GitHub Copilot is an AI pair programmer that helps you write code faster and with less work. It draws context from comments and code to suggest individual lines and whole functions instantly. GitHub Copilot is powered by OpenAI Codex, a generative pretrained language model created by OpenAI. It is available as an extension for Visual Studio Code, Visual Studio, Neovim, and the JetBrains suite of integrated development environments (IDEs).

What data has GitHub Copilot been trained on?

GitHub Copilot is powered by Codex, a generative pretrained AI model created by OpenAI. It has been trained on natural language text and source code from publicly available sources, including code in public repositories on GitHub.

Does GitHub Copilot write perfect code?

In a recent evaluation, we found that users accepted on average 26% of all completions shown by GitHub Copilot. 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%. However, GitHub Copilot does not write perfect code. 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. 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. And it may suggest old or deprecated uses of libraries and languages. When converting comments written in non-English to code, there may be performance disparities when compared to English. For suggested code, certain languages like Python, JavaScript, TypeScript, and Go might perform better compared to other programming languages. Like any other code, code suggested by GitHub Copilot should be carefully tested, reviewed, and vetted. As the developer, you are always in charge.

Will GitHub Copilot help me write code for a new platform?

GitHub Copilot is trained on public code. When a new library, framework, or API is released, there is less public code available for the model to learn from. That reduces GitHub Copilot’s ability to provide suggestions for the new codebase. As more examples enter the public space, we integrate them into the training set and suggestion relevance improves. In the future, we will provide ways to highlight newer APIs and samples to raise their relevance in GitHub Copilot’s suggestions.

How does a customer get the most out of GitHub Copilot?

GitHub Copilot works best when you divide your code into small functions, use meaningful names for functions parameters, and write good docstrings and comments as you go. It also seems to do best when it’s helping you navigate unfamiliar libraries or frameworks.

Can GitHub Copilot introduce insecure code in its suggestions?

Public code may contain insecure coding patterns, bugs, or references to outdated APIs or idioms. When GitHub Copilot synthesizes code suggestions based on this data, it can also synthesize code that contains these undesirable patterns. This is something we care a lot about at GitHub, and in recent years we’ve provided tools such as GitHub Actions, Dependabot, and CodeQL to open source projects to help improve code quality. Of course, you should always use GitHub Copilot together with good testing and code review practices and security tools, as well as your own judgment.

Does GitHub own the code generated by GitHub Copilot?

GitHub Copilot is a tool, like a compiler or a pen. GitHub does not own the suggestions GitHub Copilot provides to you. You are responsible for the code you write with GitHub Copilot’s help. We recommend that you carefully test, review, and vet the code before pushing it to production, as you would with any code you write that incorporates material you did not independently originate.

Does GitHub Copilot copy code from the training set?

GitHub Copilot’s suggestions are all generated through AI. GitHub Copilot generates new code in a probabilistic way, and the probability that they produce the same code as a snippet that occurred in training is low. The models do not contain a database of code, and they do not ‘look up’ snippets. Our latest internal research shows that about 1% of the time, a suggestion may contain some code snippets longer than ~150 characters that matches the training set. Previous research showed that many of these cases happen when GitHub Copilot is unable to glean sufficient context from the code you are writing, or when there is a common, perhaps even universal, solution to the problem.

What can I do to reduce GitHub Copilot’s suggestion of code that matches public code?

We built a filter to help detect and suppress GitHub Copilot suggestions which contain code that matches public code on GitHub. Copilot for Individual users have the choice to enable that filter during setup on their individual accounts. For Copilot for Business users, the Enterprise administrator controls how the filter is applied. They can control suggestions for all organizations or defer control to individual organization administrators. 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. 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. If there is a match, the suggestion will not be shown to you. 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. 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. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.

Other than the filter, what other measures can I take to assess code suggested by GitHub Copilot?

You should take the same precautions as you would with any code you write that uses material you did not independently originate, and should take precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.

Will GitHub Copilot work as well using languages other than English?

Given public sources are predominantly in English, GitHub Copilot will likely work less well in scenarios where natural language prompts provided by the developer are not in English and/or are grammatically incorrect. Therefore, non-English speakers might experience a lower quality of service.

Does GitHub Copilot support accessibility features?

We are conducting internal testing of GitHub Copilot’s ease of use by developers with disabilities and working to ensure that GitHub Copilot is accessible to all developers. Please feel free to share your feedback on GitHub Copilot accessibility in our feedback forum .

Does GitHub Copilot produce offensive outputs?

GitHub Copilot includes filters to block offensive language in the prompts and to avoid synthesizing suggestions in sensitive contexts. We continue to work on improving the filter system to more intelligently detect and remove offensive outputs. If you see offensive outputs, please report them directly to copilot-safety@github.com so that we can improve our safeguards. GitHub takes this challenge very seriously and we are committed to addressing it.

How will advanced code generation tools like GitHub Copilot affect developer jobs?

Bringing in more intelligent systems has the potential to bring enormous change to the developer experience. We do not expect GitHub Copilot to replace developers. Rather, we expect GitHub Copilot to partner with developers, augment their capabilities, and enable them to be more productive, reduce manual tasks, and help them focus on interesting work. We also believe that GitHub Copilot has the potential to lower barriers to entry, enabling more people to explore software development, and join the next generation of developers. We are working to test these hypotheses with both internal and external research.

