In the software development world, code review and approval are vital steps for ensuring software quality, functionality, and security. However, managers often face challenges with these processes, including lack of technical expertise, time constraints, high volumes of change requests, manual effort requirements, and necessary documentation. Generative artificial intelligence (AI) combined with AWS deployment tools and services can streamline these tasks.
This article presents a solution for an integrated end-to-end deployment workflow that includes automated change analysis and approval workflow functionality. This process uses Amazon Bedrock, a managed service providing foundation models from leading AI startups and Amazon, available via API.
Using Amazon Bedrock’s serverless experience, managers can get started swiftly. They can customize models using their data and integrate and deploy them into their applications using AWS tools without dealing with infrastructure management. The solution involves several steps:
1. A developer pushes new code changes to their code repository (such as AWS CodeCommit), which begins an AWS CodePipeline deployment.
2. The code is built, scans are performed, and unit tests are conducted using preferred tools.
3. AWS CodeBuild retrieves the repository, comparing the current and previous commit versions and creating a breakdown of changes made.
4. CodeBuild saves the output to an Amazon DynamoDB table along with various other information.
5. Amazon DynamoDB Streams captures the data modifications to the table.
6. The process triggers an AWS Lambda function.
7. The function invokes an Amazon Bedrock model to analyze the code changes and return a summary.
8. The model’s output is saved back to DynamoDB.
9. The manager receives an email via Amazon Simple Email Service (SES) summarizing the code changes and requesting approval for deployment.
10. The manager reviews the email and submits their approval decision and comments via the CodePipeline console.
11. Amazon EventBridge captures the approval decision and triggers another Lambda function to save the decision to DynamoDB.
12. If approved, the CodePipeline deploys the application code.
It’s important to experiment with different foundation models and prompts to customize the experience and outcomes. Managers should remember that analyses are tools to assist and expedite code review, but they shouldn’t replace human judgment. Managers should also stay mindful of potential hallucinations when working with generative AI.
The proposed solution integrates Amazon Bedrock into the deployment workflow, offering managers a beneficial tool to navigate code review challenges with efficiency. Readers are encouraged to try out the implementation and provide feedback.
