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Why is analytics in Pull Requests so important and how it helps teams improve their workflow?

Analytics is not only a tool we use in technology, it’s also the way we think and act. Most of us use analytics to resolve problems in our day-to-day life, without even realizing it, from choosing the best apartment to rent, TV to buy, to making big business on how to approach new customers, merge firms and more. Obstacles to correct and precise decision-making and moving in the right paths in any spectrum of life are on every corner. Recognizing the right analytical approach and detecting setbacks that slow our progresses are key factors that can help us advance.

How are the processes in DevOps affected by analytics in Pull Requests?

Let’s walk to dev side of the DevOps’ lead time.

Lead Time is the time it takes to implement, test, and deliver changes. i.e. the time elapsed from the first code your team commits to the code successfully running in production.
You code, you commit, you discuss your code in reviews, you improve, you test, and you deploy.

Here’s a breakdown of the lead time with emphasis on coding and reviewing parts, also called Pull Request Cycle Time.


To gain an understanding of how things are done and how you can improve in your process, you should know where the hidden gems lie, and find out the details and reasons that lead to the problems. Identifying mistakes and receiving feedback in real-time is the backbone of high-performance. If you fail to receive and give the feedback continuously, it’s impossible for the team to improve and produce better results.


A long lead time does not necessarily mean insufficient software engineering when the outcome can prevent fatal errors or the solution at the end prevents deep technical debts. But where do you lose unnecessary time? On maybe unnecessary waiting times, too much code churns after the initial commit , too long coding without any feedback, or too many different opinions?

Tips on how to detect your delays in Pull Requests


How do you detect waiting times ? 


Look at the cycle times from opening a pull request to merging one and detect the ones with long times without any activities. As notifications are integrated in Slack, you will always be informed about the missed review. 

How do you detect too many code churns or too many different opinions? 


Far too often happens that you do not understand the requirements or they are unclear once the pull request requires too many changes. Look at the activities and see if there are many changes after the initial commit.


How do you detect ‘working in silos’ situations?


Feedback is a robust tool for boosting productivity and achieving better results.The importance of feedback in Pull Requests is hard to overestimate: sharing information on what can and needs to be fixed and improved, helps optimize work processes and get things done in less time, thus working in the dark for too long can only instigate complications. Look at the time from first commit or starting a task until opening a pull request. 


In-depth assistance with Pull Request Analytics 


Speed and efficiency are two of the most important elements of collaboration. The more information you have about your pull request workflow, the more you can streamline the collaboration process.

Further tips and precise answers to the question of how to help teams in software development processes and improvement of the cycle times, can be also found in Atlassian product  Pull Request Analytics for Bitbucket , with full assistance in the following: 


  • Finding the Pull Requests that need action in the global list of all open pull requests filtered by project, programming language, due date, size, activity, etc.
  • Measuring and improving your Pull Request workflow by visualizing key aspects of your workflow or leveraging the retrospective summary during sprints.

Getting notified for open and due Pull Requests by getting Slack reminders during the sprint, and enabling your users to build intelligent workflows.

Pull Request.png

Take a look at the statistics of your pull request workflow. These can help you visualize the workflow and more easily spot areas of improvement. You’ll be able to see key metrics such as which repositories and their pull requests have long cycle times or large lines of code, allowing you to better understand where your bottlenecks stem from.

If you follow these tips above, you’ll be sure to get the most out of your pull requests so you can continuously improve your team’s collaboration.



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