What data does Copilot for Business collect?

GitHub Copilot relies on file content and additional data to work. It collects data to provide the service, some of which is then retained for further analysis and product improvements. Copilot for Business collects data as described below: User Engagement Data When you use GitHub Copilot it will collect usage information about events generated when interacting with the IDE or editor. These events include user edit actions like completions accepted and dismissed, and error and general usage data to identify metrics like latency and features engagement. This information may include personal data, such as pseudonymous identifiers. Code Snippets Data GitHub Copilot transmits snippets of your code from your IDE to GitHub to provide Suggestions to you. Code snippets data is only transmitted in real-time to return Suggestions, and is discarded once a Suggestion is returned. Copilot for Business does not retain any Code Snippets Data.

How is the data in Copilot for Business used and shared?

User Engagement Data is used by GitHub, Microsoft, and OpenAI to provide the service and to enable improvements. Such uses may include: Evaluating GitHub Copilot, e.g., by measuring the positive impact it has on the user Fine tuning ranking and sorting algorithms and prompt crafting Detecting potential abuse of GitHub Copilot or violation of Acceptable Use Policies Conducting experiments and research related to developers and their use of developer tools and services

How can users of Copilot for Business control use of their data?

User engagement data (which includes pseudonymous identifiers and general usage data), is required for the use of GitHub Copilot and will continue to be collected, processed, and shared with Microsoft and OpenAI when you use GitHub Copilot. Copilot for Business does not retain any Code Snippets Data.

What data does Copilot for Individuals collect?

GitHub Copilot relies on file content and additional data to work. It collects data to provide the service, some of which is then retained for further analysis and product improvements. GitHub Copilot collects the following data for individual users: User Engagement Data When you use GitHub Copilot it will collect usage information about events generated when interacting with the IDE or editor. These events include user edit actions like completions accepted and dismissed, and error and general usage data to identify metrics like latency and features engagement. This information may include personal data, such as pseudonymous identifiers. Code Snippets Data Depending on your preferred telemetry settings, GitHub Copilot may also collect and retain the following, collectively referred to as “code snippets”: source code that you are editing, related files and other files open in the same IDE or editor, URLs of repositories and files path.

How is the data in Copilot for Individuals used and shared?

User Engagement Data and Code Snippets Data is used by GitHub, Microsoft, and OpenAI to improve GitHub Copilot and related services and to conduct product and academic research about developers. Such uses may include: Directly improving GitHub Copilot, including assessing different strategies in processing and predicting which suggestions users may find helpful Developing and improving closely related developer products and services from GitHub, Microsoft, and OpenAI Investigating and detecting potential abuse of GitHub Copilot or violation of Acceptable Use Policies Conducting experiments and research related to developers and their use of developer tools and services Evaluating GitHub Copilot, e.g., by measuring the positive impact it has on the user Improving the underlying code generation models, e.g., by providing positive and negative examples Fine tuning ranking and sorting algorithms and prompt crafting When processing Code Snippets Data, GitHub takes the protection measures described below in How is the transmitted Code Snippets data protected? and follows responsible practices in accordance with our Privacy Statement so that the use of your telemetry data to improve these models does not result in this data being shared with other GitHub Copilot users.

How is the transmitted Code Snippets data protected?

We know that user edit actions, source code snippets, and URLs of repositories and file paths are sensitive data. Consequently, several measures of protection are applied, including: The transmitted data is encrypted in transit and at rest Access is strictly controlled. The data can only be accessed by (1) named GitHub personnel working on the GitHub Copilot team or on the GitHub platform health team, (2) Microsoft personnel working on or with the GitHub Copilot team, and (3) OpenAI personnel who work on GitHub Copilot Role-based access controls and multi-factor authentication are required for personnel accessing code snippet data

How can users of Copilot for Individuals control use of their Code Snippets Data?

GitHub Copilot gives you choices about how it uses the data it collects. User Engagement Data (which includes pseudonymous identifiers and general usage data), is required for the use of GitHub Copilot and will continue to be collected, processed, and shared with Microsoft and OpenAI as you use GitHub Copilot. Users of Copilot for Individuals can choose whether Code Snippets Data is retained by GitHub and further processed and shared with Microsoft and OpenAI by adjusting user settings . Users of Copilot for Individuals can request deletion of Code Snippet Data associated with their GitHub identity by filling out a support ticket .

Will my private code be shared with other users?

No. We follow responsible practices in accordance with our Privacy Statement to ensure that your code snippets will not be used as suggested code for other users of GitHub Copilot.

Does GitHub Copilot ever output personal data?

Because Codex, the model powering GitHub Copilot, was trained on publicly available code, its training set included public personal data that was included in that code. From our internal testing, we found it to be very rare that GitHub Copilot suggestions included personal data verbatim from the training set. In some cases, the model will suggest what appears to be personal data – email addresses, phone numbers, etc. – but those suggestions are actually fictitious information synthesized from patterns in training data and therefore do not relate to any particular individual. 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. 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. We will keep improving the filter system to be more intelligent to detect and remove more personal data from the GitHub Copilot suggestions.

