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Case studies

Codigy has three analytical lenses:

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All three are aimed for fast and safe releases. They are interconnected to provide actionable information on the root cause. An example: 


Your team might be slow but the root cause is the state of your codebase or an overwhelming amount of simultaneous features in development.

1. đź’–Team health

Case 1: Identify developer or manager performance issues
Above normal Churn can point to either the developer’s lack of knowledge or product manager continuously changing requirements.

 

Case 2: Improve release safety by boosting developer habits
Track active days to make sure all your developers contribute daily to avoid large & dangerous commits made once a week. 

 

Case 3: Track weekly performance of your team
Check contribution and active days to make sure your remote team is efficient and not overwhelmed by non-coding activities.

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2. 🧙‍♂️Branch health

Case 1: Get the best peer-reviewers based on your goal

Codigy automatically suggests reviewers for each pull request based on your peer-review goal. You can set your goal to: 

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  • Release safety - most knowledgable reviewers will be suggested.
  • Knowledge sharing - Codigy will be converting developers with the lowest codebase ownership into significant contributors to improve the bus factor.
  • Mixed - two reviewers will be suggested for each pull request, one from each category above.

 

Case 2: Focus on the most dangerous pull-requests

Codigy analyzes all pull requests and prioritizes them using the Scariness factor and Impact.


Scariness factor is based on:

  • Grade of the affected files;
  • Contributors’ role in the affected files;
  • Contributors’ experience in the project;
  • Contributors’ experience with the programming language used.​

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Impact is based on:

  • Number of affected files;
  • Affected file size;
  • Total size of changes.

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These two parameters will help you understand which pull requests are the riskiest and how deeply you need to review it.

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3. 🕵️‍♂️Codebase health

Case 1: Find the best developer for the task or knowledge sharing
Knowledge map highlights each developer's confidence on the map, it’s visual and simple.

 

Case 2: Get rid of inefficient or high-risk files in your codebase

  1. Use codebase health filters to find files where your developers are losing most time.
  2. Add them to your watchlist and follow suggestions for each file.

 

Available filters:

  • Lost knowledge - shows files that were last edited over 6 months ago or by contributors who left the team. In case you need to modify these files, you can expect lower efficiency and a high chance of defects.
  • No defined owner - highlights files that have no defined owner. Files with owner have a lower probability of defects and higher efficiency.
  • Microcontributors - highlights files that have developers with low confidence in the selected file. Micro-contributors increases the likelihood of defects.

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Upcoming filters:

  • Bus factor
  • Oversized files
  • Overcrowded files
  • Odd dependencies

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4. Communication methods

5. 🎉Upcoming: Account overview tool

Designed for teams who work on large or multiple products with hundreds of repositories.

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Enables you to:

  • Group any number of repositories into products;
  • Track and compare multiple product team performance;
  • Receive top-level reports;

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