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GitHub Copilot Chat Transparency Note

Learn about the capabilities, limitations, and best practices for using GitHub Copilot Chat during the technical preview.

What is GitHub Copilot Chat?

GitHub Copilot Chat is a chat interface that lets you ask and receive answers to coding-related questions. The chat interface provides you with quick and convenient access to coding information and support without requiring you to navigate complex documentation or search through online forums. This chat interface can answer a wide range of coding-related questions, including syntax, programming concepts, test cases, debugging, and more.

In addition to the GitHub Copilot auto-suggestions available within the GitHub Copilot code editor, this chat interface is designed to answer coding questions to boost your productivity by offering easy access to coding-related information and assistance.

What are the capabilities of GitHub Copilot Chat?

How does GitHub Copilot Chat work?

The system behavior of Copilot Chat can be broken down into several key steps.

  • 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 corpus of text data. The language model analyzes the input prompt and generates a response based on its understanding of the syntax and semantics of the input.
  • Response generation: The language model generates a response based on its analysis of the input prompt. 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 in a way that is easy to read and understand. This may include syntax highlighting, indentation, and other formatting features.

Overall, the system behavior of Copilot Chat is designed to be intuitive and user-friendly, allowing you to ask coding-related questions and complete coding tasks quickly and easily. However, it is important for users to be aware of the potential limitations and risks of using Copilot Chat, and to carefully review and validate the responses generated by the system to ensure they are accurate and appropriate for the task at hand.

How can I use GitHub Copilot Chat?

Copilot Chat can provide coding assistance in a variety of scenarios. Some examples include:

  • Generate unit test cases: Copilot Chat can help write unit test cases by generating code snippets based on the existing code in the editor or the code snippet highlighted in the editor by the user. This can be useful if you want to write test cases quickly and efficiently, without spending too much time on repetitive tasks. For example, if you are writing a test case for a specific function, you can use Copilot Chat to suggest possible input parameters and expected output values based on the function's signature and body. Copilot Chat can also suggest assertions that ensure the function is working correctly, based on the code's context and semantics. Furthermore, Copilot Chat can help you write test cases for edge cases and boundary conditions that might be difficult to identify manually. For instance, it can suggest test cases for error handling, null values, or unexpected input types, helping you ensure their code is robust and resilient. However, it is important to note that generated test cases may not cover all possible scenarios, and manual testing and code review are still necessary to ensure the quality of the code.

  • Explain code: 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 plain 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. Overall, by generating plain language explanations and suggesting documentation, Copilot Chat can help developers and stakeholders understand the selected code more easily and efficiently, leading to better collaboration and more effective software development. However, it is important to note that the generated explanations and documentation may not always be accurate or complete, and manual review and correction may be necessary.

  • Propose code fixes: Copilot Chat can help 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 is important to note that the suggested fixes may not always be optimal or complete, and manual review and testing may be necessary to ensure the quality of the code.

  • Answer coding questions: Copilot Chat allows you to ask 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. Additionally, a chat interface can provide a more personalized and interactive experience compared to traditional documentation or support forums, allowing developers to receive assistance in real-time.

What are the limitations of GitHub Copilot Chat?

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

Here are some of the limitations of Copilot Chat:

  • Limited scope: Copilot Chat has been trained on a large corpus of code but still has a limited scope and may not be able to handle more complex code structures or obscure programming languages. 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. Users should be careful when using Copilot Chat to generate code for security-sensitive applications and should 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. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.

  • 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.

What are the best practices for improving the performance of GitHub Copilot Chat?

The Copilot Chat service can support a wide range of 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 concerns mentioned under the "Limitations" section, there are various measures that you can adopt.

  • Keep your prompts on topic: The intended use of Copilot Chat is 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. Users should always review and test the code generated by Copilot Chat to ensure that it meets their 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. Users should follow best practices for secure coding, such as avoiding hard-coded passwords or SQL injection vulnerabilities, and code review practices as described above to address Copilot Chat’s limitations.

  • Provide feedback: If users encounter any issues or limitations with Copilot Chat, they should provide feedback through the share feedback link on the portal. This can help the developers to improve the tool and address any concerns or limitations.

  • Stay up to date: Copilot Chat is a new technology and is likely to evolve over time. Users 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.