Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/bitovi/jira-auto-scheduler
A statistical monte-carlo roadmapping tool for Jira.
https://github.com/bitovi/jira-auto-scheduler
Last synced: about 5 hours ago
JSON representation
A statistical monte-carlo roadmapping tool for Jira.
- Host: GitHub
- URL: https://github.com/bitovi/jira-auto-scheduler
- Owner: bitovi
- License: mit
- Created: 2021-04-04T19:21:37.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-11T13:53:44.000Z (7 months ago)
- Last Synced: 2024-04-14T10:58:11.175Z (7 months ago)
- Language: JavaScript
- Homepage: https://auto-scheduler.bitovi-jira.com/
- Size: 5.58 MB
- Stars: 25
- Watchers: 35
- Forks: 1
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: changelog.md
- License: LICENSE
Awesome Lists containing this project
README
# Statistical AutoScheduler for Jira
The [Statistical AutoScheduler](https://auto-scheduler.bitovi-jira.com/) is used to build probabilistic plans. Probabilistic plans account for uncertainty in estimating. The Statistical AutoScheduler produces a roadmap of epics as probability distribution:
[See it in action with mock data here!](https://auto-scheduler.bitovi-jira.com/)
## Features
- Loads epics from Jira and writes epic `Start date` and `Due date` to Jira.
- Supports multiple teams, team velocities, and tracks within a team.
- Specify the probability threshold to adjust to your risk tolerances
- Supports a wide variety of Jira configuration settings
## Need help or have questions?
This project is supported by [Bitovi, an Agile Project Management consultancy](https://www.bitovi.com/services/agile-project-management-consulting). You can get help or connect on our:
- [LinkedIn](https://www.linkedin.com/company/bitovi/)
- [Discord](https://discord.gg/J7ejFsZnJ4)
- [Twitter](https://twitter.com/bitovi)
Or, attend our next free & public [training](https://www.bitovi.com/events/program-management-webinar).
Or, you can [hire us](https://www.bitovi.com/services/agile-project-management-consulting) for training, consulting, or program management.
## Why
Accurate estimation is hard! But, [estimation is important](https://www.bitovi.com/academy/learn-agile-program-management-with-jira/estimating.html#why-estimate) because
it helps to know the cost of an initiative when prioritizing it.
Most teams build roadmaps by breaking down the work, getting a __single-time__ (or time-adjacent) __estimate__ of each work item, and sum up the work items to arrive at a due date. These dates never turn out to be accurate. This is for two reasons:
1. Getting a single-time-estimate hides the uncertainty present in the estimate. A estimate of 2 weeks of 10% certainty is widely different than an estimate of 2 weeks with 90% certainty.
2. Software work has a [log-normal blow-up factor that needs to be accounted for](https://erikbern.com/2019/04/15/why-software-projects-take-longer-than-you-think-a-statistical-model.html).
The Statistical AutoScheduler accounts for both of these points.
Finally, even with improved modeling, a single due date can never be provided. Instead, decisions should be made with an understanding of the inherent uncertainty. The Statistical AutoScheduler provides probabilities over a range of dates. For example, while the average due date is March 6th, there's still a 10% chance the work will extend beyond April 2nd:
Ultimately, using the AutoScheduler provides both:
- More accurate roadmaps
- And, plans that properly reflect uncertainty
... which helps teams make more informed decisions.
## How it works
The Statistical AutoScheduler loads a list of epics from Jira containing:
- An estimate in story points.
- A confidence from 10 to 100%.
- A list of blockers
- An optional "team". If no team is provided, the epic's team will be the epic's project name.
Then, given the team velocities provided to the app, it:
1. For each epic, randomly selects a "work time" based on the log-normal probability distribution of the epic's estimate and confidence
2. Schedules out the epics using the following algorithm:
1. Identify the longest critical path based on blockers
2. Schedule those epics in the first space allotted for the epic's team
3. Repeat
3. Finally, it repeats the scheduling algorithm __5000 times__, arriving at a probability distribution for the work as a whole
For more background, check out:
- [Why software projects take longer than you think: a statistical model](https://erikbern.com/2019/04/15/why-software-projects-take-longer-than-you-think-a-statistical-model.html)
- [Statistical Software Estimator](https://bitovi.github.io/statistical-software-estimator/)
## Quick Start
The following [Quick Start Video](https://youtu.be/wNOrmthMnFA) shows how:
- [0:10](https://youtu.be/wNOrmthMnFA?t=10) - Adding the `Story points median` and `Story points confidence` fields.
- [1:25](https://youtu.be/wNOrmthMnFA?t=85) - Creating the initial epics we will use for the roadmap
- [1:53](https://youtu.be/wNOrmthMnFA?t=113) - Adding the `Story points median`, `Story points confidence` and `Story point estimate` fields to the Epics screen.
- [2:43](https://youtu.be/wNOrmthMnFA?t=163) - Adding `Story points median` and `Story points confidence` values to epics.
- [4:08](https://youtu.be/wNOrmthMnFA?t=248) - Connecting the Statistical Auto Scheduler to your Jira instance.
- [4:27](https://youtu.be/wNOrmthMnFA?t=267) - Exploring the results.
- [5:10](https://youtu.be/wNOrmthMnFA?t=310) - Configuring the Statistical Auto Scheduler to write adjusted story points to the `Story point estimate` field.
- [5:36](https://youtu.be/wNOrmthMnFA?t=336) - Saving the plan back to Jira
[](https://youtu.be/wNOrmthMnFA)
## Full Set Up
To learn how to use this in context, read the full [Agile Program Management with Jira Training](https://www.bitovi.com/academy/learn-agile-program-management-with-jira.html).
To use it with its default configuration, you need to create and add the following fields to all epics:
- `Story points median`
- `Story points confidence`
Make sure these fields and the following fields are added to the Epic screens too:
- `Start date`
- `Due date`
## Use
Once set up, you just need to make sure each epic has:
- A "median" story point estimate. By default, it's assumed to be the `Story points median` field.
- A "confidence" in the estimate. By default, it's assumed to be the `Story points confidence` field.
- Blockers on other epics set.
## Estimating with Time instead of Story Points
The tool can be used with time-based estimates. You just need to do a little math to determine the velocity.