Skip to main content

À propos du chat GitHub Copilot sur GitHub Mobile

GitHub Copilot Chat peut vous aider à trouver des réponses aux questions de codage directement dans GitHub Mobile.

À propos de GitHub Copilot Chat in GitHub Mobile

GitHub Copilot Chat in GitHub Mobile est une interface de discussion qui vous permet d’interagir avec GitHub Copilot pour poser des questions relatives au codage et recevoir leurs réponses dans GitHub.com. L’interface de conversation permet d’accéder aux informations sur le codage et au support sans avoir à parcourir la documentation ni à rechercher dans des forums en ligne. Outre GitHub Mobile, Copilot Chat est actuellement pris en charge dans GitHub.com, Visual Studio Code, Visual Studio et la suite d’IDE JetBrains. Pour plus d’informations sur GitHub Copilot, consultez « À propos de GitHub Copilot Individual », « À propos de GitHub Copilot Business » et « À propos de GitHub Copilot Enterprise. »

GitHub Copilot Chat peut répondre à un large éventail de questions relatives au codage sur des sujets comme la syntaxe, les concepts de programmation, les cas de test, le débogage, etc. GitHub Copilot Chat n’est pas conçu pour répondre à des questions portant sur autre chose que le codage ni pour fournir des informations générales sur des sujets autres que le codage.

La principale langue prise en charge pour GitHub Copilot Chat in GitHub Mobile est l’anglais.

GitHub Copilot Chat fonctionne en utilisant une combinaison de traitement en langage naturel et de machine learning pour comprendre votre question et vous fournir une réponse. Ce processus peut être divisé en plusieurs étapes.

Input processing

The input prompt from the user is pre-processed by the Copilot Chat system and sent to a large language model to get a response based on the context and prompt. User input can take the form of code snippets or plain language. The system is only intended to respond to coding-related questions.

Language model analysis

The pre-processed prompt is then passed through the Copilot Chat language model, which is a neural network that has been trained on a large body of text data. The language model analyzes the input prompt.

Response generation

The language model generates a response based on its analysis of the input prompt and the context provided to it. This response can take the form of generated code, code suggestions, or explanations of existing code.

Output formatting

The response generated by Copilot Chat is formatted and presented to the user. Copilot Chat may use syntax highlighting, indentation, and other formatting features to add clarity to the generated response. Depending upon the type of question from the user, links to context that the model used when generating a response, such as source code files or documentation, may also be provided.

GitHub Copilot Chat is intended to provide you with the most relevant answer to your question. However, it may not always provide the answer you are looking for. Users of Copilot Chat are responsible for reviewing and validating responses generated by the system to ensure they are accurate and appropriate. For more information on improving the performance of Copilot Chat in GitHub Mobile, see "Improving performance for Copilot Chat in GitHub Mobile."

Differences per GitHub Copilot plan

The options available to you in Copilot Chat in GitHub Mobile vary depending on the GitHub Copilot plan you are using.

  • Only people with a GitHub Copilot Enterprise subscription can access and have conversations using the data from private indexed repositories.
  • If you have a GitHub Copilot Enterprise subscription and you have enabled Bing search integration (beta), Copilot Chat in GitHub Mobile may respond using information based on the results of a Bing search. For information on how to enable or disable Bing search integration, see "Managing policies and features for Copilot in your enterprise" in the GitHub Enterprise Cloud documentation.
  • In addition to general coding conversations or conversations about a single file, people with a Copilot Individual subscription have the ability to discuss top popular public repositories using embeddings.

If you do not have a GitHub Copilot subscription, you can purchase a Copilot Individual subscription directly in the iOS version of GitHub Mobile, or in the Google Play Store for the Android version of GitHub Mobile.

Use cases for GitHub Copilot Chat in GitHub Mobile

GitHub Copilot Chat in GitHub Mobile can provide coding assistance in a variety of scenarios.

Explaining code and suggesting improvements

Copilot Chat can help explain selected code by generating natural language descriptions of the code's functionality and purpose. This can be useful if you want to understand the code's behavior or for non-technical stakeholders who need to understand how the code works. For example, if you select a function or code block in the code editor, Copilot Chat can generate a natural language description of what the code does and how it fits into the overall system. This can include information such as the function's input and output parameters, its dependencies, and its purpose in the larger application.

Copilot Chat can also suggest potential improvements to selected code, such as improved handling of errors and edge cases, or changes to the logical flow to make the code more readable.

By generating explanations and suggesting related documentation, Copilot Chat may help you to understand the selected code, leading to improved collaboration and more effective software development. However, it's important to note that the generated explanations and documentation may not always be accurate or complete, so you'll need to review, and occasionally correct, Copilot Chat's output.

