An AI-based coding journey utilizes Artificial Intelligence, such as Large-Language-Models (LLMs), to assist developers in various aspects of the coding process, including:
- Writing
- Editing
- Debugging
- Testing
- Documenting
Let's discuss precisely where and for what purpose AI can help developers.
The autocomplete feature allows for completing standard code. Developers can generate complete code snippets from prompts or code comments.
Let me explain this with the help of an example: For instance, if a developer frequently needs to create a login page, building it repeatedly from scratch does not contribute to the learning phase. Artificial Intelligence can replace this repetitive aspect.
AI can be used to detect errors, bugs, and security vulnerabilities within the code. It can also identify and suggest fixes for potential errors.
AI can help write comments directly into the code for better understanding and clarity. Writing documentation is indeed a hectic process, isn't it, developers?
Many AI coding tools offer extensions for different Integrated Development Environments (IDEs) such as Visual Studio, Neovim, JetBrains, etc. This expands the use cases of AI tools in coding, making it more convenient for developers.
So rather than again and again going to the website of AI tool and copy and paste and vice-versa , one can easily embed AI in there code editors for better usability and for saving the time
Several artificial intelligence tools offer similar features, including:
- ChatGPT
- GitHub Copilot
- Amazon Code Whisperer
- And more...
These tools aim to enhance the coding experience by providing intelligent assistance in writing, editing, debugging, testing, and documenting code.