Automating Code Reviews with Amazon CodeGuru

Warning: This lab simulates the use of Amazon CodeGuru due to the cost of CodeGuru Reviewer making it prohibitive for users to associate new repositories with CodeGuru.

Amazon CodeGuru is a machine-learning-powered solution for automating performance reviews and improving application performance. It does by acting both on your code repositories and actual applications, and it currently supports Java code.

When you submit a pull request to a CodeGuru-associated code repository, such as Amazon CodeCommit, CodeGuru will peruse the changes in the pull request and make recommendations based on its own analysis of millions of lines of external code, as comments on your file changes. Additionally, if you deploy CodeGuru as an agent in your application, over time it will make observations and recommendations to help you find and fix code issues such as performance leaks, wasted CPU cycles and more.

In this lab, you'll associate an Amazon CodeCommit repository with CodeGuru and create a pull request to be analyzed.

Learning Objectives 

Upon completion of this lab you will be able to:

• Be familiar with the basics of Amazon CodeGuru
• Use CodeGuru to analyze code in a repository

Intended Audience

This lab is intended for:

• Developers
• DevOps engineers

Prerequisites

You should be familiar with:

• Familiarity with the AWS console is helpful but not required

Updates

June 6th, 2023 - Updated instructions to improve clarity

October 12th, 2022 - Updated the lab to simulate the use of CodeGuru; student users no longer have permission to associate repositories with CodeGuru

May 19th, 2022 - Updated the instructions to reflect a specific CodeGuru Reviewer comment in the pull request

April 4th, 2022 - Updated the instructions and screenshots to reflect the latest UI