Since entering the programming scene, GitHub Copilot has sparked discussions about whether it’s a giant leap toward the end of traditional coding or just a really good autocomplete tool.
With the rise of AI, Copilot has gotten even better.
Let’s break it down and see why it’s a handy tool for programmers to speed up the coding process, learn new frameworks or libraries, or explore different ways of solving problems.
What Is GitHub Copilot?
GitHub Copilot is an AI pair programmer providing auto-complete style recommendations as you code. It accelerates your coding process by analyzing contextual elements like variable names, surrounding code, and function signatures, generating relevant suggestions in real time.
The concept of an “AI pair programmer” underscores its collaborative nature, emphasizing that it complements and augments your coding efforts—it cannot work without you.
GitHub Copilot is powered by a generative AI model developed by GitHub, OpenAI, and Microsoft.
Once installed, the coding assistant analyzes the developer’s code, providing suggestions for completing code snippets, suggesting relevant functions, and even refactoring existing code.
Installing GitHub Copilot: Step-by-Step Guide
GitHub Copilot integrates with various popular integrated development environments (IDEs), such as Visual Studio Code, Visual Studio, Neovim, and JetBrains IDEs.
GitHub Copilot can be accessed through personal accounts with GitHub Copilot Individual or organizational accounts with GitHub Copilot Business. GitHub Copilot is free for verified students, teachers, and maintainers of popular open-source projects.
You can try GitHub Copilot for free with a one-time 30-day trial. After the free trial, you will need a paid subscription for continued use. Billing plans are available at GitHub Docs.
To get started, you’ll need the following:
- An active GitHub Copilot subscription;
- Microsoft Visual Studio Code (or another preferred IDE);
- The GitHub Copilot extension installed in the Visual Studio Code.
How to Install GitHub Copilot?
- Find the GitHub Copilot extension page in the Visual Studio Code Marketplace and click Install.
- Click Open Visual Studio Code in the pop-up window that appeared.
- Click Install in the “Extension: GitHub Copilot” tab in Visual Studio Code.
- If you have not previously authorized Visual Studio Code in your GitHub account, you will be prompted to sign into GitHub in Visual Studio Code.
- If you have previously authorized Visual Studio Code for your account on GitHub, GitHub Copilot will be automatically authorized.
- If you don’t get the prompt to authorize, click the bell icon in the bottom panel of the Visual Studio Code window.
- In your browser, GitHub will request the necessary permissions for GitHub Copilot. To approve these permissions, click Authorize Visual Studio Code.
- To confirm the authentication, in Visual Studio Code, in the “Visual Studio Code” dialog box, click Open.
GitHub Copilot can be customized to align with your coding style. Configure settings like indentation, line length limits, and suggestion verbosity to make Copilot an extension of your coding preferences.
Creation of UI Elements with Prompts in GitHub Copilot
GitHub Copilot’s capability to generate code suggestions extends to creating user interface (UI) elements. By providing clear and concise prompts, developers can elicit relevant code snippets for various UI components, such as buttons, dropdown menus, and text fields.
Example: We need to create a tracker of monthly income and expenses.
Let’s generate a prompt for GitHub Copilot:
<!–bootstrap grid with 12 rows and 1 column for small screens, and 6 rows and 2 columns for medium screens and above, each cell contains label of the month from January to December, and 2 bootstrap number inputs side by side with labels, 1 for income and 1 for expenses –>)
Based on the input, GitHub Copilot starts to suggest the code to generate the desired output.
To accept the suggested code, just press Tab, and the assistant will write the code.
It’s crucial to understand that Copilot isn’t flawless enough to write code independently, as we highlighted earlier, its “pair programmer” nature. You are responsible for crafting the overall logic, guiding the tool with well-thought-out prompts, and overseeing the suggestions it generates. You steer the process through practical and precise instructions while the tool accelerates the code-writing process for you.
So, after a brief review, the developer reviews and improves the proposed structure and creates the desired table faster.
GitHub Copilot proves to be a valuable tool for frontend developers to elicit relevant code snippets for various UI components efficiently. The example illustrates the importance of reviewing Copilot’s suggestions by an experienced developer to ensure correctness and code optimization.
While Copilot accelerates the code-writing process, user intervention remains essential for ensuring logic, accuracy, and adherence to specific requirements. Let’s focus on how to engineer prompts that guarantee Copilot’s efficient assistance.
Getting Desired Outcomes with Prompts: Best Practices
Once we have reviewed the capabilities to generate entire functions, classes, or blocks of code faster, let’s touch on the best practices for prompt engineering to give clear instructions to GitHub Copilot and generate desired results.
- High-level context in comments: Provide high-level context in a comment at the top of the file and leave more detailed instructions in the form of comments and code.
- Provide specific details: For precise code suggestions from GitHub Copilot, provide specific details. If you aim, for example, to fetch data from an API, specify the type of data, processing method, and the target API endpoint.
- Provide examples: Apart from instructing GitHub Copilot verbally, you can also demonstrate desired actions using examples in your preferred coding style.
- Iterate and refine: Continuously refine your code with Copilot’s suggestions. Start with its recommendations and customize them to meet your specific requirements. Regularly review and adjust the generated code to match your coding style and project needs.
- Validate generated code: Validate Copilot-generated code for correctness, efficiency, and adherence to standards. Conduct thorough code reviews, run comprehensive tests, and make necessary adjustments to maintain codebase quality.
In turn, GitHub is experimenting with LLMs to evolve Copilot, focusing on creating a developer-friendly AI experience that is predictable, tolerable, steerable, and verifiable to enhance overall productivity and effectiveness. All this is done to extend GitHub Copilot across the developer lifecycle.
Other Ways to Enhance Your Coding Workflow with GitHub Copilot
Explore additional capabilities of GitHub Copilot to refine your coding process:
- Code refactoring: GitHub Copilot aids in refactoring by suggesting concise and efficient alternatives, identifying redundancies, simplifying complexity, and recommending improved coding patterns for cleaner, more maintainable code.
- Error & exception handling: Copilot assists in error handling by suggesting mechanisms and blocks for exceptions, enhancing code robustness and resilience to unexpected scenarios.
- Customization: Enhance GitHub Copilot by customizing its training with your codebases, allowing it to generate more accurate, context-aware suggestions tailored to your specific application domain.
- Consistency across teams: GitHub Copilot is valuable for team development, aiding in maintaining coding standards by suggesting consistent styles and practices. Its suggestions are discussion starters in code reviews, fostering collaboration, and knowledge sharing among team members.
It still requires profound expertise to build software. Even with Copilot’s help, a developer should verify and understand generated code. Non-programmers can’t jump on Copilot and make whatever they want. Or is it so yet?
Keep a keen eye on the dynamic AI assistant’s landscape—experiment with emerging options in your environment to identify the ones that align best with your requirements. Explore tools like GitHub Copilot, and stay tuned to learn more about alternatives: Bito, Amazon CodeWhisperer, Tabnine, etc.