Just like when you write any code that uses material you did not independently originate, you should take precautions to understand how it works and ensure its suitability. In addition, we have announced that we are building a feature that will provide a reference for suggestions that resemble public code on GitHub so that you can make a more informed decision about whether and how to use that code, as well as explore and learn how that code is used in other projects. If there is a match, the suggestion will not be shown to you. With the filter enabled, GitHub Copilot checks code suggestions with its surrounding code for matches or near matches (ignoring whitespace) against public code on GitHub of about 150 characters.

These organization administrators can turn the filter on or off during setup (assuming their Enterprise administrator has deferred control) for the users in their organization. They can control suggestions for all organizations or defer control to individual organization administrators. For Copilot for Business users, the Enterprise administrator controls how the filter is applied. We built a filter to help detect and suppress GitHub Copilot suggestions which contain code that matches public code on GitHub.Ĭopilot for Individual users have the choice to enable that filter during setup on their individual accounts. As the developer, you are always in charge. Like any other code, code suggested by GitHub Copilot should be carefully tested, reviewed, and vetted. For suggested code, certain languages like Python, JavaScript, TypeScript, and Go might perform better compared to other programming languages. When converting comments written in non-English to code, there may be performance disparities when compared to English. And it may suggest old or deprecated uses of libraries and languages. GitHub Copilot can only hold a very limited context, so it may not make use of helpful functions defined elsewhere in your project or even in the same file. It is designed to generate the best code possible given the context it has access to, but it doesn’t test the code it suggests so the code may not always work, or even make sense. However, GitHub Copilot does not write perfect code. We also found that on average more than 27% of developers’ code files were generated by GitHub Copilot, and in certain languages like Python that goes up to 40%.

In a recent evaluation, we found that users accepted on average 26% of all completions shown by GitHub Copilot.

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