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https://github.com/rnwolf/jira-metrics-extract

CLI tool to extract metrics from Jira, save data and charts locally for further analysis & presentation. Kanban view of world as per Actionable Agile Metrics
https://github.com/rnwolf/jira-metrics-extract

Last synced: 26 days ago
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CLI tool to extract metrics from Jira, save data and charts locally for further analysis & presentation. Kanban view of world as per Actionable Agile Metrics

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README 

        
JIRA Metrics Data extract utility

=================================



This utility helps extract data from JIRA for processing with the

ActionableAgile™ Analytics tool (https://www.actionableagile.com/analytics-tools/),

as well as ad-hoc analysis using Excel.



This version of the tool is a fork from Martin (https://github.com/optilude/jira-cycle-extract). It has been modified to produce metrics based on issue sizes in adition to the existing metrics based on issue counts. Additional exports of issue data size changes enable easier backlog change analysis.



It will produce a CSV file with one row for each JIRA issue matching a set of

filter criteria, containing basic information about the issue as well as the

date the issue entered each step in the main cycle workflow.



This data can be used to produce a Cumulative Flow Diagram, a cycle time

scatterplot, a cycle time histogram, and other analytics based on cycle time.



To make it easier to draw these diagrams, the tool can also be used to output

CSV files with pre-calculated values for charting in e.g. Excel.



Finally, if you have the right dependencies installed, it can output basic

charts as images.



Installation

------------



Install Python 2.7 or Python 3.5 and pip. See http://pip.readthedocs.org/en/stable/installing/.



Install using `pip`::



    $ pip install jira-metrics-extract



To install cli and all dependencies down load the requirements.txt file and then install using `pip`::



    $ pip install -r requirements.txt



This will install a binary called `jira-metrics-extract`. You can test that it was

correctly installed using::



    $ jira-metrics-extract --help



If you are using Anaconda then download environment.yml.



Install using `conda`::



    $ conda env create -f environment.yml



Activate the new environment:



Linux, OS X: source activate jira-metrics

Windows: activate jira-metrics



You can test that it was correctly installed using::



    $ jira-metrics-extract --help



If you only installed the jira-metrics-extract with pip and you want to use the built-in charting capabilities, then you need to install Seaborn

(which in turn installs Matplotlib and SciPy) and Statsmodels. You can get

these with the `charting` extra::



    $ pip install jira-metrics-extract[charting]



These dependencies are not installed by default because they can sometimes

be a bit tricky to install.



Using Docker

------------



If you have Docker installed, you can run `jira-metrics-extract` as a docker image::



    $ docker run --rm -it -v "$PWD":/data optilude/jira-metrics-extract:latest --help



This will map the working directory (`/data`) of the containerised

application to the current working directory (`$PWD`). Any files you

specify on the command line (without any further path prefixes) will be

read from or output to the current directory, e.g::



    $ docker run --rm -it -v "$PWD":/data myhomedir/jira-metrics-extract:latest config.yml cycle.csv



Configuration

-------------



Write a YAML configuration file like so, calling it e.g. `config.yaml`::



        # How to connect to JIRA?

        Connection:

            domain: https://myserver.atlassian.net/

            username: myusername # If missing or and not in an environment var, you will be prompted at runtime

            password: secret     # If missing or and not in an environment var, you will be prompted at runtime

            verify: False # If missing True is assumed.



