GitHub Action & GitHub App

In the initial version, I developed a GitHub Action workflow designed to activate upon every push to the repository and each pull request. The primary objectives were to execute coverage and linter checks for a Rust project, capture these outputs, and automatically comment on the pull request with the results. This workflow was successfully implemented and rigorously tested within the repository. ¹.

• Significant advancements were made in this iteration:

• A new GitHub Action workflow, ai-comment.yml, was created to operate on every push and pull request, enhancing our project’s automation and integration capabilities. ²

• A key task was to aggregate all Rust files following a specified schema, excluding any that matched defined patterns (‘test’, ‘schema’). This was achieved through a straightforward shell script, which efficiently processed and prepared these files for further analysis.

  • Note: I am sharing these files because they are on a public repo.

  • I’ve done this with a simple shell script below:

    rust_file_aggregator.sh

    #!/bin/bash
    
    # Initialize the file
    echo "" > rust.md
    
    # Find all the Rust files in the current directory
    # and its subdirectories.
    # and iterate over the files
    find . -name "*.rs" | while read file; do
        # Check if the file name matches the exclusion patterns
        # ['test', 'schema']
        if [[ $file != *test* && $file != *schema* ]]; then
            # Print the file name
            echo "Processing file: $file"
            # Append the file name to the output file
            echo "### FILE: $(basename $file)" >> rust.md
            # Append the file content to the output file
            cat $file >> rust.md
        fi
    done
    

  • These files were then transmitted to a mock API endpoint (Which I created), crafted using AWS Lambda and DynamoDB, demonstrating a practical application of serverless technologies in automating code review processes.

• Additionally, a webhook workflow was set up to trigger upon receiving webhook events ³, further integrated with two AWS Lambda functions for dynamic API simulation API-CRON and repository data management Webhook.

  • API-CRON Which was part of Mock API to randomly simulate the API behavior.
  • Webhook Which reads data from the DynamoDB and add comment to the PR.

This phase marked a significant shift in the project’s direction, with the introduction of a GitHub App and Webhook, both hosted on AWS Lambda, showcasing a complex, integrated development environment:

• The flow of that GitHub was as follow:
  • The user will install the GitHub App on their repository.
  • The GitHub App webhook will be triggered by the GitHub event.
  • The GitHub App will send the data to the AWS Lambda.
  • The AWS Lambda will process the data, save to the DynamoDB and commit the required file and secrets to the repository.
    • ai-comment.yml and rust_file_aggregator.sh will be committed to the repository.
    • The MOCK API url will be saved to the repository secrets.
• The version 2 Lambda was used as it is just updated the following:
  • The webhook Lambda updated to update the comment on the PR with the data from the Mock API.
• The new Lambda GithubAppWebhook added to handle the GitHub App webhook event.

This phase marked a more significant shift in the project’s direction, with the introduction of a GitHub App to manage the PR. Instead of committing the ai-comment.yml and rust_file_aggregator.sh to the repository, the GitHub App will handle the PR and comment on the PR with the data from the Mock API.

• The flow of that GitHub was as follow:
  • The app now listens to the PR event and comment on the PR with the data from the Mock API.
  • The Github app clone the user repo whenever pr created or syncronized.
    • The App perform the required step as previously was hapening on the repo.
• The lambda functions transformed to FastAPI`` and hosted on AWS EC2`.