When using Copilot Autofix for historical alerts, you can now choose the branch to which you want to commit an autofix. You can also decide whether to then open a pull request, check out the branch locally, or open it in GitHub Desktop.
Copilot Autofix provides automatic fix suggestions for code scanning alerts in your codebase.
This update integrates Autofix more closely within the developer workflow, so you can quickly iterate on fix suggestions and collaborate on those with your team.
Enterprise admins can now manage and apply content exclusions at the enterprise level. This expands upon previous capabilities where only org admins and repo admins could apply exclusions. Enterprise admins can now implement exclusions that apply to all users within the enterprise, providing a more comprehensive and centralized approach to managing content exclusions.
How to get started?
Enterprise admins can access Copilot Content Exclusions by navigating to the Policies tab, clicking on Copilot, and then selecting the Content Exclusions tab.
How will repo-level rules change with the introduction of enterprise-level rules?
There are no changes to repo-level rules with the introduction of enterprise-level settings. If a repo admin has excluded certain files from that repository, those exclusions will continue to apply to all users working on that repo within the enterprise.
How will org-level rules change with the introduction of enterprise-level rules?
Currently, org-level rules apply to all users across the enterprise. However, once enterprise-level settings are available and applied by enterprise admins, org-level rules will only affect users who are assigned Copilot seats from that specific org. This change allows for more targeted control within each organization, ensuring that org rules are scoped more precisely.
Important Details for Org Admins
If you haven’t set any rules at the org level yet, any rules you set going forward will only apply to users getting Copilot seats from your org.
If you are an existing org with rules already set up for content exclusions, here’s what you need to know:
Before November 8th:
If Enterprise Admins Do Not Set Rules: Org-level rules will continue to apply to all users across the enterprise, functioning as they do today.
If Enterprise Admins Set Rules: Once enterprise-level rules are applied, org-level rules will only apply to users with Copilot seats from your specific org.
After November 8th:
Org-level rules will no longer apply enterprise-wide. They will be limited to users who are assigned Copilot seats from your org, regardless of whether enterprise-level rules are applied.
Please coordinate with your enterprise admins to ensure that rules are set correctly for your organization.
New skills have been added to Copilot Chat in VS Code, enabling you to search across GitHub to find commits, issues, pull requests, repositories, and topics. GitHub Copilot will either automatically infer when to use the @github agent, or you can invoke it directly by asking questions like:
– @github What are all of the open PRs assigned to me?
– @github What are the latest issues assigned to me?
– @github When was the latest release?
– @github Show me the recent merged pr's from @dancing-mona
In the latest Visual Studio Code release, you will find a suite of enhancements to GitHub Copilot Chat, designed to streamline your coding, debugging, and testing processes.
Sign up for early access to the latest OpenAI o1 models for more precise and efficient coding assistance. Once you have access, you will have the model picker control in Copilot Chat in VS Code. You can then choose which model version to use for your chat conversations.
Enhanced code quality with GPT-4o
Copilot Inline Chat now uses GPT-4o, giving you faster, more accurate, and higher-quality code and explanations when you use Chat in the editor.
Public code matching in chat
You can allow GitHub Copilot to return code that could match publicly available code on GitHub.com. When this functionality is enabled for your organization subscription or personal subscription, Copilot code completions already provided you with details about the matches that were detected. We now show you these matches for public code in Copilot Chat as well.
If this is enabled for your organization or subscription, you might see a message at the end of the response with a View matches link. If you select the link, an editor opens that shows you the details of the matching code references with more details.
File suggestions in chat
In chat input fields, you can now type # to get file name suggestions and quickly attach them to your prompt as context. This works in chat locations that support file attachments, such as the Chat view, Quick Chat, Inline Chat, and Notebook Chat.
Drag and drop files to add chat context
You can now attach additional files as context for a chat prompt by dragging files or editor tabs from the workbench directly into chat. For Inline Chat, hold Shift and drop a file to add it as context instead of opening it in the editor.
File attachments included in history
When you attach a file or editor selection as relevant context to your chat request, Copilot Chat will include them in the history of follow-on requests so that you can keep referring to them without having to reattach them. Previously, this context was added only for the current request and was not included in the history of follow-on requests.
Inline Chat and completions in Python native REPL
The native REPL editor, used by the Python extension, now supports Copilot Inline Chat and code completions directly in the input box.
You can perform an exact search across your files with the Search view. It also now uses Copilot to give search results that are semantically relevant.
This functionality is still in preview and by default, the setting is not enabled. Try it out and let us know what you think!
