This content originally appeared on Bits and Pieces - Medium and was authored by Eden Ella
How to create a codebase that is simple, minimal, modular, reusable, and easily readable — for AI and humans
There are plenty of articles online that compare and rank AI coding assistants, but there’s little discussion about the codebase they interact with. How do you design a codebase that is optimized for AI coding assistants? How can you make sure that your codebase is AI-friendly?
As AI coding assistants play an increasingly significant role in the software development process, these questions should be top of mind for every developer.
What Makes a Good AI Coding Assistant?
In short, a good AI coding assistant should be able to produce efficient, contextually relevant, and accurate code suggestions.
An ideal AI coding assistant should also emphasize code readability and maintainability while providing suggestions. After all, the goal of an AI coding assistant is to help human developers write better code faster. This means that it should be able to provide suggestions that are not only correct but also efficient and easy to understand by other developers.
Other factors contribute to the overall quality and performance of an AI coding assistant, such as how well it integrates with your IDE and other development tools. These factors are important, but they are not the focus of this article. Instead, we will focus on those aspects affected by our codebase design.
How to Help Your AI Coding Assitant Provide Better Suggestions and Reuse Existing Code
Whether the language model powering our coding assistant is fine-tuned to our codebase or utilizes various retrieval-augmented generation (RAG) methods to extract relevant information from our codebase, the quality of its suggestions is highly dependent on the readability of our code.
If you read any of my previous articles, you’re aware that I’m both a fan and a team member of Bit, so you probably know where I’m going with this.
Bit enhances the readability and maintainability of your codebase, making it easier for AI coding assistants to offer precise and contextually relevant suggestions.
Additionally, by composing your software using reusable Bit componetns, you allow AI to efficiently leverage existing modules, reducing the need to write additional lines of code.
Enhance Codebase Clarity Through a Composable Design
Software built with Bit consists of reusable units of code called “Bit components.” These components encapsulate a single concern and can be of all levels of granularity, from utility functions and UI elements to entire services and applications.
This ultra-modular, composable approach, where your entire software system is a composition of Bit components, makes for an incredibly readable codebase. This is primarily due to the nature of Bit components:
- Each component encapsulates a single concern that aligns with your business goals, making it easier to understand the codebase’s objectives and intentions.
- Each component has a clear dependency graph, making it easy to understand how different parts of your codebase are related.
- Each component has documentation, API reference, and component examples, making it easier to understand how and when to use it.
- Each component is set up with a reusable development environment, clearly defining how it should be maintained, tested, and built, as well as providing guidance on its usage, including required runtime, frameworks, and libraries.
Bit ensures your AI assistant has all the information it needs to understand your codebase. The information is provided in a simple, standardized way—Bit components—that are self-contained and agnostic to your codebase’s specific structure or their “physical” location in any specific project. This makes it much easier for your AI coding assistant to extract and use this information.
For example, the screenshots below show the information contained in a Bit Component. This information includes its source code, dependencies, development tools, configurations, and additional metadata, such as which of its files are for production and which are only used for development.
Improve Code Maintainability by Leveraging Reusable Components
As mentioned earlier, good code suggestions are not only correct but also adhere to our development standards; they are efficient, and they are easy to understand.
The previous section discussed how Bit Components can help your AI coding assistant understand or use your codebase better to provide more accurate suggestions. However, this is only half the story. The other half is about reusability.
What better way to ensure your AI coding assistant provides suggestions that are efficient and consistent with the rest of your codebase than by having it use the same curated building blocks?
Using this approach, an AI coding assistant can suggest new components that are composed of existing components, ensuring consistency and maintainability across your codebase.
The fewer new lines of code your AI suggests, the less likely it is to introduce bugs or inconsistencies, and the easier it is for other developers to understand and maintain the codebase.
For a tangible example, let’s see how that works with Hope AI, Bit’s native AI coding assistant. We’ll ask it to generate a new “contact us” form. As you’ll see, it will try to compose it from your existing components like “input” and “button” rather than generating the new component entirely from scratch.
If we accept this suggestion, the generated ‘contact-us’ Bit component will have the following Bit components (for our existing codebase) as dependencies:
Additionally, since documentation and examples make our Bit components more easily discoverable and easy to use, Hope AI has also included documentation:
Simplify Prompts and Enhance AI Contributions
Developing software using components helps reduce the cognitive load on human developers by breaking down tasks into smaller, more manageable pieces. This principle also applies to AI coding assistants.
By segmenting the codebase into smaller units, we can more easily craft effective prompts and enhance the AI’s ability to contribute.
Learn More
- Bit. Composable software platform.
- Hope AI: A Code Assistant for Composable Software
- JavaScript Monorepos: Exploring Decentralized Alternatives
Optimizing Your Codebase for AI Coding Assistants was originally published in Bits and Pieces on Medium, where people are continuing the conversation by highlighting and responding to this story.
This content originally appeared on Bits and Pieces - Medium and was authored by Eden Ella
Eden Ella | Sciencx (2024-08-05T10:59:34+00:00) Optimizing Your Codebase for AI Coding Assistants. Retrieved from https://www.scien.cx/2024/08/05/optimizing-your-codebase-for-ai-coding-assistants/
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