1. Automated Labeling of GitHub Issues for Triage with NLP


    To automate the labeling of GitHub issues for triage using Natural Language Processing (NLP), you would typically need an NLP model that can understand the content of the issues and assign labels accordingly. This could involve a separate process, like a machine learning service or a script that processes incoming GitHub issues.

    However, managing this process with Pulumi involves setting up the infrastructure to support the automation. This could include provisioning a GitHub repository, setting up webhooks to trigger the NLP process when new issues are created, and deploying a service (like an AWS Lambda or Azure Function) that receives the webhook events, processes them through an NLP model, and applies labels to the issues using the GitHub API.

    Below is a basic example of how you might set up such a system using Pulumi in Python. This program assumes that you already have a machine learning model hosted somewhere that you can call (via HTTP, for example) to get issue labels based on the issue content.

    We will use the pulumi_github package to work with GitHub resources:

    • github.IssueLabel: Represents a label that can be applied to GitHub issues.
    • github.RepositoryWebhook: Represents a webhook on a GitHub repository that can invoke an external service (like an AWS Lambda function) when new issues are created.

    Here's a program that sets up a new GitHub label and webhook:

    import pulumi import pulumi_github as github # Configuration variables for the GitHub repository and NLP service URL github_repository_name = 'my-repo' nlp_service_url = 'https://my-nlp-service.com/issue-labeler' # This URL would point to your NLP model endpoint # Create a new label in the GitHub repository issue_label_bug = github.IssueLabel("issue-label-bug", repository=github_repository_name, name="bug", color="ff0000") # You would create more labels depending on the categories your NLP model can classify # Create a GitHub repository webhook for the new issues repository_webhook = github.RepositoryWebhook("repository-webhook", repository=github_repository_name, configuration=github.RepositoryWebhookConfigurationArgs( url=nlp_service_url, content_type="json" ), events=["issues"], active=True) pulumi.export("webhook_id", repository_webhook.id)

    In the above program:

    1. We define a label 'bug' with a red color that can be applied to issues in our repository.
    2. We set up a webhook on the repository to trigger on new issues. When a new issue is created, the webhook sends an HTTP POST to the specified NLP service URL (nlp_service_url).

    Please note that the actual NLP processing and labeling logic will be implemented on the service that you configure the webhook to call. The webhook URL should point to an endpoint that will receive the GitHub issue payload, process the content with NLP to understand the context, and then use the GitHub API to apply the required labels to the issue.

    The next steps beyond this Pulumi program would be:

    • Develop or integrate with an existing NLP model that can analyze text and classify it into specific categories (like 'bug', 'feature request', etc.).
    • Create a service (e.g., AWS Lambda, Azure Function) that can accept GitHub webhook payloads, extract the issue content, pass it to the NLP model for classification, and then apply the appropriate labels to the issues in GitHub.
    • Ensure that the service has the necessary permissions to call the GitHub API to update issues in the repository.

    Please replace my-repo with your actual GitHub repository name and https://my-nlp-service.com/issue-labeler with the URL to your NLP service endpoint that will process the issue content. Remember to handle the GitHub webhook secret securely in your service and validate the payloads you receive for security purposes.