New fix test logic now helps you diagnose failing unit tests. This logic is triggered in some scenarios by the /fix slash command, and you can also invoke it directly with the /fixTestFailure slash command. The command is enabled in chat by default but can be disabled via the setting github.copilot.chat.fixTestFailure.enabled.
You can now use an experimental /setupTests slash command to configure the testing set up for your workspace. This command can recommend a testing framework, provide steps to set up and configure it, and suggest a VS Code extension to provide testing integration in VS Code.
When you use the /tests command to generate tests for your code, Copilot Chat can recommend /setupTests and testing extensions if it looks like such an integration has not been set up yet in your workspace.
You can use the /startDebugging slash command to find or create a launch configuration and start debugging your application. When you use @vscode in Copilot Chat, /startDebugging is now available by default.
You can now access chat via the Command Center, which provides access to all relevant chat commands, like starting the different chat experiences or attaching context to your prompt. Note that the Command Center itself needs to be enabled for the chat Command Center entry to show.
Custom test generation instructions (Experimental)
Generating tests with Copilot helps you write code that is more robust. With custom instructions you can ensure that the generated tests meet your specific coding style and requirements.
In addition, you can now define instructions for test generation in settings or import them from a file. For example, if you always want to use a particular unit testing framework for your tests. Configure the test-generation instructions in the github.copilot.chat.experimental.testGeneration.instructions setting.
✍️ We want your feedback
Try out these new features and share your experiences and feedback in our issues.
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With a subscription to Copilot Individual or Copilot Business, you can now access Copilot in GitHub.com, allowing you to:
Discover codebases on GitHub effortlessly using powerful natural language code search using Copilot Chat.
Streamline development processes by receiving suggestions to resolve build failures and summarizing changes in pull requests.
Quickly get up to speed with the help of Copilot through summaries and key takeaways from discussions, issues, pull requests and more.
These features are also now available in GitHub Mobile for all Copilot users.
You can now join the waitlist for early access to OpenAI o1 for use in GitHub Copilot in Visual Studio Code and GitHub Models. The waitlist is currently available to all Copilot users.
In Visual Studio Code, you can choose to use o1-preview or o1-mini to power GitHub Copilot Chat in place of the current default model, GPT-4o.
Note: to access this feature, you’ll need to be on VS Code Insiders with the latest pre-release version of the Copilot Chat extension.
In GitHub Models, you can use o1 models both in the playground and via the API. GitHub Models is currently in limited preview and you can sign up for access today.
Access to these models will roll out progressively while in preview and usage will be rate-limited.
Join the discussion and share feedback with us via Discussions.
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Now you can remediate existing security issues in your public repositories faster with Copilot Autofix for CodeQL alerts. Following our general availability release for all Advanced Security customers, Copilot Autofix for CodeQL alerts is now generally available (GA) for all public repositories, for free.
Powered by GitHub Copilot, this feature provides automatic fixes for vulnerabilities found by CodeQL, both on pull requests and for historical alerts that already exist in a codebase.
Importantly, you stay in full control of your codebase: Copilot Autofix will try and suggest fixes for CodeQL alerts in pull requests, but it’s ultimately up to you to decide whether you wish to accept Copilot’s suggestion wholly, partially, or not at all. The same applies to historical alerts in a codebase: you can request an autofix from Copilot, then review it, and decide whether you want to open a PR with the fix suggestion or commit straight to the affected branch (or neither).
Copilot Autofix is available for all public repositories that use code scanning CodeQL, and is enabled by default for alerts on PRs. It does not generate additional notifications. If you would like to enable Copilot Autofix on your organization’s private repositories, please have a look at this blog post where we announce Autofix for GitHub Advanced Security.
GitHub Copilot Extensions are now available in public beta 🚀 to all GitHub Copilot users and open for any developer or organization to create extensions. Alongside, we’re introducing a comprehensive Copilot Extensions Toolkit,
designed to equip developers by centralizing the information they need
to build quality extensions.
💡 What are Copilot Extensions and how to use them
Copilot Extensions integrate with your favorite dev tools directly into Copilot Chat across Visual Studio, VS Code, and GitHub.com (with support for JetBrains IDE coming soon!). Interact with databases, testing frameworks, deployment tools, and more — all without leaving your flow. For example:
– Docker’s extension can help you generate the right Docker assets for your project
– New Relic’s extension can help instrument your system and onboard with New Relic from within your editor
Additionally, enterprises and organizations have the ability to build private extensions. Copilot can interact with context from your internal developer tooling, execute workflows, and adhere to your organization’s best practices.
🏁 Getting Started
To use extensions
– If you have access to Copilot through a Copilot Business or Copilot Enterprise subscription, an organization or enterprise owner needs to enable the Copilot Extensions policy for your organization or enterprise.