        #Criteria:

        #    Project:

        #        - ATCM # JIRA project key. Can query across multiple projects, add additional rows.

        #    Issue types: # Which issue types to include? - Delivery Story - Task - Bug

        #        - Delivery Story

        #        - Bug

        #    Valid resolutions: # Which resolution statuses to include (unresolved is always included)

        #        - SIGNED OFF

        #        - DONE

        #    JQL:  fixVersion = "ATCM Release 1.0" and priority = Must  AND status != Withdrawn

        #    # Additional filter as raw JQL, optional  eg labels != "Spike"



        #Criteria:

        #     Project:

        #        - ATCM # JIRA project key. Can query across multiple projects, add additional rows.

        #     Issue types:

        #        - Delivery Story

        #        - NFR

        #     Valid resolutions:

        #        - SIGNED OFF

        #        - DONE

        #     JQL: fixVersion = "ATCM Release 1.0" 



        # Compound query when work done by multiple teams as part of a larger programme.

        Queries:

            Attribute: ATCM

            Criteria:

                - Value: Team ATCM

                  Project:

                      - ATCM # JIRA project key. Can query across multiple projects, add additional rows.

                  Issue types: # Which issue types to include? - Delivery Story - Task - Bug

                      - Delivery Story

                      - Bug

                  Valid resolutions: # Which resolution statuses to include (unresolved is always included)

                      - SIGNED OFF

                      - DONE

                  JQL:  fixVersion = "ATCM Release 1.0" and priority = Must  AND status != Withdrawn



                - Value: Team NFR

                  Project:

                      - ATCM # JIRA project key. Can query across multiple projects, add additional rows.

                  Issue types: # Which issue types to include? - Delivery Story - Task - Bug

                      - NFR

                  Valid resolutions: # Which resolution statuses to include (unresolved is always included)

                      - SIGNED OFF

                      - DONE

                  JQL: fixVersion = "ATCM Release 1.0" and priority = Must AND status != Withdrawn



        # Describe the workflow. Each step can be mapped to either a single JIRA

        # status, or a list of statuses that will be treated as equivalent

        # At least two steps are required. Specify the steps in order.



        Workflow:

            Open:

              - OPEN

              - To Do

              - New

              - Not Started

              - Parked

            Analysis:

              - REFINE

              - CANDIDATE FOR SPRINT

              - REFINE

              - Research

            Committed:

              - READY FOR SPRINT

              - Prioritised

            Develop:

              - Reopened

              - BUILD

              - DEVELOPMENT COMPLETE

              - READY FOR BPO SIGN OFF

              - BLOCKED

              - Awaiting review

              - In Progress

              - In review

              - Ready to Test

              - Awaiting Sign Off

              - In QA

              - Integrated

              - Reviewed

              - In Acceptance

              - Story Development

              - Doing

            Done:

              - BUILD TO RELEASE

              - HASS QA

              - READY FOR LIVE

              - DONE

              - Closed

              - Resolved

              - Signed Off



        # High level Actionable Agile Metrics approach to viewing work flow

        # Map key columns to open, backlog, committed, final, complete, abandoned

        Workflow StatusTypes Mapping:

            Open: open

            Analysis : backlog

            Committed : committed

            Develop : final

            Done : complete



        # Map field names to additional attributes to extract

        Attributes:

            #Components: Component/s

            #Priority: Priority

            Release: Fix version/s

            StoryPoints: Story Points

            Labels: labels



        #Known values:

        #    Release:

        #        - "ABC Release 1.0"



        # Additional parameters that can be overridden by command line options

        Max Results: 1000

        Quantiles:

            - 0.5

            - 0.85

            - 0.95

        # This could be date (e.g. 8th Aug 2016) or relative date as in example below

        Charts From: 1 month ago

        Charts To: today 



If you are unfamiliar with YAML, remember that:



* Comments start with `#`

* Sections are defined with a name followed by a colon, and then an indented

  block underneath. `Connection`, `Criteria`, `Workflow` and `Attributes` area

  all sections in the example above.

* Indentation has to use spaces, not tabs!

* Single values can be set using `Key: value` pairs. For example,

  `Project: ABC` above sets the key `Project` to the value `ABC`.

* Lists of values can be set by indenting a new block and placing a `-` in front

  of each list value. In the example above, the `Issue types` list contains

  the values `Story` and `Defect`.



The sections for `Connection`, `Criteria` and `Workflow` are required.



