Jira Tool Usage Regularity Analysis for Project LIBCLOUD
1 Report Purpose
The purpose of this report is provide you, the analyst, with insight into how often project personnel are using Jira. Any tool usage, for example such as opening or closing a story or task, changing a value associated with a bug, or commenting on an epic, is counted when determining Jira usage regularity.
2 Benefits of Regular Tool Usage
If project team personnel use Jira regularly, then they tend to more accurately record the work that they have done and the results that they have obtained. Also, regular tool use helps the project’s lead get an up-to-date understanding of the work-in-progress, enabling the project lead to offer help where needed, coordinate team activities, and communicate accurately to customers.
3 Report Analytic Parameter Settings
3.1 Setting Explanations
This report can be adjusted by you, the analyst, at report creation time to focus on different aspects of project personnel’s Jira tool usage. The parameters that can be adjusted are:
| Parameter | Usage | Example | Default |
|---|---|---|---|
| Start and stop dates | Used when the analyst wants to examine Jira usage behavior during a narrower range than the project’s entire Jira usage history | If the project lead decided to start encouraging regular Jira usage after a certain date, such as January 1, 2021, the report can be set to only look at activities after that date. | Start = earliest detected tool usage, Stop = last detected tool usage |
| The personnel included in the tool usage analysis | Used when the analyst wants to examine Jira usage by a subset of all project personnel | If certain personnel are thought to be exemplars of consistent tool usage, the report can be set to look only at activities of those personnel. | All personnel are automatically included in the analysis. |
| Time interval basis to check for Jira usage regularity | Used when the analyst wants to compare the actual Jira usage to a target level of usage. This parameter is used in conjunction with interval multiple to determine how often Jira is expected to be updated. | Select ‘day’ to check for daily Jira usage. | Automatically set to ‘day’. |
| Interval Multiplier | Used in conjunction with the time interval to compare the actual Jira usage to the target level of usage. | If the time interval is set to ‘week’, set interval multiplier to ‘3’ for a target usage interval of 3 weeks. | Automatically set to ‘1’. |
| Weekends | Used to allow weekends (TRUE) in the analysis or ignore them (FALSE). | Setting weekends to ‘TRUE’ means that Jira is expected to be used on weekends. | Automatically set to ‘FALSE’. |
3.2 Parameter Settings Used In This Report
The following parameter settings were used:
This report examines activities between 2009-12-17 and 2019-05-31.
All project personnel were included in this analysis.
The analysis was done with the expectation that the team regularly used Jira at least every day.
The analysis that was performed ignored weekends.
4 Results
4.1 Fundamental Analysis
- The team is typically using Jira:
- On average, every 3.07 days,
- 50% of entries are made at a time interval of less than 1.77 days and 50% at a greater time interval,
- The largest time interval between team entries into Jira is 39 days made on 2010-03-22.
- The team’s Jira usage is not meeting expectations, on average.
A run chart of team Jira activities (rounded to the day-level) 
4.2 Advanced Analysis
No team’s actions are performed in a perfect mechanical cadence. To reflect that a team’s actions are not mechanically perfect and that variation will occur, processes are evaluated with respect to value called the process performance index1 (Ppk). This value reflects whether a process’ average performance, plus some amount of natural variation, will still meet expectations. Ppk is calibrated as follows:
- Ppk = 1 is acceptable. This means that the project’s average process performance, combined with natural variation in the process, will meet expectations.
- Ppk > 1 is excellent. This means that the project’s average process performance, even under disrupted or unusual circumstances, will meet expectations.
- Ppk < 1 indicates that improvement is desirable given a target tool usage of daily.. This means that the project’s average process performance, combined with natural variation in the process, will often not meet expectations.
Note: ppk indicates the team’s potential for meeting expectations on a consistent basis, not whether it met expectations at a particular point in time.
The LIBCLOUD project team’s process performance index is Ppk = -7.46. This indicates that the team should work to improve how regularly it uses Jira.
5 Recommendations
Before considering the following recommendations in the context of the LIBCLOUD project, you should make sure that the results reported above make sense in the context of the project, that project personnel are receptive to any changes, and that any recommendations to be implemented will help the project perform measurably better in a way that project personnel care about, project leadership cares about, or the project’s customer(s) care about.
To improve how often the project regularly uses Jira:
- Recommend that the project team lead set a target usage rate and encourage project personnel to try achieving that rate.
- Make sure project team members know how to use Jira and, if they do not, provide training or mentoring to help them use Jira better.
- Using an individualized version of this report, identify team members whose actions are contributing to the team not meeting expectations, then privately provide the report to these team members and assist them in improving their performance.
The process performance index, denoted Ppk, indicates if the team’s process is generally capable of meeting tool usage performance expectations, regardless of if the team meets the expectations or not on any specific day. In this report, a one-sided upper bound Ppu is used. The equation is \[P_{pu} = \displaystyle \frac{(USL - \overline{x})}{\hat{s}}\] where USL = day, \(\overline{x}\) is the mean of the data point values, and \(\hat{s}\) is the \(99.7^{th}\) quantile of the exponential distribution with \(\lambda = \frac{1}{\overline{x}}\).↩︎