Pystradamus is an evidence-based scheduling tool that mines Jira tickets to build probability curves for future work.
To install via pip (the recommended method)...
$ pip install git+https://github.com/Handshake/pystradamusNow have pystradamus dump a generic config file into your home directory
$ pystradamus config > ~/.pystradamus.cfgNext, edit your specific Jira settings in the config we just made. Pystradmus will use this configuration from then on. You can also override the config to use at run time with the -c flag.
Pystradamus works in two stages: Refresh and Predict. The refresh stage pull historical data from your Jira instance for a particular usage. Example...
$ pystradamus history -r joeThis will find all tickets that had an estimate in the past and build a local sqlite3 database of these estimates as well the time spent "in-progress" per ticket in Jira.
Predict mode is what projects the currently open and assigned tickets for this user into the future. For example after having refreshed user joe in the previous step we can now predict his currently open and assigned workload like so...
$ pystradamus history -p joe
HS-3503 [0.05] Update storefront promo edit preview to match actual dash
50% chance: 2014-04-11 19:19:59.466628
95% chance: 2014-04-11 21:24:51.305642
HS-3323 [0.1] Custom help page for buyers
50% chance: 2014-04-13 01:10:35.751628
95% chance: 2014-04-18 19:13:52.397642
HS-3411 [0.1] Buyer "My Settings Page"
50% chance: 2014-04-14 02:24:05.444628
95% chance: 2014-04-25 17:02:53.489642
HS-3295 [0.1] Storefront Order Screen Rework
50% chance: 2014-04-15 03:37:35.137628
95% chance: 2014-05-02 14:51:54.581642
HS-3310 [0.05] Storefront explanation for multiple ship dates
50% chance: 2014-04-15 04:15:35.758628
95% chance: 2014-05-03 11:24:50.788642What you've just been given is Joe's next 5 tickets, with their 50% and 95% confidence fits to the calendar. The more history for a user the better the predictive results.