Where can I learn more about GitHub Privacy and data protection?

For more information on how GitHub processes and uses personal data, please see the GitHub Copilot Privacy Statement .

Your AI-powered programming partner

Q: How can a customer contribute?

By using GitHub Copilot and sharing your feedback in the feedback forum , you help to improve GitHub Copilot. Please also report incidents (e.g., offensive output, code vulnerabilities, apparent personal information in code generation) directly to copilot-safety@github.com so that we can improve our safeguards. GitHub takes safety and security very seriously and we are committed to continually improving.

CodifyAI utilizes state-of-the-art AI technology to deliver real-time code suggestions and comprehensive functions seamlessly integrated into your editor.

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#!/usr/bin/env ts-node

import { fetch } from "fetch-h2";

// Determine whether the sentiment of text is positive

// Use a web service

async function isPositive(text: string): Promise<boolean> {

  const response = await fetch(`http://text-processing.com/api/sentiment/`, {

    method: "POST",

    body: `text=${text}`,

    headers: {

      "Content-Type": "application/x-www-form-urlencoded",

},

});

  const json = await response.json();

  return json.label === "pos";

package main

type CategorySummary struct {

    Title       string

    Tasks       int

    AvgValue    float64

func createTables(db *sql.DB) {

    db.Exec("CREATE TABLE tasks (id INTEGER PRIMARY KEY, title TEXT, value INTEGER, category TEXT)")

func createCategorySummaries(db *sql.DB) ([]CategorySummary, error) {

    var summaries []CategorySummary

    rows, err := db.Query("SELECT category, COUNT(category), AVG(value) FROM tasks GROUP BY category")

    if err != nil {

        return nil, err

    defer rows.Close()

    for rows.Next() {

        var summary CategorySummary

        err := rows.Scan(&summary.Title, &summary.Tasks, &summary.AvgValue)

        if err != nil {

            return nil, err

        summaries = append(summaries, summary)

    return summaries, nil

import datetime

def parse_expenses(expenses_string):

    """Parse the list of expenses and return the list of triples (date, value, currency).

    Ignore lines starting with #.

    Parse the date using datetime.

    Example expenses_string:

        2016-01-02 -34.01 USD

        2016-01-03 2.59 DKK

        2016-01-03 -2.72 EUR

"""

    expenses = []

    for line in expenses_string.splitlines():

        if line.startswith("#"):

            continue

        date, value, currency = line.split(" ")

        expenses.append((datetime.datetime.strptime(date, "%Y-%m-%d"),

                        float(value),

                        currency))

    return expenses

class CreateShippingAddresses < ActiveRecord::Migration

  def change

    create_table :shipping_addresses do |t|

      t.string :name

      t.string :address

      t.string :city

      t.string :zip

      t.string :state

      t.string :phone

      t.string :email

      t.references :shipping_method, index: true, foreign_key: true

      t.references :customer, index: true, foreign_key: true

      t.timestamps null: false

end

end

end

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Powered by extensive training on billions of lines of code, CodifyAI transforms natural language prompts into coding suggestions across a wide range of languages.

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Frequently asked questions

What is CodifyAI?

CodifyAI is an AI-powered coding assistant designed to help you write, debug, and optimize your code faster. It provides features such as code completion, code explanation, error detection, debugging, and code optimization. It is an extension available on Visual Studio Code and is licensed under the MIT License.

What data has CodifyAI been trained on?

CodifyAI has been trained on a massive amount of data consisting of billions of lines of code. This data comes from public GitHub repositories and public datasets, which allows CodifyAI to have a diverse range of training examples. With this extensive training, CodifyAI is equipped with the ability to recognize patterns in code and provide accurate suggestions for code completion, error detection, debugging, code explanation, and optimization.

Does CodifyAI write perfect code?

No, CodifyAI does not write perfect code. It is a tool designed to assist with coding by providing suggestions and insights, but the responsibility for ensuring the quality and correctness of the code ultimately lies with the human programmer.

How does a customer get the most out of CodifyAI?

To get the most out of CodifyAI, customers can use the tool's features such as code completion, code explanation, error detection, debugging, and code optimization to write, debug, and optimize their code faster. Additionally, it is important to remember that while CodifyAI can provide suggestions and insights, the responsibility for ensuring the quality and correctness of the code ultimately lies with the human programmer.

How can a customer contribute?

There are several ways in which customers can contribute to the development of CodifyAI. One way to contribute is by reporting bugs or suggesting new features. You can do this by emailing us or going to the discussion board. This helps the developers understand what issues you are facing and what features you would like to see added to the tool.

Another way to contribute is by helping with the development of the tool. If you have experience with coding or AI, you could contribute by developing new features or improving existing ones. To do this, you can contact the developers and express your interest in contributing.

Finally, if you find CodifyAI helpful, you can promote it by sharing it with your friends and colleagues. This helps spread the word about the tool and can help more people benefit from its features.

If you are interested in contributing to the development of CodifyAI, please send a message to the developers.