AWS Summit Sydney 2024: Build self-healing code with generative AI on AWS

Summary

The script envisions a future where developers collaborate with AI coding companions, leveraging generative AI for code suggestions, completions, and understanding complex codebases. It demonstrates a self-healing code system that autonomously fixes bugs in real-time by analyzing logs, identifying errors, and proposing solutions. The system, showcased through a demo, highlights the potential of AI to enhance developer capabilities, streamline workflows, and improve code quality and operational efficiency.

Takeaways

• 🧠 The script introduces the concept of a generative AI coding companion that can interact with developers to plan and write code, suggesting libraries and modifying existing code bases.
• 🔧 It discusses the potential of AI in the developer experience, emphasizing that generative AI is not here to replace developers but to enhance their capabilities and streamline workflows.
• 💡 The presentation showcases Amazon Code Whisperer for real-time code suggestions and completions, Amazon CodeGuru for understanding and explaining code, and Amazon CodeTransformer for language version upgrades.
• 🛠️ The idea of self-healing code is introduced, which extends the developer's reactive approach to include metrics, logs, and traces to improve code health and performance.
• 🔑 The script outlines the components necessary for self-healing code: application logs, a source code system like Git, and a generative AI agent capable of modifying code.
• 📈 The demonstration includes a practical example of an 'orders API' that receives product order requests, illustrating a simple HTTP API integrated with AWS services.
• 🐞 The scenario of an unexpected user error causing a system breakdown is presented, highlighting the need for a reactive approach to development.
• 🔍 The self-healing code system is shown to autonomously detect errors, analyze their causes, and create bug fixes and pull requests for review.
• 📝 The detailed process of the AI agent's operation is explained, from cloning the source code to pushing changes back to the source code repository after modifications.
• 🔄 The script concludes with a live demonstration of the self-healing code system creating and merging a pull request to fix an error in the orders API.
• 🚀 The final takeaway emphasizes the current feasibility of self-healing code and the next-gen developer experience, encouraging developers to experiment with generative AI in their workflows.

Q & A

What is the main theme of the session described in the script?

The main theme of the session is the introduction and demonstration of a generative AI coding companion and the concept of self-healing code in the developer experience.

What is a generative AI coding companion?

A generative AI coding companion is an AI tool that assists developers by providing code suggestions, completions, and explanations to enhance their coding capabilities and streamline workflows.

How does the AI coding companion help in understanding a new codebase?

The AI coding companion can be used to ask pointed questions about specific functions or to explain the overall codebase in human-understandable terms, aiding in comprehension for developers new to a project.

What is Amazon Code Whisperer and how is it used?

Amazon Code Whisperer is a service that provides real-time code suggestions and completions within an IDE, helping developers write functions, unit tests, and infrastructure as code templates more efficiently.

What is the purpose of the self-healing code system demonstrated in the script?

The self-healing code system is designed to autonomously detect, analyze, and fix bugs in the codebase, improving operational capabilities and reducing the developer's burden in maintaining code health and performance.

How does the self-healing code system integrate with the development process?

The self-healing code system integrates by monitoring application logs, identifying errors, analyzing their causes, and creating bug fixes and pull requests for review and integration into the source code.

What is the role of the 'AI agent' in the self-healing code system?

The AI agent in the self-healing code system is responsible for mapping application logs to the source code, generating improved source code, and pushing changes back into the source code repositories.

Can you explain the process of how the self-healing code system reacts to an error?

When an error occurs, the system captures the error logs, uses a subscription filter to detect specific log entry matches, invokes a Lambda function to create a hash and store it in a Dynamo DB table. Dynamo DB streams detect changes and invoke another Lambda function which interacts with the AI agent to generate a bug fix and a pull request.

What is the significance of using an MD5 hash in the self-healing code system?

The MD5 hash is used to uniquely identify each error message or stack trace, ensuring that each unique error is addressed only once and reducing unnecessary processing.

How does the AI agent interact with the source code repository?

The AI agent clones the source code into its local file system, interacts with the large language model to generate code modifications, writes changes to the local file system, and uses git commands to push the changes and create a pull request.

What are some of the challenges and considerations for implementing self-healing code in real-world scenarios?

Challenges include handling more complex bugs that may traverse multiple files or codebases, managing token sizes for large language models to minimize costs and accommodate large codebases, and ensuring that the AI understands the context of the code to provide appropriate fixes.