– Visit the GitHub Marketplace to install extensions.
– Get started with our documentation and start using extensions in Copilot Chat in GitHub.com or in the VS Code and Visual Studio editors.
To build extensions
– Access our documentation and Copilot Extensions Toolkit for tutorials and tools
– Develop your extension, and decide whether you want to keep it private to your organization or submit it to the GitHub Marketplace.
– VS Code extension developers can also add Copilot functionality to their existing VS Code extensions. Learn more here.
Share your experiences to help us improve the platform!
– Join the discussion within the GitHub Community.
– To share feedback on specific extensions, let us know in our Copilot Extensions feedback hub.
– If you’re building extensions, fill out the Extension Developer Survey for detailed feedback and feature requests.
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You can now interact with GitHub Copilot directly within your active code file with Inline Chat for GitHub Copilot in JetBrains! This new feature is designed to enhance your coding experience by integrating interactive assistance directly within your code editor.
To start using it, ensure you have the GitHub Copilot plugin version 1.5.21.6667 or above installed in your JetBrains IDEs.
How to get started?
Open Your File: Begin by opening the file you want to work on.
Place Your Cursor: Position your cursor on the specific line or code block you want to discuss.
Use the Shortcut: To access GitHub Copilot’s inline chat feature, press Shift+Ctrl+I (Mac) or Shift+Ctrl+G (Windows). Alternatively, right-click and choose “GitHub Copilot > Copilot: Inline Chat”. You can also simply click on the Copilot icon that appears when you select a line or section of code
How Inline Chat enhances your coding experience
Enhanced Workflow: Keep your focus on coding while receiving suggestions directly within the editor.
Contextual Awareness: Provide Copilot with specific code snippets for more relevant recommendations.
Focused Interaction: Enjoy a streamlined experience without the need for frequent context switching.
When to use Inline Chat
Refactoring: Request alternative methods to achieve the same functionality with cleaner, more maintainable code.
Testing: Get help generating unit tests for specific sections of your code.
Code Improvement: Seek assistance with restructuring complex logic, renaming variables, or adding comments for better readability.
Vulnerability Assessment: Consult Copilot about potential vulnerabilities, but remember to use established security tools for a comprehensive evaluation.
Performance Optimization: Obtain suggestions for improving your code’s efficiency.
How Inline Chat differs from Side Panel Chat
While both Inline Chat and Side Panel Chat allow interaction with Copilot, Inline Chat provides a more focused experience by integrating conversations directly with your active file. The Side Panel Chat, on the other hand, offers a dedicated space for broader discussions and tracking past interactions.
Start leveraging the power of Inline Chat in JetBrains Copilot today and make your coding experience more seamless and efficient!
Copilot Chat in GitHub.com is now trained on common support scenarios and GitHub’s documentation to provide you the most up to date context to help you resolve common issues that may arise when using GitHub.
Here are some examples of questions you can now ask:
– Can I use Copilot knowledge bases with Copilot Individual?
– How do I configure SSH?
– A job is stuck in a post-build clean up step and it refuses to cancel or timeout. How do I stop it?
Since last month’s upgrade to GPT-4o, we now increased the available Chat context, so you can reference larger files and have longer chat conversations with GitHub Copilot Chat in VS Code. Additionally, you can now click Attach Context in Inline and Quick Chat to add more relevant context to your queries.
This month’s release also brings the following improvements to Copilot Chat in VS Code:
Easily generate tests using the Generate Tests using Copilot action or the /tests slash command. Copilot will now update and append tests to existing files or create a new test file if none exists. Learn more.
Revisit previous chat sessions with the Show Chats button. Sessions now have AI-generated names and can be manually renamed. Entries are sorted by the date of the last request and grouped by date buckets. Learn more.
Provide specifics on unsatisfactory Chat responses by selecting the Thumbs down button. A dropdown with detailed options helps you pick a problem type or report it as an issue to us, helping us improve Copilot. Learn more.
Code Actions now have clearer names: Generate Tests using Copilot and Generate Documentation using Copilot. Just place the cursor on an identifier and choose the action. Learn more.
Experimental New Features
Experimental settings are available in VS Code to gather your feedback and influence the future development of Copilot. Share your thoughts in our issues.
Define instructions for code generation (e.g., always use underscore for private field names) in settings or import from a file. Edit them in github.copilot.chat.experimental.codeGeneration.instructions, either as a user setting for yourself or as a workspace setting for your team. Learn more.
You can now use Copilot Chat in GitHub.com to search across GitHub to find and learn more about GitHub Advanced Security Alerts from code scanning, secret scanning, and Dependabot. This change helps you to better understand and seamlessly fix security alerts in your pull request. ✨
Try it yourself by asking questions like:
– How would I fix this alert?