Under `Conection`, only `Domain` is required. If not specified, the script will

look for environment variables and if those are not found it will prompt for 

both or either of username and password when run.



Under `Criteria`, all fields are technically optional, but you should specify

at least some of them to avoid an unbounded query. `Issue types` and

`Valid resolutions` can be set to either single values or lists.



Under `Workflow`, at least two steps are required. Specify the steps in order.

You may either specify a single workflow value or a list (as shown for `Done`

above), in which case multiple JIRA statuses will be collapsed into a single

state for analytics purposes.



The file, and values for things like workflow statuses and attributes, are case

insensitive.



When specifying attributes, use the *name* of the field (as rendered on screen

in JIRA), not its id (as you might do in JQL), so e.g. use `Component/s` not

`components`.



The attributes `Type` (issue type), `Status` and `Resolution` are always

included.



When specifying fields like `Component/s` or `Fix version/s` that may have

lists of values, only the first value set will be used.



Multiple queries

----------------



If it is difficult to construct a single set of criteria that returns all

required issues, multiple `Criteria` sections can be wrapped into a `Queries`

block, like so::



    Queries:

        Attribute: Team

        Criteria:

            - Value: Team 1

              Project: ABC

              Issue types:

                  - Story

                  - Bug

              Valid resolutions:

                  - Done

                  - Closed

              JQL: Component = "Team 1"



            - Value: Team 2

              Project: ABC

              Issue types:

                  - Story

                  - Bug

              Valid resolutions:

                  - Done

                  - Closed

              JQL: Component = "Team 2"



In this example, the `Component` field in JIRA is being used to signify the team

delivering the work, but may also be used for other things. Two JIRA queries

will be run, corresponding to the two `Criteria` blocks.



In addition, a new column called `Team` will be added to the output, as

specified by the `Attribute` field under `Queries`. For all items returned by

the first query, the value will be `Team 1` as per the `Value` field, and for

all items returned by the second query, it will be `Team 2`.



Multi-valued fields

-------------------



Some fields in JIRA can contain multiple values, e.g. `fixVersion`. By default,

the extractor will use the first value in such a field if one is specified in

the `Attributes` block. However, you may want to extract only specific values.



To do so, add a block like the following::



    Attributes:

        Release: Fix version/s



    Known values:

        Release:

            - "R01"

            - "R02"

            - "R03"



The extractor will pick the first "known value" found for the field. If none of

the known values match, the cell will be empty.



Running

-------



To produce the basic cycle time data, run `jira-metics-extract` passing the name

of the YAML configuration file and the name of the output CSV file::



    $ jira-metrics-extract config.yaml data.csv



This will extract a CSV file called `data.csv` with cycle data based on the

configuration in `config.yaml`, in a format compatible with the

ActionableAgile toolset.



If you prefer Excel files for manual analysis::



    $ jira-metrics-extract --format=xlsx config.yaml data.xlsx



If you prefer JSON::



    $ jira-metrics-extract --format=json config.yaml data.json



The JSON format can be loaded by the Actionable Agile Analytics tool if you

self-host it and the single-page HTML file for the AAA tool and the JSON file

are accessible from the same web server, via a URL parameter::



    http://myserver/analytics.html?url=data.json



You can specify a path or full URL, but due to same-origin request restrictions,

your browser is unlikely to let you load anything not served from the same

domain as the analytics web app itself.



**Note:** When the `--format` is set, it applies to all files written, not

just the main cyle data file (see other options below). It is important to be

consistent with the file extensions. In particular, if you are using the `xlsx`

format you should also make sure all output files use a `.xlsx` extension.



There are lots more options. See::



    $ jira-metrics-extract --help



Use the `-v` option to print more information during the extract process.



