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Make The Most Of Your One-to-Ones With One-to-One Analytics

 

A one-to-one is a valuable opportunity for software engineers to learn from the past and chart the course of their future.  During a one-on-one, a software engineer has the opportunity to have a personal discussion with their manager about their past work and learn how to improve so that they can get where they want to go.  In order to make the most out of one-on-ones, however, both sides should have certain key performance metrics (KPIs) handy.

Why Do KPIs Matter?

Going into a one-to-one without the proper statistics and data about the software engineer’s work makes these meetings ineffective for both sides.  If the engineer doesn’t have data to support the discussion, then it’s hard for them to remember the areas of development when doing a retrospective of their work.  When armed with data-driven insights, however, the manager can help develop individual engineers and continually improve the team as a whole.

Collecting the necessary metrics for a one-to-one meeting can be difficult without the proper software.  But thanks to One-to-One Analytics, you can easily collect metrics for continuous improvement to bring to your next one-to-one meeting.

One-to-One Analytics

One-to-One Analytics is a Confluence integrated macro that a user can configure during a one-to-one using GitHub repository data for a single user.  This app provides metrics and helps software engineers visualize KPIs by using interactive charts. 

Here are a few of the KPIs that you can measure with One-to-One Analytics:

Time To Open (TTO)

This is the time from first commit to pull request creation.  Working too long in isolation prevents you from getting early feedback from your peers, so being able to measure your TTO is imperative to improving your work.

Lead Time (LT)

The time from opening to merging a pull request is another important metric to measure.  When the time for this process is minimized, you reduce the risk of merge conflicts and collect feedback from your customers much earlier.  One-to-One Analytics measures LT to help you gauge whether or not you have room to grow in that area.

Lines of Code (LOC)

One-to-One Analytics allows you to see the size of your pull requests, which is the sum of added and deleted lines of code from a pull request.  Small pull requests are more motivating to review, and can earn you higher-quality feedback.

Merges Without Reviewers

This includes the pull requests that aren’t reviewed before merging.  Team work is essential, as having another pair of eyes review your work is always a good idea.

With the use of the data provided by One-to-One Analytics, you can have more productive one-to-one meeting discussions.  You can document this data in Confluence so that you can add goals and feedback to reference in future meetings.  If you’re interested in maximizing the results you get from one-to-ones, then you can find One-to-One Analytics on the Atlassian Marketplace.  Keep an eye out for the addition of more KPIs, including Bitbucket Data Center support and other additional features.

https://marketplace.atlassian.com/apps/1226155/one-to-one-analytics?hosting=cloud&tab=overview

marketplacebanner.jpg

1 comment

We are pleased to announce several updates in the latest version of One-to-One Analytics:

  • Bitbucket Data Center is now supported as a data source
  • Reviewer-based KPIs
    • Comments Per Review: includes the total number of pull request comments made during a pull request review. The team benefits from quality feedback so staying involved as a PR reviewer helps everyone. 
    • Review Time (RT): is the time between a pull request being created and the pull request review. Slow response times as a PR reviewer can slow the entire team down. Keeping an eye on review times helps to avoid bottlenecks.

 

- Justin @ Mibex Software

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