What Is an AI Code Assistant?
An AI code assistant is a tool that utilizes artificial intelligence to assist software developers in their coding tasks. Just as a human assistant would do, an AI code assistant provides suggestions, fixes errors, and even writes code snippets, all with the aim of making the coding experience smoother and more efficient.
AI code assistants provide much more than the automation features in traditional code editors or Integrated Development Environments (IDEs). They are intelligent tools that understand the context of the code and provide assistance accordingly. They can help developers write cleaner, more efficient code, produce high quality code faster, and reduce the time spent on debugging and refactoring. Overall, AI code assistants are making a major contribution to development productivity.
This is part of a series of articles about AI tools for developers.
In this article:
- How AI Code Assistants Work
- Natural Language Processing and Generative AI in Code Assistants
- The Process of Training AI Code Assistants
- How AI Code Assistants Interpret and Generate Suggestions
- Support for Multiple Programming Languages
- Key Features of AI Code Assistants
- Autocompletion and Code Prediction
- Code Refactoring Suggestions
- Error Detection and Correction
- Documentation and Explanation of Code
- Notable AI Code Assistant Tools
- GitHub Copilot
- Amazon CodeWhisperer
- Replit Ghostwriter
- Snyk Code
How AI Code Assistants Work
Natural Language Processing and Generative AI in Code Assistants
Traditionally, AI code assistants relied on Natural Language Processing (NLP) techniques. These techniques allow the assistant to process large amounts of code data, understand coding patterns and structures, and generate suggestions or insights that can be useful for developers in their current context.
With the recent advances in generative AI, modern code assistants incorporate large language models (LLMs) like GPT-3.5 and GPT-4 to provide more value and perform a wider variety of tasks. These LLMs can generate human-like text and code based on the context given to them. When applied to code, they can create syntactically correct, contextually appropriate code segments. They can also interpret prompts provided in natural language, adding another layer of convenience and utility for developers.
Learn more in our detailed guide to AI code generation (coming soon)
The Process of Training AI Code Assistants
Training an AI code assistant is an extensive process that involves exposing the AI to previously written code. Different code assistant products are trained on different datasets: some are trained on large, publicly available datasets like public GitHub repositories. Others can be trained on more specific datasets like the code created by a specific organization.
In general, the broader the dataset, the more capabilities the AI code assistant can have, but this also means its outputs can be less predictable and might introduce the risk of low quality or insecure code.
In the case of AI code assistants that leverage LLMs, training also involves a two-step process, pre-training and fine-tuning. Pre-training involves training the model on a vast dataset, enabling it to learn the structure of natural language and code. In fine-tuning, the model is trained on a narrower, more specialized dataset to refine its performance.
How AI Code Assistants Interpret and Generate Suggestions
Most AI code assistants use NLP techniques to read your code, understand the context, and generate suggestions. They do this by analyzing your code in real-time, identifying patterns and structures, and comparing these to the code data they were trained on. Based on this analysis, they provide suggestions that are most similar to the patterns and structures identified in your code.
LLM-based AI code assistants take this a step further. They utilize the context of the code along with the natural language prompts given by the developer to generate relevant code suggestions. For instance, if a developer starts writing a function to calculate the factorial of a number, the AI assistant could suggest the rest of the function based on the initial prompt and its understanding of the coding context.
Support for Multiple Programming Languages
Most AI code assistants are designed to be versatile and support all popular programming languages. However, for languages that are relatively new, domain specific, or proprietary, there might be limited available data, which can limit the capabilities or usefulness of an AI code assistant.
Key Features of AI Code Assistants
Autocompletion and Code Prediction
One of the most basic, yet powerful features of an AI code assistant is autocompletion. This feature allows the assistant to predict what you’re going to type next, based on the context of your code, and provide suggestions accordingly. This not only saves you time but also ensures that you write clean, error-free code.
AI code assistants based on LLMs can not only complete a few words or the next line of code, but can actually provide entire blocks of code or complete functions based on a partial input.
Code Refactoring Suggestions
AI code assistants can also provide code refactoring suggestions, helping you optimize your code for better performance. They do this by analyzing your code and identifying areas that can be improved or optimized. They then provide suggestions on how to refactor these areas.
AI code assistants based on LLMs can attempt to refactor the code and even explain, in natural language, why the suggested changes are more optimal. This can help developers learn, and also makes it easier to judge if the suggestions are appropriate for the needs of the project.
Error Detection and Correction
One of the more advanced features of an AI code assistant is error detection and correction. The assistant can identify potential errors in your code, highlight them, and provide suggestions on how to fix them. This feature can save time currently spent debugging and troubleshooting, allowing you to focus on developing your application.
AI code assistants based on LLM take this one step further, and can explain in natural language what is wrong with the code and why a certain fix is needed.
Documentation and Explanation of Code
AI code assistants can also assist in documenting and explaining your code. They can generate comments for your code, explaining what each section does, and how it fits into the overall application. This not only helps you understand your own code better but also makes it easier for others to understand your code.
AI code assistants based on LLM can write complete pieces of documentation, such as tutorials or functional descriptions, based on code and natural language prompts.
