Pull Request Analytics: How to Get Pull Request Cycle Time / Lead Time for Bitbucket

May 2, 2024
#Reporting#How To#Bitbucket
16 min
bitbucket code review

In this article, we’ll describe two ways to get pull request Cycle Time / Lead Time for Bitbucket Data Center using the Awesome Graphs for Bitbucket app.

What Pull Request Cycle Time is and why it is important

Pull Request Cycle Time / Lead Time is a powerful metric to look at while evaluating the engineering teams’ productivity. It helps track the development process from the first moment the code was written in a developer’s IDE and up to the time it’s deployed to production.

Pull Request Analytics

Please note that we define Cycle Time / Lead Time as the time between the developer’s first commit and the time it’s merged and will refer to it as Cycle Time throughout the article.

The Cycle Time is commonly composed of four metrics:

  • Time to open (from the first commit to open)
  • Time waiting for review (from open to the first comment)
  • Time to approve (from the first comment to approved)
  • Time to merge (from approved to merge)

With this information, you can get an unbiased view of the engineering department’s speed and capacity and find the points to drive improvement. It can also be an indicator of business success, as controlling the pull request Cycle Time can increase output and efficiency and deliver products faster.

How to find Cycle Time in Bitbucket

Using Awesome Graphs for Bitbucket, you can track the average Cycle Time of pull requests at the project and repository levels. Additionally, you can find the specific Cycle Time of a single pull request.

For each Bitbucket project and repository, the app displays the average time it takes to resolve pull requests as well as the breakdown of the average time by stage. You can also configure the report to see the average Cycle Time of a particular team or user in a chosen project or repo.

pull request cycle time report in Bitbucket

Below the report, you can find the list of all pull requests included in it, along with their Cycle Time.

find Cycle Time in Bitbucket

Clicking on a value in the Cycle Time column against a particular pull request allows you to see the breakdown of the Cycle Time and analyze each phase.

cycle time metrics

How to get Time to Open, Time to Review, Time to Approve, and Time to Merge metrics via REST API

Another way to get pull request Cycle Time is to export data from Bitbucket and build a custom report. You can get all the necessary pull request data from Awesome Graphs for Bitbucket and its REST API combined with Bitbucket’s REST API resources. We’ll use Python to make requests into the APIs, calculate and aggregate this data and then save it as a CSV file, like this:

The following script will do all this work for us:

import sys
import requests
import csv
from dateutil import parser
from datetime import datetime
bitbucket_url = sys.argv[1]
login = sys.argv[2]
password = sys.argv[3]
project = sys.argv[4]
repository = sys.argv[5]
since = sys.argv[6]
until = sys.argv[7]
s = requests.Session()
s.auth = (login, password)

class PullRequest:

    def __init__(self, pr_id, title, author, state, created, closed):
        self.pr_id = pr_id
        self.title = title
        self.author = author
        self.state = state
        self.created = created
        self.closed = closed

def parse_date_ag_rest(date):
    return parser.isoparse(date).replace(tzinfo=None, microsecond=0)

def get_date_from_timestamp(timestamp):
    return datetime.fromtimestamp(timestamp / 1000).replace(microsecond=0)

def subtract_dates(minuend, subtrahend):
    if minuend is None or subtrahend is None or minuend < subtrahend:
        return None
        return round(((minuend - subtrahend).total_seconds() / 86400), 2)

def get_pull_requests():
    pull_request_list = []

    get_prs_url = bitbucket_url + '/rest/awesome-graphs-api/latest/projects/' + project + '/repos/' + repository \
                  + '/pull-requests'

    is_last_page = False

    while not is_last_page:

        response = s.get(get_prs_url, params={'start': len(pull_request_list), 'limit': 1000,
                                              'sinceDate': since, 'untilDate': until}).json()

        for pr_details in response['values']:

            pd_id = pr_details['id']
            title = pr_details['title']
            author = pr_details['author']['user']['emailAddress']
            state = pr_details['state']
            created = parse_date_ag_rest(pr_details['createdDate'])

            if pr_details['closed'] is True:
                closed = parse_date_ag_rest(pr_details['closedDate'])
                closed = None

            pull_request_list.append(PullRequest(pd_id, title, author, state, created, closed))

        is_last_page = response['isLastPage']

