GitHub Copilot Introduction, an AI-assisted Coding
Last Updated on June 5, 2024
GitHub Copilot is one of the most interesting tools released to the preview stage by GitHub last year, initially announced on June 29, 2021. It is an AI-assisted pair programmer that helps write code more quickly and efficiently. GitHub Copilot extracts context from comments and code to provide quick suggestions for individual lines and whole functions. On paper, this concept looks promising. AI-assisted programming is a huge step forward in terms of lowering the barrier to entry into programming. However, It may not be clever enough to propose the greatest coding practices right now. In this article, you will get to know basic information that helps you understand how does it works and in which way it may be beneficial for you.
What is GitHub Copilot and how does it work?
OpenAI Codex is a new AI system developed by OpenAI powers and sits at the core of GitHub Copilot. It was taught to comprehend both programming and natural language using freely available sources. Three main functionalities that this tool has to offer are converting comments to code, auto-filling for repetitive code, and testing without the toil.
GitHub AI supports a wide range of frameworks and programming languages. The technical preview performs well in JavaScript, Python, Java, Ruby, TypeScript, and Go, but it understands lots of different languages. It is available as an extension in three different development environments.
Here’s how to get started in each one of them:
GitHub Copilot for Visual Studio Code
You must first install the Visual Studio Code plugin before you can utilize GitHub Copilot. Visual Studio Code should now be open. When you sign in to GitHub, you’ll be asked to authorize the extension. You’ll be sent back to Visual Studio Code once you’ve authorized the plugin.
GitHub Copilot for JetBrains
The extension is confirmed to work with JetBrains IDEs: IntelliJ and PyCharm versions 2021.2 and above. Look for ‘github copilot’ on the JetBrains marketplace. Click the ‘Install’ button and accept the Third-Party Plugins Privacy Notice. Then you will have to restart the IDE. After this operation is completed, the GitHub Copilot tool will be visible in the menu.
In an external browser, log in to GitHub Copilot using the device auth flow and authorize the GitHub Copilot IntelliJ plugin using your GitHub account.
GitHub Copilot for Neovim
Install Node.js 12 or newer and the Neovim 0.6 or newer. Vim-plug, packer.nvim, or any other plugin manager can be used to install github/copilot.vim. Start Neovim and invoke :Copilot setup.
File content, both in the file you’re editing and in nearby or related files, is used by GitHub Copilot to provide context. When you use GitHub Copilot, it may additionally gather repository or file path URLs to find relevant context.
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How good is it?
GitHub Copilot gives you the possibility to cycle through alternative recommendations, accepting or rejecting them on the spot or adjusting the lines. The developer is always in control. Copilot is said to be learning from made modifications and adapts to personal coding style. GitHub reported about running tests against a group of Python functions that have good test coverage in open source repositories. The function bodies were left blank and GitHub Copilot was tasked with filling them up. On the first try, the model got it right 43% of the time and 57% of the time when given 10 attempts.
Extension tries to comprehend your purpose and create the best code it can, but the lines it offers may or may not function or make sense. As it becomes smarter by the day, any suggestions should be thoroughly tested, reviewed, and verified, just like any other code. GitHub says It works best if the developer breaks code down into discrete functions, gives function arguments meaningful labels and comments on the go. It also seems to perform best when guiding through new libraries or frameworks.
Is GitHub Copilot safe?
According to research published in August 2021, when Copilot was used to create code in situations relevant to high-risk cybersecurity issues, 40% of the code generated by Copilot was found to be insecure. It is necessary to oversee outcomes as if It was any other code.
AI is trained by public code which is not free from unsafe practices, flaws, and outdated information. When GitHub Copilot generates code suggestions based on this information, it may generate code that has certain non-risk-free patterns. GitHub Copilot should always be used in conjunction with testing and security tools, as well as users’ discretion. To develop the model, GitHub collects telemetry data, which includes information on which suggestions people accept or reject. Private code is not shared with other GitHub Copilot users as suggested code.
Will it affect developer jobs?
AI will not replace developers in their jobs for quite some time. For now, AI-assisted programming is a great tool to help humans make their life easier. Developer productivity could be increased thanks to GitHub Copilot. It could also assist in lowering the cost of software development. This technology will help current engineers be more productive by minimizing manual duties and allowing them to focus on more exciting work. GitHub Copilot has the potential to decrease entry barriers, allowing more people to try their hand at software creation and become part of the future generation of engineers.
GitHub Copilot’s technical treview and tests
GitHub Copilot is available for free to a limited number of testers during the technical preview. To become one of them you’d have to sign in to join the waitlist on their website.
If the technical preview goes well, GitHub will move on with developing a commercial version of the extension.
Main pros and cons
To sum this up, the extension may make the developer’s job more efficient, less time-consuming, and simply enjoyable. However, shortcomings connected with the reproduction of pure quality code are possible and need to be taken into the consideration by users.