– How many alerts do I have on this PR?
– What class is this code scanning alert referencing?
– What library is affected by this Dependabot alert?
– What security alerts do I have in this repository?
With this change, you can now use natural language within Copilot Chat in GitHub.com to search across GitHub to find commits, issues, pull requests, repositories, and topics.
Try it yourself:
– What are the most recent issues assigned to me?
– What repos are related to [insert topic]?
– What is the most recent PR from @user?
We’ve also made some changes under the hood to make Copilot more efficient with how it stores conversation histories. This means that Copilot can now remember more of the history of your conversation which should result in more informed and reliable responses ✨.
Custom models for GitHub Copilot are now available in Limited Public Beta for Copilot Enterprise. This new capability lets you fine-tune Copilot to better understand and align with your organization’s unique coding practices, improving the relevance and accuracy of code suggestions across your projects.
What are custom models?
Custom models are large language models (LLMs) that have been fine-tuned using your organization’s codebases. By training a model on your proprietary libraries, specialized languages, and internal coding patterns, Copilot delivers code suggestions that are more context-aware and tailored to your organization’s needs.
During this beta, you can create a custom model using your GitHub repositories. Optionally, you may also enable the collection of code snippets and telemetry from developers’ Copilot prompts and responses to further fine-tune the model. This process closely aligns Copilot’s suggestions with your coding practices, making them more relevant and accurate. As a result, your development teams will spend less time on code reviews, debugging, and manual code adjustments, ultimately boosting team productivity and ensuring more consistent code quality.
Importantly, your data remains entirely yours. It is never used to train another customer’s model, and your custom model is kept private, ensuring full control, security, and privacy.
When to Use Custom Models
Custom models enable you to make Copilot’s suggestions more relevant to your specific needs, which can lead to higher acceptance rates of the code suggested by Copilot among your developers. Consider using custom models in the following scenarios:
Enhance Library and API Usage: When your organization relies heavily on custom libraries or APIs that aren’t well-represented in public datasets, a custom model can prioritize these in its suggestions, making it easier for your developers to follow internal standards.
Improve Support for Specialized Languages: If your team works with less common or proprietary languages, custom models can make Copilot much more effective. Fine-tuning helps Copilot understand these languages better, reducing friction and improving productivity.
Adapt to Evolving Codebases: As your codebase changes, you have full control over when and how often to retrain your custom model. By regularly retraining, you can ensure that Copilot keeps up with the latest coding patterns, so it continues to provide relevant and accurate suggestions.
How to Get Started
Sign Up for the Beta: Sign up here to participate in the Limited Public Beta and make sure your organization is on the Copilot Enterprise plan.
Prepare Your Repositories:
Choose the repositories that best reflect your organization’s coding standards. Include those with proprietary libraries, specialized languages, or key internal frameworks to get the most out of fine-tuning. If your enterprise has multiple GitHub organizations, note that only one organization and its repositories can be used for training during this beta.
Enable Telemetry Collection:
To further customize your model, consider enabling the collection of code snippets and telemetry related to developers’ prompts and Copilot’s suggestions. This data will be securely collected and used for additional fine-tuning, improving the accuracy and relevance of Copilot’s output for your team. Your data will only be used to enhance your custom model and will not be shared with others. For more details about our data-handling practices, please visit the Trust & Security Center or review GitHub’s data protection agreement.
Training and Usage:
After setup, your custom model will be trained using the selected repositories. Once it’s ready, your developers’ IDEs will automatically start using the custom model, which will inform all in-line code completions.
Monitoring & Quality Assessment:
Regularly retrain your custom model to keep it aligned with new code and evolving practices. Use the Copilot Usage Metrics API to track metrics like suggestion acceptance rates and see how much it’s improving.
You can now exclude non-Git files from being accessed by Copilot, in addition to Git files. This update gives you greater control over the content Copilot can access, ensuring that it will not access files that an organization owner has marked for exclusion, whether the files are part of a Git repository or not.
How to exclude non-Git files
The wildcard scope has expanded to include both files within and outside Git repositories, supporting the exclusion of non-Git files.
Previously
Wildcard rules applied exclusively to files within the Git repository. For example:
"*":
- /test1 # => Blocks from the root of all git repositories: `/test1`
Now
Wildcard rules apply to files within the Git repository and the filesystem root. For example:
"*":
- /test1 # => Blocks from the root of all git repositories AND the filesystem root: `/test1`, `/test1`
Note: These changes to our Content Exclusion beta apply to the latest versions of both the VS Code and JetBrains Copilot extensions, covering the code completions and chat features in each.
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