Developers were quick to try out GitHub Copilot when it launched - but it isn't the only choice if you want AI-powered help writing and debugging code. Here's a line up of five code assistants.

What Are AI Code Assistant Tools? 

AI code assistants are AI-powered tools designed to assist developers in writing code more efficiently. They leverage machine learning algorithms to understand your coding style, anticipate your needs, and provide intelligent suggestions. Modern AI coding assistants are based on large language models (LLMs), and are able to generate entire code blocks, functions, or modules, based on natural language prompts.

AI code assistants are not just about code completion and auto-suggestions. They offer many features that make coding more efficient, accurate, and enjoyable. Let's take a closer look at some of these features next.

Features of AI Code Assistants 

Code Completion and Auto-Suggestions

One of the most prominent features of AI code assistants is code completion and auto-suggestions. While typing, these tools provide real-time suggestions for the next piece of code, saving you from the hassle of remembering complex syntax or function names. This feature is especially helpful when you are working with a new language or framework and are not fully accustomed to its syntax.

The auto-suggestions provided by AI code assistants are not arbitrary. They are based on the context of your code, your coding style, and the best coding practices for the language you are using. This makes the suggestions highly relevant and ensures that they align with your code's overall structure and logic.

Debugging and Error Correction

Mistakes are part and parcel of coding. Even the most experienced developers make errors, and debugging can be a time-consuming process. AI code assistants come equipped with intelligent debugging and error correction features that can identify potential errors as you write code. They not only point out the errors but also suggest corrections, making the debugging process quicker and smoother.

AI code assistants are capable of identifying a wide range of errors, including syntax errors, logical errors, and even some types of runtime errors. They are also adept at detecting potential bugs that can lead to problems later on. This proactive error detection and correction can significantly reduce the time spent on debugging and allow you to focus more on the core logic of your code.

Code Refactoring and Optimization

Code refactoring and optimization is another area where AI code assistants shine. They can suggest ways to refactor your code to make it more efficient, readable, and maintainable. Whether it's suggesting a more efficient algorithm, a better way to structure your code, or a simpler way to implement a feature, AI code assistants can help you write cleaner and more efficient code.

Furthermore, these tools can analyze your code to identify potential performance bottlenecks and suggest optimizations. Whether it's improving the performance of a slow-running loop, reducing the memory footprint of your program, or optimizing your code for parallel execution, AI code assistants can provide valuable insights to enhance your code's performance.

AI Code Assistants Tools To Try

GitHub Copilot

GitHub Copilot is an AI-powered code assistant developed by GitHub in collaboration with OpenAI. It provides users with code suggestions in various programming languages and frameworks, effectively acting as a pair programmer. Copilot is trained on a vast corpus of publicly available code and can generate code snippets, entire functions, and even complex algorithms. Its integration with Visual Studio Code allows for a seamless coding experience.

Copilot adapts to the coding style of the user, improving its suggestions over time. It's particularly useful for writing boilerplate code, learning new languages or frameworks, and overcoming coding blocks. However, it's important to review and test the suggested code.

Tabnine

Tabnine is an AI-driven code completion tool designed to boost developer productivity by offering smart and context-aware code completions. It supports a wide range of programming languages and integrates with various IDEs. Tabnine's deep learning model is trained on a diverse set of codebases, allowing it to understand and predict developer intentions accurately. 

The tool offers suggestions for code snippets, method completions, and can even predict the next line of code or generate entire code blocks. Tabnine's ability to adapt to the user's coding style makes it a highly personalized tool. It also respects code privacy, with the ability to run the model locally on the user's machine (enterprise plan only), ensuring that sensitive code doesn't leave the local environment.