![]() ![]() ![]() 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. We will keep improving the filter system to be more intelligent to detect and remove more personal data from the GitHub Copilot suggestions. We have also implemented a filter that blocks emails when shown in standard formats, but it’s still possible to get the model to suggest this sort of content if you try hard enough. For example, when one of our engineers prompted GitHub Copilot with, “My name is Mona and my birthdate is,” GitHub Copilot suggested a random, fictitious date of “December 12,” which is not Mona’s actual birthdate. – but those suggestions are actually fictitious information synthesized from patterns in training data and therefore do not relate to any particular individual. In some cases, the model will suggest what appears to be personal data – email addresses, phone numbers, etc. From our internal testing, we found it to be very rare that GitHub Copilot suggestions included personal data verbatim from the training set. As the developer, you are always in charge.īecause the model powering GitHub Copilot was trained on publicly available code, its training set included personal data that was included in that code. 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%. This could result in returning some employees who have reservations that started in the previous year and ended in the current year.Ĭopilot can produce code with bugs or not completely correct code so it’s important to carefully review and test any code generated by Copilot to ensure that it meets the required functionality and is free of bugs or issues.In a recent evaluation, we found that users accepted on average 26% of all completions shown by GitHub Copilot. It does not check whether the reservation end date is also in the current year. The potential bug in this code is that it only checks whether an employee has a reservation with a start date in the current year. I tried to ask Copilot what a potential bug in this code might be, by providing it with a question within a comment ( //q:What is a potential bug in this code?) but I didn’t get any suggestions while ChatGPT was more helpful with providing insights in potential bugs and issues with some peace of code. Copilot can generate multiple suggestions for code, but it is up to the you to evaluate them carefully and choose the best suggestion that fits your needs. This is because the second query performs a single database query that filters the Employees table and loads only the relevant data into memory, while the first query performs two separate database queries and then filters the data in memory using LINQ. ![]() In terms of performance, the second query is likely to be more efficient than the first query. ![]()
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