Bug Triaging Automation Using AWS Lambda and ML Models

Question

Pulumi · Accepted Answer

To set up a bug triaging automation system using AWS Lambda and ML models, we will need to create several components:

1. **AWS Lambda** - A serverless compute service that runs your code in response to events and automatically manages the compute resources.
2. **ML Models** - For the machine learning part, we would typically use Amazon SageMaker, a fully managed machine learning service. You could create, train, and deploy machine learning models at scale. However, since this is a bug triaging system, we'll assume that an ML model has been created and trained elsewhere, and we simply need to integrate it.

3. **Amazon API Gateway** - Used to create, publish, maintain, monitor, and secure APIs at any scale. This acts as the "front door" for our Lambda function.

4. **Amazon S3** (Simple Storage Service) - Optionally, if the bugs are reported in files (like logs or screenshots), we might need S3 to store and retrieve these files.

5. **Amazon DynamoDB** - A NoSQL database service for all applications that need consistent, single-digit millisecond latency at any scale. This could be used to store and query the bug reports.

6. **Amazon EventBridge or SNS** - These services could be used to handle notifications or triggers based on certain criteria, such as a new bug report being filed or an anomaly detected by the ML model.

Since it's a complex system, I'll assume you have an ML model already trained to analyze bug reports and perhaps classify them or predict their priority/severity.

Here's an outline of what the automated bug triaging system might look like in Python using Pulumi:

1. An API Gateway endpoint where users can submit bugs.
2. A Lambda function is invoked by the API Gateway that processes the bug report. It could store the report in a database and send the content to the ML model for triaging.
3. Depending on the output of the ML model, subsequent actions could occur, such as notifying a developer, updating a ticketing system, or providing an automated response.

Let's write the code for setting up the AWS Lambda and the API Gateway. For the ML model, we'll assume it's a SageMaker endpoint that our Lambda function can invoke.

```python
import pulumi
import pulumi_aws as aws

# First, we'll create a new IAM role that our Lambda function will assume
lambda_role = aws.iam.Role('lambda-role',
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            }
        }]
    }""")

# Attach the AWSLambdaBasicExecutionRole policy to the role
aws.iam.RolePolicyAttachment('lambda-role-attachment',
    role=lambda_role.name,
    policy_arn='arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole')

# Next, create the Lambda function
bug_triage_lambda = aws.lambda_.Function('bug-triage-lambda',
    code=pulumi.FileArchive('./app.zip'),  # Replace with your actual Lambda deployment package
    role=lambda_role.arn,
    handler='app.handler',  # Replace with your actual handler
    runtime='python3.8')

# Now, we create an API Gateway to trigger the Lambda function
api = aws.apigatewayv2.Api('bug-triage-api',
    protocol_type='HTTP')

# Create a default integration; this is where we connect the API with the Lambda
integration = aws.apigatewayv2.Integration('bug-triage-integration',
    api_id=api.id,
    integration_type='AWS_PROXY',
    integration_uri=bug_triage_lambda.invoke_arn,
    payload_format_version='2.0')

# Define a route for the POST method on our API
route = aws.apigatewayv2.Route('bug-triage-route',
    api_id=api.id,
    route_key='POST /bugs',
    target=pulumi.Output.concat('integrations/', integration.id))

# Finally, we deploy the API so that it is live
deployment = aws.apigatewayv2.Deployment('bug-triage-deployment',
    api_id=api.id)

# And bind the deployment to a stage for accessible invoking
stage = aws.apigatewayv2.Stage('bug-triage-stage',
    api_id=api.id,
    deployment_id=deployment.id,
    auto_deploy=True)

# We output the invoke URL of the API so we know where to send bug reports
pulumi.export('invoke_url', api.api_endpoint)
```

This code sets up a simple HTTP endpoint, which you could POST bug reports to. The `app.handler` function in Lambda is where you would integrate with your ML model to triage bugs.

Replace the placeholder `app.zip` with your actual Lambda function deployment package, and `app.handler` with your function handler. Similarly, the ML model integration and other aspects like database storage, notifications, or file storage would need to be built into the Lambda function's logic or represented with additional Pulumi resources.