Use the `-n` option to limit the number of items fetched from JIRA, based on

the most recently updated issues. This is useful for testing the configuration

without waiting for long downloads::



    $ jira-metrics-extract -v -n 10 config.yaml data.csv



To produce **Cumulative Flow Diagram statistics**, use the `--cfd` option::



    $ jira-metrics-extract --cfd cfd.csv config.yaml data.csv



This will yield a `cfd.csv` file with one row for each date, one column for each

step in the workflow, and a count of the number of issues in that workflow state

on that day. To plot a CFD, chart this data as a (non-stacked) area chart. You

should technically exclude the series in the first column if it represents the

backlog!



To produce **cycle time scatter plot statistics**, use the `--scatterplot` option::



    $ jira-metrics-extract --scatterplot scatterplot.csv config.yaml data.csv



This will yield a `scatterplot.csv` file with one row for each item that was

completed (i.e. it reached the last workflow state), with columns giving the

completion date and the number of days elapsed from the item entering the first

active state (i.e. the second step in the workflow, on the basis that the first

item represents a backlog or intake queue) to the item entering the completed

state. These two columns can be plotted as an X/Y scatter plot. Further columns

contain the dates of entry into each workflow state and the various issue

metadata to allow further filtering.



To be able to easily draw a **histogram** of the cycle time values, use the

`--histogram` option::



    $ jira-metrics-extract --histogram histogram.csv config.yaml data.csv



This will yield a `histogram.csv` file with two columns: bin ranges and the

number of items with cycle times falling within each bin. These can be charted

as a column or bar chart.



To find out the 30th, 50th, 70th, 85th and 95th **percentile cycle time** values,

pass the `--percentiles` option::



    $ jira-metrics-extract --percentiles percentiles.csv config.yaml data.csv



To calculate different percentiles use the `--quantiles` option::



    $ jira-metrics-extract --percentiles percentiles.csv --quantiles=0.3,0.5,0.8 config.yaml data.csv



Note that there should not be spaces between the commas!



To find out the **daily throughput** for the last 60 days, use the

`--throughput` option::



    $ jira-metrics-extract --throughput throughput.csv config.yaml data.csv



To use a different time window, e.g. the last 90 days::



    $ jira-metrics-extract --throughput throughput.csv --throughput-window=90 config.yaml data.csv



The various options can be used in combination, and it is technically OK to

skip the second positional (`data.csv`) parameter (in which case the file will

not be written).



If you have charting dependencies installed (see above), there are various

options available to allow you to draw **charts**, for example::



    $ jira-metrics-extract --charts-scatterplot=scatterplot.png config.yaml data.csv



The available charts are:



* `--charts-scatterplot` to draw a **scatterplot** of cycle times, with percentile lines

* `--charts-histogram` to draw a **histogram** of cycle times, with percentile lines

* `--charts-cfd` to draw a **Cumulative Flow Diagram**

* `--charts-throughput` to draw a daily **throughput bar chart**

* `--charts-burnup` to draw a simple **burn-up** chart (completed item count vs. time)

* `--charts-burnup-forecast` to draw a **burn-up chart with a Monte Carlo simulation**

  showing paths towards a completion target. The completion target will by default

  be the number of items in the backlog, but can be set explicitly with the

  `--charts-burnup-forecast-target` options. The simluation by default uses

  100 trials. The number of trials can be set with the

  `--charts-burnup-forecast-trials` option. You can set a deadline marker with the

  `--charts-burnup-forecast-deadline` option, which should be set to a date. If

  you also set `--charts-burnup-forecast-deadline-confidence` to a fraction (e.g.

  `0.85`) it will be used to find a confidence interval in the simulation to which

  the deadline will be compared.

* `--charts-wip` to draw a **WIP boxplot** showing min, max, median and mean WIP

  by week. By default, this will show the last 5 or 6 weeks' of data (depending

  on the weekday). You can change this with the `--charts-wip-window` parameter,

  set to a number of weeks.

* `--charts-ageing-wip` to draw an **ageing WIP chart**: a scatter plot of current

  cycle time against state in the cycle, i.e. how items are trending towards completion.

