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The top 8 AI tools for code generation

October 3, 2023

Code generation is one of the most exciting new technologies in software development. AI tools can now generate code that is just as good, or even better, than human-written code. This has the potential to revolutionize the way we write software.

Imagine teaching a child to create a simple paper boat. You guide through the folds, the tucks, and the final touches. Now, imagine if the child had a tool that could predict the next fold, or better yet, suggest a design tweak to make the boat float better.

AI code generation tools do exactly that but in the ocean of programming, helping navigate, create better ‘boats’ (codes), and occasionally introducing innovative tweaks to enhance performance and efficiency.

What are AI tools for code generation?

AI tools for code generation are software programs that use artificial intelligence to generate code. You can use these tools to generate code for a variety of programming languages, including Python, Java, JavaScript, and C++.

How do AI tools for code generation work?

AI tools for code generation work by training on large datasets of existing code. This training allows the tools to learn the patterns and rules that govern code writing. Once the tools are trained, they can be used to generate new code based on a natural language description or a few examples of existing code.

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Benefits of using AI tools for code generation

There are several benefits to using AI tools for code generation:

  • Increased productivity: AI tools can help you write code faster by automating repetitive tasks.
  • Improved code quality: AI tools can help you write better code by identifying potential errors and suggesting improvements.
  • Reduced development costs: AI tools can help you reduce the cost of software development by automating tasks that would otherwise be done by human developers.

 

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How to use AI tools for code generation?

Let’s envision a scenario where a developer, Alex, is working on a project that involves writing a Python function to fetch data from a weather API. The function must take a city name as input and return the current temperature. However, Alex isn’t entirely sure how to construct the HTTP request or parse the API’s JSON response.

Using an AI code generation tool like GitHub Copilot, which is powered by OpenAI Codex, Alex starts typing a comment in their code editor, describing the functionality they desire:

 

 

With Copilot active, the tool reads this comment and begins to generate a potential Python function below it:

 

In the generated code, Copilot creates a function get_temperature and automatically imports the requests library to make HTTP requests. It builds the URL for the API request using an API key placeholder and the input city_name, then sends a GET request to the weather API. Finally, it parses the JSON response to extract and return the current temperature.

Note: The API key and base_url may need to be modified according to the actual weather API documentation that Alex chooses to use.

Alex now has a robust starting point and can insert their actual API key, adjust endpoint URLs, or modify parameters according to their specific use case. This code generation saves Alex time. It also provides a reliable template for interacting with APIs. This is helpful if they’re unfamiliar with making HTTP requests in Python.

 

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Such AI tools analyze patterns in existing code and generate new lines of code optimized for readability, efficiency, and error-free execution. Moreover, these tools are especially useful for automating boilerplate or repetitive coding patterns, enhancing the developer’s productivity by allowing them to focus on more complex and creative aspects of coding.

How to fix bugs using AI tools?

Imagine a developer is working on a Python function that finds the square of a number. They initially write the following code:

 

Here, there’s a syntax error – the multiplication operator * is mistakenly written as x. When they try to run this code, it will fail. Enter GitHub Copilot, an AI-powered coding assistant developed by GitHub and OpenAI.

Upon integrating GitHub Copilot in their coding environment, the developer would start receiving real-time suggestions for code completion. In this case, when they type return num, GitHub Copilot might suggest the correction to complete it as return num * num, fixing the syntax error and providing a valid Python code.

 

The mechanism of Amazon’s CodeWhisperer for reviewing code
The mechanism of Amazon’s CodeWhisperer for reviewing code. Source: Amazon

 

 

The AI provides this suggestion based on patterns and syntax correctness it has learned from numerous code examples during its training. By accepting the suggestion, the developer swiftly moves past the error without manual troubleshooting, thereby saving time and enhancing productivity.

GitHub Copilot goes beyond merely fixing bugs. It can offer alternative methods, predict subsequent lines of code, and even provide examples or suggestions for whole functions or methods based on the initial inputs or comments in the code, making it a powerful ally in the software development process.

8 AI tools for code generation

Here are 8 of the best AI tools for code generation:

1. GitHub Copilot:

An AI code completion tool that can help you write code faster and with fewer errors. Copilot is trained on a massive dataset of code and can generate code in a variety of programming languages, including Python, Java, JavaScript, and C++.

2. ChatGPT:

Not just a text generator! ChatGPT exhibits its capability by generating efficient and readable lines of code, optimizing the programming process by leveraging pattern analysis in existing code.

 

Read more about 6 best ChatGPT plugins

 

3. OpenAI Codex:

A powerful AI code generation tool that can be used to generate entire programs from natural language descriptions. Codex is trained on a massive dataset of code and can generate code in a variety of programming languages, including Python, Java, JavaScript, and Go.

4. Tabnine:

An AI code completion tool that can help you write code faster and with fewer errors. Tabnine is trained on a massive dataset of code and can generate code in a variety of programming languages, including Python, Java, JavaScript, and C++.

5. Seek:

An AI code generation tool that can be used to generate code snippets, functions, and even entire programs from natural language descriptions. Seek is trained on a massive dataset of code and can generate code in a variety of programming languages, including Python, Java, JavaScript, and C++.

6. Enzyme:

An AI code generation tool that is specifically designed for frontend web development. Enzymes can be used to generate React components, HTML, and CSS from natural language descriptions.

7. Kite:

An AI code completion tool that can help you write code faster and with fewer errors. Kite is trained on a massive dataset of code and can generate code in a variety of programming languages, including Python, Java, JavaScript, and C++.

8. Codota:

An AI code assistant that can help you write code faster, better, and with fewer errors. Codota provides code completion, code analysis, and code refactoring suggestions. Codota is trained on a massive dataset of code and can generate code in a variety of programming languages, including Python, Java, JavaScript, and C++.

Why should you use AI code generation tools?

AI code generation tools such as these make a difference by saving developers’ time, minimizing errors, and even offering new learning curves for novice programmers.

Envision using GitHub Copilot: as you begin typing a line of code, it auto-completes or suggests the next few lines, based on patterns and practices from a vast repository of code. It’s like having a co-pilot in the coding journey that assists, suggests, and sometimes, takes over the controls to help you navigate through.

In closing, the realm of AI code generators is vast and ever-expanding, creating possibilities, enhancing efficiencies, and crafting a future where man and machine can co-create in harmony.

 

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