    return pull_request_list

def get_first_commit_time(pull_request):
    commit_dates = []

    commits_url = bitbucket_url + '/rest/api/latest/projects/' + project + '/repos/' + repository + '/pull-requests/' \
                  + str(pull_request.pr_id) + '/commits'

    is_last_page = False

    while not is_last_page:

        commits_response = s.get(commits_url, params={'start': len(commit_dates), 'limit': 500}).json()

        for commit in commits_response['values']:
            commit_timestamp = commit['authorTimestamp']

        is_last_page = commits_response['isLastPage']

    if not commit_dates:
        first_commit = None
        first_commit = commit_dates[-1]

    return first_commit

def get_pr_activities(pull_request):
    counter = 0
    comment_dates = []
    approval_dates = []

    pr_url = bitbucket_url + '/rest/api/latest/projects/' + project + '/repos/' + repository + '/pull-requests/' \
             + str(pull_request.pr_id) + '/activities'

    is_last_page = False

    while not is_last_page:

        pr_response = s.get(pr_url, params={'start': counter, 'limit': 500}).json()

        for pr_activity in pr_response['values']:

            counter += 1

            if pr_activity['action'] == 'COMMENTED' and pr_activity['comment']['author']['emailAddress'] != pull_request.author:
                comment_timestamp = pr_activity['comment']['createdDate']
            elif pr_activity['action'] == 'APPROVED':
                approval_timestamp = pr_activity['createdDate']

            is_last_page = pr_response['isLastPage']

    if not comment_dates:
        first_comment_date = None
        first_comment_date = comment_dates[-1]

    if not approval_dates:
        approval_time = None
        approval_time = approval_dates[0]

    return first_comment_date, approval_time

print('Collecting a list of pull requests from the repository', repository)

with open(f'{project}_{repository}_prs_cycle_time_{since}_{until}.csv', mode='a', newline='') as report_file:
    report_writer = csv.writer(report_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)

    for pull_request in get_pull_requests():
        print('Processing pull request', pull_request.pr_id)

        first_commit_time = get_first_commit_time(pull_request)

        first_comment, approval = get_pr_activities(pull_request)

        cycle_time = subtract_dates(pull_request.closed, first_commit_time)

        time_to_open = subtract_dates(pull_request.created, first_commit_time)

        time_to_review = subtract_dates(first_comment, pull_request.created)

        time_to_approve = subtract_dates(approval, first_comment)

        time_to_merge = subtract_dates(pull_request.closed, approval)


print('The resulting CSV file is saved to the current folder.')

To make this script work, you’ll need to pre-install the requests and dateutil modules. The csvsys, and datetime modules are available in Python out of the box. You need to pass the following arguments to the script when executed:

  • the URL of your Bitbucket, 
  • login, 
  • password, 
  • project key, 
  • repository slug, 
  • since date (to include PRs created after), 
  • until date (to include PRs created before).

Here’s an example:

py script.py https://bitbucket.your-company-name.com login password PRKEY repo-slug 2023-11-30 2024-02-01

Once the script’s executed, the resulting file will be saved to the same folder as the script.

How to build Cycle Time report

After you generated a CSV file, you can process it in analytics tools such as Tableau, PowerBI, Qlik, Looker, visualize this data on your Confluence pages with the Table Filter and Charts for Confluence app, or integrate it in any custom solution of your choice for further analysis. 

how to find cycle timeAn example of the data visualized with Table Filter and Charts for Confluence.

In this article, you will find more details on how to build a Cycle Time report in Confluence using the Table Filter, Charts & Spreadsheets for Confluence app.

By measuring pull request Cycle Time, you can:

  • See objectively whether the development process is getting faster or slower.
  • Analyze the correlation of the specific metrics with the overall cycle time.
  • Compare the results of the particular teams and users within the organization or across the industry.

With Awesome Graphs for Bitbucket, you can gain more visibility into the development process and facilitate project management. Using the app as a data provider tool will help build tailored reports and address your particular needs. Plus, exclusively for our Data Center clients, we now offer a Premium Support subscription where our technical team having in-depth knowledge of Bitbucket data is on hand to write custom scripts and swiftly resolve your specific use cases.

Feel free to contact us if you’d like to discover whether our app can address your specific needs.


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