* `--charts-net-flow` to show a bar chart of the **weekly net flow**:

  departures - arrivals. By default, this will show the last 5 or 6 weeks' of

  data (depending on the weekday). You can change this with the

  `--charts-net-flow-window` parameter, set to a number of weeks.



Also note: all the `--charts-*` options have a corresponding `--charts-*-title`

option that can be used to set a title for the chart.



Finally, to limit the date range of the data shown in the charts, you can use the

options `--charts-from` and `--charts-to` to specify a starting and/or ending 

date (inclusive). Both are optional.



Troubleshooting

---------------



* If Excel complains about a `SYLK` format error, ignore it. Click OK. See

  https://support.microsoft.com/en-us/kb/215591.

* JIRA error messages may be printed out as HTML in the console. The error is

  in there somewhere, but may be difficult to see. Most likely, this is either

  an authentication failure (incorrect username/password or blocked account),

  or an error in the `Criteria` section resulting in invalid JQL.

* If you aren't getting the issues you expected to see, use the `-v` option to

  see the JQL being sent to JIRA. Paste this into the JIRA issue filter search

  box ("Advanced mode") to see how JIRA evaluates it.

* Old workflow states can still be part of an issue's history after a workflow

  has been modified. Use the `-v` option to find out about workflow states that

  haven't been mapped.

* Excel sometimes picks funny formats for data in CSV files. Just set them to

  whatever makes sense.

* If you are on a Mac and you get an error about Python not being installed as

  a framework, try to create a file `~/.matplotlib/matplotlibrc` with the

  following contents::



    backend : Agg

* To install the charting dependencies on a Mac, you probably need to install a

  `gfortran` compiler for `scipy`. Use Homebrew (http://brew.sh) and install the

  `gcc` brew.



Ad-hoc analysis

---------------



Sometimes, you may want to perform more exploratory, ad-hoc analysis of the

cycle data. `jira-metrics-extract` uses Python Pandas (http://pandas.pydata.org)

to do most of its heavy lifting, and Pandas provides a rich environment for

data science.



The Jupyter Notebook (http://jupyter.org) is a popular way to conduct

interactive, ad-hoc analysis using Pandas (and more!).



If you have this running, here's an example of a notebook that uses

`jira-cycle-extract` to query JIRA with a given YAML file configuration and

makes the data available for further analysis::



    import getpass

    import datetime



    import pandas as pd

    import numpy as np

    import matplotlib.pyplot as plt

    import matplotlib as mpl

    import seaborn as sns



    from jira import JIRA

    from jira_metrics_extract import cycletime, config



    # Print charts in the notebook, using retina graphics

    %matplotlib inline

    %config InlineBackend.figure_format = 'retina'

    sns.set_context("talk")



    # Prompt for JIRA username, password and config file:

    username = raw_input("Username:")

    password = getpass.getpass("Password:")

    config_filename = raw_input("Config file:")



    # Parse options

    options = {}

    with open(config_filename) as config_file:

        options = config.config_to_options(config_file.read())



    # Connect to JIRA

    jira = JIRA(options={'server': options['connection']['domain']}, basic_auth=(username, password))



    # Fetch issues and calculate cycle data as a Pandas DataFrame

    q = cycletime.CycleTimeQueries(jira, **options['settings'])

    cycle_data = q.cycle_data(verbose=False)



    # Calculate other DataFrames for CFD, scatterplot, histogram, percentile and throughput data

    cfd_data = q.cfd(cycle_data)

    scatter_data = q.scatterplot(cycle_data)

    histogram_data = q.histogram(cycle_data)

    quantiles=[.5,.85,.95]

    percentile_data = q.percentiles(cycle_data, percentiles=quantiles)

    daily_throughput_data = q.throughput_data(cycle_data[cycle_data])



You can now do all kinds of analysis on the DataFrames (`cycle_data`, `cfd_data`

and so on).