AI Code Generation with Visual Copilot

AI Engineer by SAHAJ GODHANI Published 23 August 2024

Remember the good old days of coding? We used to bend over, tapping on the keyboard line by line, counting on our memory, documentation, and maybe the simple autocomplete abilities of IDEs. It was a heavy process, full of errors and long debugging sessions.

Fast forward to today and we are living through one of the most profound, seismic shifts to how software is developed: AI code generation fundamentally changes how we build software. But what is AI code generation, and how does it work?

What is AI code generation?

AI code generation is the process by which software tools, driven by artificial intelligence, create code automatically for different inputs, from natural language descriptions to visual designs.

These tools are powered, to a large degree, by Large Language Models that have been trained off of huge pools of existing code — learning from millions of lines penned by human developers in a huge number of languages and frameworks.

How does AI code generation work?

AI code generation primarily relies on LLMs. These are deep neural networks — concretely, transformer networks — trained on massive amounts of text data. These models can understand and generate human-like text, including code.

LLMs bundle several capabilities together:

1. Machine Learning: It learns patterns from vast amounts of code to predict and create new code.
2. Natural Language Understanding: They can process and understand human language inputs.
3. Code Generation: They are syntactically correct, contextually appropriate, based on the input, and from learned patterns.

The AI code generation process

The following is a simplified view of the common AI code generation process:

1. Input: It consists of what the developer provides, either through a prompt or a natural language description of the functionality to implement, or as partially written code.
2. Context Understanding: This AI will study the input and any ambient code to understand the requirements of the desired code.
3. Generation: AI will generate the closest matching code for the training and context provided; often, this involves predicting the most likely next tokens in the code sequence, either character or words.
4. Refinement: The majority of AI code generation tools have the ability for iterative refinement, in which users are free to improve the generated code by giving feedback or more context.
5. Integration: what sort of thing would it be like if it was possible to input generated codes straight into a developer’s workflow? This usually happens through IDE plugins or web interfaces.

The Impact of AI Code Generation on Developer Productivity

AI-powered code generation tools are changing how developers work. The advantages mentioned make them highly productive and effective at their work, thereby making the whole coding process much easier. Among these advantages are:

1. Faster code writing: Artificially intelligent code generators can generate code at a very high speed as compared to manual typing. This aids the developer in developing code faster and gives time for solving higher-level problems.
2. Reduced boilerplate code: Much of the coding work is repetitive and related to writing boilerplate code. AI can generate this sort of code quickly, thus saving a lot of time for the developer and reducing errors.
3. Code completion and suggestions: AI-powered code completion tools offer real-time code suggestions that assist the developer in writing efficient error-free code.
4. Assistance on complex tasks: AI can help in specific programming tasks that may be difficult or time-consuming for a developer to perform, like optimization of algorithms or implementation of design patterns.
5. High-quality code: Based on machine learning from the huge repositories of high-quality code, AI will teach tools how to produce better code that follows best practices and coding standards.
6. Easier debugging: AI in some of the tools can assist in debugging the code through suggestions of possible fixes or even highlighting common errors.
7. Multi-language support: An AI source code generator can often write code in more than one programming language.

Popular AI code generators

AI code generation tools enhance productivity, reduce errors, make learning easier, speed up problem-solving, and provide uniformity of coding style; they have changed the way we work drastically.

Claude
Claude is an AI assistant developed by Anthropic for understanding context and producing coherent relevant code. its functionality in generating code from natural language descriptions sets it apart.

ChatGPT

While OpenAI’s ChatGPT has been riveting, its capability to generate codes doesn’t turn out to be an exception either. Although this is more of a general-purpose AI, it has acquired remarkable capabilities to generate code snippets, which make it a great AI code generator.

GitHub Copilot

It’s an awesome AI code completion tool that’s been created in collaboration with OpenAI. GitHub Copilot integrates well with VS Code and other IDEs to provide real-time code suggestions based on learnings from a vast number of GitHub repositories. With GitHub Copilot Chat, you are able to generate code, unit test cases, and much more.

Challenges and limitations of AI code generation

While AI-based code generation comes in very handy, it is not without challenges and limitations.

Security Considerations: If trained with public repositories, AI models can inadvertently provide code containing security vulnerabilities or copyrighted material that may raise potential legal and data security concerns.
Code Quality: AI-generated code may violate best practices or specific coding standards in many cases. That means, it can be functional but very probably inefficient or hard to maintain.
Contextual Understanding: This is where most AI coding assistants fall short — lack of context understanding of a project at large. This can result in suggestions quite removed from the overall architecture or business logic for any application.
Integration problems: Sometimes, the generic AI coding tools introduce code that is not very well-integrated into existing projects or even other areas in the codebase.
Fidelity Issues: AI-powered tools can create rather imaginative deviations from the expressed intention in the text descriptions or low-fidelity inputs, especially with visual elements or precise styling.

Visual Copilot: Complementing design to code generation

Visual Copilot is an AI-powered Figma-to-code toolchain and has constituted one that supplements the existing traditional AI code generation tools, particularly in the realm of front-end development. Different from text-based AI code generators, Visual Copilot begins with visual designs. This process solves fidelity in translation problems from ideas or descriptions into code.

Visual Copilot uses sophisticated AI models in combination with an open-source compiler, Mitosis, to generate structured code hierarchies from flat designs and then refine them by machine learning models for specific framework and styling preferences.

Convert Figma designs to clean code with Visual Copilot

Getting started with Visual Copilot is straightforward:

• Open the Visual Copilot Figma plugin.
• Select a layer or design in your Figma file.
• Click the Generate code button.
• Copy the generated code into your project.
• Customize the code to support animations, custom fonts, SVG, etc.

The Future of AI in Software Development

Shortly, with AI technology increasingly developing, much more powerful tools will spring up to improve the productivity of developers. Such tools will play an integral role in software development and collaborate with human developers to boost productivity, improve the quality of code, and bring innovation at a faster rate.

Some probable future developments may be:

1. More accurate context understanding: AI models, in understanding more about projects, will very quickly lead to relevant and more integrable code suggestions.
2. More detailed code explanation: Sometimes, AI tools are capable of providing code explanations, thus allowing the developer to understand complex algorithms or unfamiliar patterns.
3. Advanced debugging and testing: AI could automatically generate test cases and provide far more efficiency in fixing bugs.
4. Natural language programming: In the future, there may be more conversational-like interfaces for code generation as NLP capabilities improve, which would open up programming even to those users who do not have a technical background.
5. Architecture design: Future AI tools could help design the entire system architecture from given high-level requirements.
6. Multimodal AI: More Visual Copilot-like systems that work with text and visual input would become commonplace, offering a one-stop solution for the developer.

Such power can a new dimension of efficiency and innovation by having AI assistants complemented by human creativity. Knowing the code generation capabilities of AI will further open your mind toward newer ways of writing better code at a greater speed, whether you are an experienced developer or just a newcomer to coding. These technologies, as they evolve further, will undoubtedly make a greater impact on the future of creating software.

If you enjoyed this post and would like to read more, you can subscribe or follow here — https://sahajgodhani777.medium.com/ to get an email whenever I publish a story.

Work Details

Visit our Portfolio