GitHub Copilot. More Effective Coding with AI

GitHub Copilot. More Effective Coding with AI

In summer 2021, Microsoft and its subsidiary GitHub announced the launch of Copilot – a neural network capable of writing code correctly for developers.

What’s Copilot?

GitHub CEO Nat Friedman simply says that Copilot – is “Your AI pair programmer”. Built on the GPT-3 language model, GitHub Copilot is an AI-powered code generator that can help developers write code faster and more efficiently than ever before. 

When a developer uses GitHub Copilot, the tool analyzes the code they are working on and generates suggestions for how to complete it. These suggestions can take the form of autocompletion suggestions, where GitHub Copilot suggests code as you type, or code snippets generated from natural language descriptions.

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Core functionality

For example, if a developer types “create a function that sorts an array of numbers,” GitHub Copilot will generate the code for that function, including comments explaining how it works. This can save developers a lot of time and effort, especially when working on large, complex projects.

GitHub Copilot also has the ability to learn from the codebase it is trained on. This means that as developers use it, the tool becomes more familiar with their coding style and can make more accurate and relevant suggestions.

To use GitHub Copilot, developers need to install the tool as an extension in their IDE (integrated development environment). Once installed, they can start using it. It’s important to note that GitHub Copilot is not perfect and may not always generate accurate or optimized code. Developers should always review and test the code generated by GitHub Copilot to ensure it meets their requirements.

Why Copilot is a game changer for developers

GitHub Copilot is a tool that offers many advantages for developers. Here are some of the main pros of using GitHub Copilot:

  • Faster coding. GitHub Copilot helps developers write code faster by offering autocompletion suggestions and generating code snippets based on natural language descriptions.
  • Improved accuracy. With GitHub Copilot, developers can be more confident in the accuracy of their code. Since the tool is powered by an AI language model, it has the ability to catch common syntax errors and suggest corrections.
  • Increased productivity. By automating repetitive coding tasks, GitHub Copilot frees up more time for developers to focus on more important aspects of their work.
  • Better collaboration. GitHub Copilot can help team members collaborate more effectively by providing them with pre-written code snippets that they can share and modify as needed.
  • Access to a vast knowledge base. GitHub Copilot has access to a vast repository of code from the GitHub community, which it can use to generate suggestions and code snippets for developers.
  • Reduced cognitive load. By providing developers with suggested code snippets and autocompletion suggestions, GitHub Copilot reduces the cognitive load involved in writing complex code.
  • Easy to use. GitHub Copilot is easy to install and use, with an intuitive interface that allows developers to get started quickly.

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Potential Downside of using Copilot

While GitHub Copilot is a powerful tool that can help developers be more productive and efficient, there are some potential drawbacks of using it. 

  • Dependency on the AI model. GitHub Copilot’s accuracy and usefulness are highly dependent on the quality of the underlying AI language model. If the model is not trained properly, or if it is not able to handle certain types of code, the suggestions generated by GitHub Copilot may be inaccurate or unhelpful.
  • Security concerns. GitHub Copilot works by analyzing and processing the code that developers are working on. This raises some potential security concerns, as it could allow sensitive code or data to be inadvertently shared or accessed by third parties.
  • Risk of code plagiarism. Since GitHub Copilot is designed to generate code snippets based on natural language descriptions, there is a risk that it could generate code that is very similar to already existing code, possibly leading to unintentional code plagiarism.
  • Limited flexibility. While GitHub Copilot is designed to automate repetitive coding tasks and suggest code snippets based on natural language descriptions, it may not be able to handle more complex or unconventional coding tasks.
  • Potential bias in AI model. Like all AI models, there is a risk of bias in GitHub Copilot’s language model, which could impact the quality and accuracy of the code suggestions it generates.
  • Limitations on open-source usage. Although GitHub Copilot is free for individual developers and for use in open-source projects, there are licensing restrictions that apply to commercial usage.

How our developers mitigate the risks

Of course, some of the drawbacks are beyond our control. But still, there are also steps our developers can take to mitigate the risks and overcome the challenges.

  • Practicing good security habits. To minimize the risk of sensitive code or data being inadvertently shared or accessed, our developers practice good security habits, such as using secure coding practices, keeping their IDE and GitHub Copilot extensions up to date, and using access controls to limit who can access their code.
  • Code plagiarism verification. To ensure that the code generated by GitHub Copilot is original and not plagiarized, our developers use plagiarism detection tools, such as Copyscape and Turnitin, to verify the originality of the code.
  • Using GitHub Copilot as an assistance, not a replacement. While GitHub Copilot can be a helpful tool for generating code suggestions and automating repetitive tasks, we don’t use it as a replacement for careful manual coding and testing. Our developers always review and test the code generated by GitHub Copilot to ensure it meets their requirements.

Overall, GitHub Copilot is a powerful tool that can help developers be more productive and efficient in their coding work. By automating repetitive coding tasks and generating accurate code suggestions, it can save developers time and effort, allowing them to focus on more important aspects of their work.

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