How to elevate software development with AI-assisted coding

How to Elevate Software Development with AI-Assisted Coding

Hello, and thank you for joining this technical session at Google I/O 2024. My name is Way, and I'm a Developer Advocate at Google. Today, my colleague Shista and I will be discussing the exciting topic of AI-assisted coding. Generative AI is transforming many industries today, and the software industry is no exception. Many practitioners are already benefiting from adopting AI.

AI in the Software Development Life Cycle

In a typical software development life cycle, AI can assist in various phases:

• Planning: AI can help analyze user requirements and write product requirement documents (PRDs).
• Designing: During architecture design, AI can help brainstorm and propose innovative solutions.
• Coding: AI can speed up coding, automate monotonous tasks, and offer alternative implementations.
• Release: Once the application is released, AI can monitor usage and analyze user feedback.

AI in the Coding Phase

AI automates many tedious tasks, allowing developers to focus on creative aspects of their workflow. It can act as a coding companion to review or critique code, thereby improving code quality. Additionally, AI can introduce creativity by suggesting alternative or better implementations. Adopting AI in coding leads to higher productivity and greater developer satisfaction. A recent Stack Overflow survey revealed that 70% of developers are already using or planning to use AI in their workflow.

AI Use Cases in Coding

AI coding assistants cover a wide range of use cases such as code completion, code generation, code chat, code explanation, and error debugging. Many Google products incorporate these capabilities.

Example: Google Colab

Google Colab is a hosted Jupyter notebook service. It offers AI-generated suggestions for code completion based on the current coding context. For instance, when creating a function to compute a circle's area, AI can guess the implementation and suggest it as you type, allowing you to finish the function with minimal effort.

Example: Project IDX

Project IDX is a web-based workspace enabling full-stack, multi-platform app development. Here, AI can generate entire functions based on natural language prompts and even write unit tests or documentation upon request.

Example: Gemini Code Assist

Gemini Code Assist is available in multiple IDEs like Visual Studio Code, JetBrains, Cloud Workstations, and more. It supports over 20 programming languages and can explain code, optimize it, and debug errors.

Example: Chrome Dev Tools

In Chrome Dev Tools, AI can be used to explain errors and assist in debugging. This can pinpoint issues and provide guidance for fixing them.

Additional Google AI Products

SGE (Search Generative Experience)

SGE enhances Google Search with generative AI, allowing developers to get answers with code snippets, explanations, and examples directly within search results.

Gemini Chat

Gemini Chat offers answers to code-related questions, helps in code refactoring, and explains code with the ability to execute and edit it.

AI on Developers.Google.Com

Google's main developer documentation site now includes AI capabilities like AI search, documentation chat, and AI code explanations, combining generative AI with expert-authored documentation.

Code Gemma Models

For those wanting to build applications on Google's code models, we have released Code Gemma. Code Gemma is a family of open code models trained on up to 500 billion tokens of code. These models achieve state-of-the-art performance in code completion and generation.

Techniques for Enhanced Performance

• Fill in the Middle (FIM): Uses the left and right context of the code for better completion.
• Retrieval Augmented Generation (RAG): Enhances LLM answers using a vector database to store and retrieve code snippets.

Development Approach

Google ensures high-quality, permissively licensed data, domain-specific pre-training, and instruction tuning. Evaluations involve human and automatic assessments, followed by integration with serving configurations, monitoring, safety guardrails, and UX.

Safety and Future Directions

Safety is paramount; data is properly scrubbed, debiased, and anonymized. Safety filters and citations ensure responsible usage. Looking ahead, Google is exploring code agents, starting with a data science agent capable of creating detailed notebooks and complex reasoning.

We aim to make coding innovations available early to users through a developer playground to test and refine new features.

In summary, Google is providing a wide range of code AI innovations across diverse surfaces, supporting developers of all levels. Generative AI is poised to become more mature and ubiquitous, fostering collaborative and creative problem-solving in software development.

Thank you for joining this session at Google I/O 2024. Happy coding with AI!

Keywords

• AI-assisted coding
• Generative AI
• Google Colab
• Project IDX
• Gemini Code Assist
• Code Gemma
• Fill in the Middle (FIM)
• Retrieval Augmented Generation (RAG)
• Safety guardrails
• Code agents

FAQ

Q: What phases of the software development lifecycle can AI assist with? A: AI can assist with planning, designing, coding, and releasing phases. It helps analyze requirements, brainstorm architecture solutions, speed up coding, and monitor usage and feedback.

Q: What are some AI tools offered by Google for coding? A: Google offers tools including Google Colab, Project IDX, Gemini Code Assist, and Chrome Dev Tools. These tools provide features like code completion, generation, explanation, and error debugging.

Q: How does Google ensure the safety of its AI models? A: Google uses high-quality, permissively licensed data, scrubs and debiases data, applies safety filters, and shows citations where required to ensure responsible usage.

Q: What is Code Gemma, and how can it be used? A: Code Gemma is an open family of code models targeting coding tasks, achieving state-of-the-art performance. Techniques like FIM and RAG enhance its performance for tasks like code completion and chat.

Q: What is the future direction of AI in coding according to Google? A: Google envisions generative AI becoming more mature and ubiquitous, with future innovations like code agents facilitating creative collaboration and problem-solving.