of issues used would reduce, as there are fewer total issues available to use for the calculations.
This method produces a steady rolling average line that shows outliers better (i.e. rolling average doesn't deviate as sharply towards outliers). The rolling average line is also easy to understand, as the inflections are related to the positions of issues.
If you would like to know more about why the rolling average calculation is based on a percentage of the total issues, rather than a time period, see Comparing different methods of calculating the rolling average on the Control Chart.
The blue shaded area of the control chart represents the standard deviation — that is, the amount of variation of the actual data from the rolling average.
The standard deviation gives you an indication of the level of confidence that you can have in the data. For example, if there is a narrow blue band (low standard deviation), you can be confident that the cycle time of future issues will be close to the rolling average.
As shown on the chart legend, each dot represents an issue or a group (cluster) of issues:
If the maximum Elapsed Time value on the chart is less than 30 days, then a linear scale is used for the y-axis. If it is 30 days or greater, than a cube-root power scale is used.
When you change the Timeframe, you may include issues with an elapsed time of greater than 30 days when you previously did not, or vice versa. This will change the scale, as described above.
Tips and examples
Learn how to tweak your Control Chart to show the data you need with the following examples:
Tip 1: Remove unwanted outliers
The Control Chart can help you identify outliers. On closer examination, you may determine that certain outliers are invalid due to human error. For example, you may have a story that was started but stopped, then eventually dropped back to the backlog, but not returned to the 'To Do' status. The time that the issue spent 'In Progress' would incorrectly skew the data for your Control Chart.
To remove unwanted outliers from your Control Chart, add a label to each outlier issue (e.g. outlier) and create a Quick Filter with this JQL:
Tip 2: Remove triage casualties
In a Control Chart, you generally want to track the issues that are resolved as 'Fixed'. Issues that are triaged and resolved as a duplicate, answered, tracked elsewhere, etc can skew the data, bringing the average cycle time down considerably.
To remove triage casualties from your Control Chart, create a Quick Filter with this JQL:
Tip 3: Exclude current work
The Control Chart shows data for issues that have been in a selected column, but are no longer in a selected column. This gives the cycle time (total elapsed time) for the issues. However, by default, this will include issues that are still moving across the board.
To view the data for completed work only in your Control Chart, create a Quick Filter with this JQL:
Learn how to interpret a Control Chart with the following examples:
If you encounter an issue that is not on this list, please raise it in our issue tracker.