Learn more in our detailed guide to code explainer (coming soon)
Notable AI Code Assistant Tools
Now that we understand what an AI code assistant is and how it can assist us, let’s look at some of the best AI code assistant tools available today.
GitHub Copilot, a project powered by OpenAI’s Codex technology, works right in your code editor, suggesting whole lines or blocks of code as you type. This AI assistant has been trained on a range of public code repositories, enabling it to understand different coding languages and contexts.
What sets GitHub Copilot apart from other AI code assistants is its context understanding. It takes into account both the code and comments, providing relevant code suggestions. Furthermore, it adjusts to the way you write code, making personalized recommendations.
Amazon CodeWhisperer, also based on large language models, is specifically designed to help developers build, test, and troubleshoot their code more efficiently. Amazon CodeWhisperer is a part of AWS Developer Tools and is compatible with AWS CodeStar projects, making it an excellent choice for developers already using Amazon’s development ecosystem.
One of the key features of Amazon CodeWhisperer is its code review capability. It can identify potential issues in your code and suggest corrective measures, significantly reducing the time spent on debugging. Its ability to identify hard-to-find bugs is commendable, especially in large and complex codebases.
Tabnine is an AI code assistant that utilizes GPT-3 to provide relevant and accurate code completions. It integrates with various code editors, making it a versatile tool that can support developer productivity.
Tabnine’s primary feature is its large language model, trained on over 2 TB of high quality, secure code suitable for use in enterprises. The model enables Tabnine to understand coding context and provide relevant suggestions. It supports multiple programming languages and is capable of learning from the user’s coding style to offer personalized recommendations.
Beyond code completion, Tabnine also offers a feature called Tabnine Hub. This tool provides insights into the AI’s performance, allowing developers to understand how it’s learning and improving over time. Tabnine Hub also offers the ability to adjust settings and preferences to enhance the AI’s effectiveness further.
Replit Ghostwriter is another AI code assistant that has gained significant attention. It operates on a different principle, focusing on providing developers with code samples instead of code completions. Ghostwriter supports multiple programming languages, and integrates with the Replit online IDE.
Ghostwriter’s strength lies in its ability to generate code snippets based on a brief description. Developers can describe the functionality they need, and Ghostwriter will generate a code snippet to achieve that functionality. This feature saves time and effort, especially when developing complex functions.
Snyk Code is an AI code assistant that prioritizes security. It offers real-time feedback on potential security vulnerabilities, helping developers create secure applications.
Snyk Code uses a combination of semantic and syntactic analysis to identify problematic code. It can detect a wide range of vulnerabilities, including SQL injection, cross-site scripting, and insecure data handling. By providing immediate feedback, Snyk Code allows developers to address issues as they arise, reducing the risk of security breaches.
Snyk Code supports multiple programming languages and integrates with popular code editors and DevOps tools. It also offers a dashboard that provides an overview of the application’s security status, making it easier to manage and mitigate risks.
Swimm: Auto-Generating Documentation with AI
Swimm offers a unique approach to code assistance by focusing on knowledge sharing and documentation. While many AI code assistants aim to speed up coding through autocomplete and error detection, Swimm takes it one step further by auto-generating up-to-date, code-coupled documentation.
How does it fit into the AI code assistant landscape? Swimm employs generative AI technology to create contextually relevant documentation that helps teams understand not just what a piece of code does, but why it exists and how to work with it. By integrating directly into the IDE, it ensures that this valuable information is easily discoverable exactly where developers work.
- Code-Coupled Documentation: Swimm maintains your documentation as your code evolves. You won’t find yourself with outdated docs that lead to confusion and slow development speed.
- Easy to Discover: Integrated into your IDE, Swimm ensures that the documentation is readily available, making onboarding and code reviews more effective.
- Generative AI Assistance: Swimm utilizes AI to assist in generating documentation, taking the heavy lifting off of your team members and making it easier to maintain high-quality documentation.
- Playlists: Curate learning modules for your team, organizing your documentation into a sequential learning experience. This is invaluable for onboarding new team members or when tackling a complex new project.
- Senior Developer Knowledge Sharing: The tool is particularly useful for very senior developers who solve complex problems and can use Swimm to easily share their insights and solutions with the rest of the team.
- Efficient Teams: Directors and VPs aiming for an efficient, agile team will find Swimm’s features directly aligned with their objectives. The product significantly enhances productivity by reducing the time spent in understanding code, making the team more capable of quick iterations.
- Developer Experience Enhancement: Developer Experience teams will appreciate Swimm’s focus on making life easier for developers, aligning closely with the goal of improving efficiency across the organization.
- Security: Swimm offers a secure environment, ensuring that your sensitive documentation and code insights are safe and accessible only to those with the right permissions.
While other AI code assistants may offer advanced code completion and debugging support, Swimm addresses an often overlooked but crucial aspect of software development: effective knowledge sharing. By doing so, it not only improves the code quality but also significantly enhances team productivity and makes the onboarding process a breeze. If you’re looking to bolster your development process from all angles, integrating Swimm alongside other AI code assistants can offer a more comprehensive solution.