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README.rst

Evidence Based Scheduling

This is a simple python implementation of evidence based scheduling (à la Joel Spolsky). It generates confidence plots for project completion dates from a list of task estimates and a set of scheduling rules.

Dependencies

  • numpy
  • matplotlib (must be configured w/ interactive backend)
  • python-dateutil

Usage

To generate an ebs confidence curve, you must feed ebs.py a tasks file and a schedule rules file. Invoke as follows:

ebs.py tasks.csv rules.py

It will run monte-carlo simulations of the uncompleted tasks using velocities from completed tasks, generate a confidence plot of the results, and display it with pyplot.show().

Tasks file

The tasks file is a csv file with one entry for each task. The first row is assumed to be a header row. Each entry has the following fields, in order:

Project
A separate confidence curve will be plotted for each incomplete project, assuming purely sequential effort in the order that they appear.
Task
Individual task description. This field is ignored.
Estimate
Individual task estimate in hours.
Actual
For completed tasks only. Actual time taken to complete the task, in hours.

Rows with no estimate or actual time are ignored. It is legal to include tasks with actual completion time and no initial estimate. These will contribute to a buffer factor for unanticipated tasks. Below is an example:

project,task,estimate,actual
,,,
issue 35,data query,2,4.1
issue 35,view/template,2,2.9
issue 35,plots,1,2.4
issue 35,js,1,0.2
issue 35,css,1,3.6
issue 35,cms integration,4,
issue 35,deploy,2,
issue 35,bug fixes,,0.8
,,,
issue 27,spec formulas,0.5,3.6
issue 27,data query,3,0.3
issue 27,template,2,4.9
issue 27,js,4,7.1
issue 27,form,,1.1
issue 27,css,2,
issue 27,adapt offline classes,8,
issue 27,plots,3.3,

Rules file

The rules file must define rules as a list of (rule, anticipated effort) tuples, where rule is a dateutil.rrule instance specifying one or more calendar days and anticipated effort is the number of hours of work planned on those days. The rules are processed in order, and the first match for each day is used:

from datetime import datetime
from dateutil.rrule import *

dtstart = datetime(2015, 9, 4)
weekdays = rrule(WEEKLY, byweekday=(MO, TU, WE, TH, FR), dtstart=dtstart)
laborday = rrule(DAILY, dtstart=datetime(2015, 9, 7), count=1)
offsite_training = rrule(DAILY, dtstart=datetime(2015, 9, 10), count=4)

rules = [
    (laborday,  0),
    (offsite_training, 3),
    (weekdays,  8),
]

With the example inputs, the following plot is obtained:

example.png

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Project estimation with Evidence Based Scheduling

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