The top 8 AI tools for code generation

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.

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.

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.

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