Enforcing Code Quality: Integrating AWS CodePipeline with AWS Lambda for Custom Validation

Continuous integration and continuous delivery (CI/CD) pipelines automate application deployments. However, additional validation checks may be required before deployment. This article explores integrating AWS Lambda functions with AWS CodePipeline to perform custom validations on your code. You'll learn how to create a Lambda function for validation logic and configure CodePipeline to leverage it as a gatekeeper before deployments.

1. Building the Gatekeeper: A Lambda Function for Custom Validation

• Navigate to the AWS Lambda service console and click "Create function."
• Choose "Author from scratch" as the creation method.
• Provide a descriptive name for your function (e.g., "code-validation-checks").
• Select ".NET 6" (or your preferred runtime) as the runtime environment.
• Click "Create function" to initialize the function.

Coding the Lambda Function:

Here's a basic example of a .NET 6 Lambda function that performs basic code syntax validation (replace with your specific validation logic):

C#

using System.IO;
using System.Text.RegularExpressions;

public class Function 
{
    public async Task<string> FunctionHandler(string input, ILambdaContext context)
    {
        // Replace with your actual source code retrieval logic (e.g., from S3)
        var sourceCode = "your_source_code.zip";

        try
        {
            // Extract and validate code syntax (replace with your specific validation logic)
            var zipFile = ZipFile.OpenRead(sourceCode);
            foreach (var entry in zipFile.Entries)
            {
                if (entry.Name.EndsWith(".cs"))
                {
                    var content = await entry.OpenReadAsync().ReadAllTextAsync();
                    if (!Regex.IsMatch(content, @"public class.*"))
                    {
                        throw new Exception("Invalid code syntax detected");
                    }
                }
            }
            return "Code validation successful!";
        }
        catch (Exception ex)
        {
            return $"Code validation failed: {ex.Message}";
        }
    }
}

 

Explanation:

• This function simulates basic code syntax validation by checking for the presence of a public class declaration within C# source files.
• You'll need to replace this logic with your specific validation requirements, which could involve security checks, code formatting, or other custom criteria.

Configuring Permissions:

Ensure the IAM role associated with the Lambda function has the necessary permissions to access the source code for validation (e.g., S3 bucket permissions if retrieving code from S3).

2. Integrating the Pipeline: Adding the Lambda Validation Action

• Navigate to the AWS CodePipeline console and select the pipeline you want to modify.

Introducing the Validation Stage:

• Locate a suitable stage within your pipeline where you want to perform the validation (e.g., after the source stage but before the build stage).

Adding the Lambda Action:

• Click the "Actions" tab within the chosen stage.
• Click "Add action" and choose "AWS Lambda" as the action type.
• Provide a descriptive name for the action (e.g., "Code-Validation").
• Choose the Lambda function you created earlier (e.g., "code-validation-checks").

3. Enforcing Validation: Stopping the Pipeline on Failure

• Crucially, under the "Lambda function invocation" section, navigate to the "Execution result handling" tab.
• Choose "Fail pipeline if execution fails" to ensure the pipeline execution terminates if the Lambda function returns an error.

This configuration guarantees that the pipeline only progresses to the deployment stages if the custom validation checks within the Lambda function succeed.

4. The Secure Pipeline: Enforcing Quality with Custom Validation

With the Lambda function and CodePipeline configuration in place, the following workflow emerges:

1. Code changes trigger the CodePipeline execution.
2. The pipeline reaches the stage containing the Lambda validation action.
3. CodePipeline invokes the Lambda function, passing the source code for validation.
4. The Lambda function executes your custom validation checks.
   • If the validation is successful, the function returns a success message, and the pipeline continues execution.
   • If the validation fails (e.g., due to syntax errors or failing custom checks), the Lambda function returns an error message, and CodePipeline execution halts, preventing deployment of potentially faulty code.

This approach integrates custom validation logic into your CI/CD pipeline, enhancing code quality and preventing the deployment of code that doesn't meet your defined criteria.