Proposing code fixes

Copilot Chat can propose a fix for bugs in your code by suggesting code snippets and solutions based on the context of the error or issue. This can be useful if you are struggling to identify the root cause of a bug or you need guidance on the best way to fix it. For example, if your code produces an error message or warning, Copilot Chat can suggest possible fixes based on the error message, the code's syntax, and the surrounding code.

Copilot Chat can suggest changes to variables, control structures, or function calls that might resolve the issue and generate code snippets that can be incorporated into the codebase. However, it's important to note that the suggested fixes may not always be optimal or complete, so you'll need to review and test the suggestions.

Answering coding questions

You can ask Copilot Chat for help or clarification on specific coding problems and receive responses in natural language format or in code snippet format. This can be a useful tool for programmers, as it can provide guidance and support for common coding tasks and challenges.

Improving performance for Copilot Chat in GitHub Mobile

Copilot Chat can support a wide range of practical applications like code generation, code analysis, and code fixes, each with different performance metrics and mitigation strategies. To enhance performance and address some of the the limitations of Copilot Chat, there are various measures that you can adopt. For more information on the limitations of Copilot Chat in GitHub Mobile, see "Limitations of Copilot Chat in GitHub Mobile."

Keep your prompts on topic

Copilot Chat is intended to address queries related to coding exclusively. Therefore, limiting the prompt to coding questions or tasks can enhance the model's output quality.

Use Copilot Chat as a tool, not a replacement

While Copilot Chat can be a powerful tool for generating code, it is important to use it as a tool rather than a replacement for human programming. You should always review and test the code generated by Copilot Chat to ensure that it meets your requirements and is free of errors or security concerns.

Use secure coding and code review practices

While Copilot Chat can generate syntactically correct code, it may not always be secure. You should always follow best practices for secure coding, such as avoiding hard-coded passwords or SQL injection vulnerabilities, as well as following code review best practices, to address Copilot Chat's limitations.

Provide feedback

If you encounter any issues or limitations with Copilot Chat, we recommend that you provide feedback through the share feedback link in Copilot Chat in GitHub Mobile that appears when you dislike a response. This can help the developers to improve the tool and address any concerns or limitations.

Stay up to date

Copilot Chat in GitHub Mobile is a new technology and is likely to evolve over time. You should stay up to date with any updates or changes to the tool, as well as any new security risks or best practices that may emerge.

Limitations of Copilot Chat in GitHub Mobile

Depending on factors such as your codebase and input data, you may experience different levels of performance when using Copilot Chat. The following information is designed to help you understand system limitations and key concepts about performance as they apply to Copilot Chat.

Limited scope

Copilot Chat has been trained on a large body of code but still has a limited scope and may not be able to handle more complex code structures or obscure programming languages. For each language, the quality of suggestions you receive may depend on the volume and diversity of training data for that language. For example, JavaScript is well-represented in public repositories and is one of GitHub Copilot's best supported languages. Languages with less representation in public repositories may be more challenging for Copilot Chat to provide assistance with. Additionally, Copilot Chat can only suggest code based on the context of the code being written, so it may not be able to identify larger design or architectural issues.

Potential biases

Copilot's training data is drawn from existing code repositories, which may contain biases and errors that can be perpetuated by the tool. Additionally, Copilot Chat may be biased towards certain programming languages or coding styles, which can lead to suboptimal or incomplete code suggestions.

Security risks

Copilot Chat generates code based on the context of the code being written, which can potentially expose sensitive information or vulnerabilities if not used carefully. You should be careful when using Copilot Chat to generate code for security-sensitive applications and always review and test the generated code thoroughly.

Matches with public code

Copilot Chat is capable of generating new code, which it does in a probabilistic way. While the probability that it may produce code that matches code in the training set is low, a Copilot Chat suggestion may contain some code snippets that match code in the training set. Copilot Chat utilizes filters that block matches with public code on GitHub repositories, but you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities.

Inaccurate code

One of the limitations of Copilot Chat is that it may generate code that appears to be valid but may not actually be semantically or syntactically correct or may not accurately reflect the intent of the developer. To mitigate the risk of inaccurate code, you should carefully review and test the generated code, particularly when dealing with critical or sensitive applications. You should also ensure that the generated code adheres to best practices and design patterns and fits within the overall architecture and style of the codebase.

Inaccurate responses to non-coding topics

Copilot Chat is not designed to answer non-coding questions, and therefore its responses may not always be accurate or helpful in these contexts. If a user asks Copilot Chat a non-coding question, it may generate an answer that is irrelevant or nonsensical, or it may simply indicate that it is unable to provide a useful response.

Pour aller plus loin