Roadmap

Roadmap is a Python package that helps to analyze and visualize team's agile software development process. It provides insights into delivery performance and aims at creating more realistic roadmaps by leveraging the team historical performance.

Installation

Install from PyPI

$ pip install rmp

Usage examples

Configure backend and load data from Jira

fromrmp.backendimportBackendfromrmp.jiraimportJiraCloudConnectorimportos# For data storage, configure SQLAlchemy compatible URLos.environ['SQLALCHEMY_URL'] ='sqlite:///my_db.sqlite'# Create instance of backendbackend=Backend()
# Load databackend.add_connector(
JiraCloudConnector,
name='Jira Loader',
domain='example',
username='john.doe@example.com',
api_token='API_TOKEN',
jql='project = SPACE',
board_id=42
)
backend.load_data()

Analyze Flow Metrics

fromsqlalchemyimportcreate_enginefromrmp.flow_metricsimportFlowMetrics, Workflow, FilterKwArgsfromdatetimeimportdatetime, timezone# Create engine for data accessengine=create_engine(f"sqlite:///my_db.sqlite", echo=False)
# Configure workflow stagesworkflow=Workflow(
not_started=['To Do'],
in_progress=['In Progress', 'Code review', 'Testing'],
finished=['Done', 'Cancelled'],
)
# Define filtersfilter=FilterKwArgs(
exclude_item_types={"Bug"},
include_hierarchy_levels={0},
exclude_ranges=[
DateTimeRange("2024-12-23", "2025-01-05"), # Christmas period, team offlineDateTimeRange("2025-04-14", "2025-04-21"), # Holy Week, most of the team away
],
as_of=datetime.now(tz=timezone.utc), # Specify to query state at particular time moment
)
# Create instance of FlowMetricsfm=FlowMetrics(engine, workflow)
# Plot cycle time scatter chartfm.plot_cycle_time_scatter(**filter)
# Plot cycle time histogramfm.plot_cycle_time_histogram(**filter)
# Plot aging work in progress chartfm.plot_aging_wip(**filter)
# Plot throughput run chartfm.plot_throughput_run_chart(**filter)
# Plot cumulative flow diagramfm.plot_cfd(**filter)
# Find dates and probabilities to deliver 90 items using Monte Carlo simulationfm.plot_monte_carlo_when_hist(runs=10000, item_count=90, **filter)
# Find how many items can be delivered by date with their probabilities using Monte Carlo simulationtarget_date=datetime.now() +pd.Timedelta(days=30)
fm.plot_monte_carlo_how_many_hist(runs=10000, target_date=target_date, **filter)
# Output finished items and prioritized backlog with 85% confidence forecasted delivery dates df=fm.df_timeline_items(mc_when=True, mc_when_runs=1000, mc_when_percentile=85, **filter)
fm.styled_timeline_items(df) # Returns Styled represenation of timeline

Development

Select Python version using pyenv

pyenv local 3.11.8

Install Poetry dependencies

poetry install

Activate virtual environment

eval$(poetry env activate)

Run tests

pytest

Check code style and format

ruff check
ruff format

Run static type checker

mypy