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On Sun, 18 Aug, 8:00 AM UTC
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GitHub rolls out AI-powered fixes for code vulnerabilities
Copilot Autofix, a new addition to the GitHub Advanced Security service, analyzes vulnerabilities in code and offers code suggestions to help developers fix them. GitHub has unveiled Copilot Autofix, an AI-powered software vulnerability remediation service as part of its GitHub Advanced Security (GHAS) service. GitHub introduced Copilot Autofix in production on August 14. "Copilot Autofix analyzes vulnerabilities in code, explains why they matter, and offers code suggestions that help developers fix vulnerabilities as fast as they are found," GitHub said in the announcement. GHAS customers on GitHub Enterprise Cloud already have Copilot Autofix included in their subscription. GitHub has enabled Copilot Autofix by default for these customers in their GHAS code scanning settings. Beginning in September, Copilot Autofix will be offered for free in pull requests to open source projects. During the public beta, which began in March, GitHub found that developers using Copilot Autofix were fixing code vulnerabilities more than three times faster than those doing it manually, demonstrating how AI agents such as Copilot Autofix can radically simplify and accelerate software development. Copilot Autofix can be generated for dozens of classes of vulnerabilities, such as SQL injection and cross-site scripting, which developers can dismiss, edit, or commit in their pull request, the company said.
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GitHub previews AI-powered code scanning autofix
Code scanning autofix pairs GitHub's CodeQL code scanner with GitHub Copilot APIs to generate fix suggestions for discovered vulnerabilities. GitHub is previewing code scanning autofix, a feature that combines its GitHub Copilot AI assistant with its CodeQL code scanner to provide suggested fixes to discovered vulnerabilities. Code scanning autofix is available in a public beta to GitHub Advanced Security customers. Launched March 20, code scanning autofix makes vulnerability fixes available right away as a developer is coding, GitHub said. GitHub Copilot AI is used to provide a code suggestion and explanation directly in the pull request. Code scanning autofix covers more than 90% of alert types in JavaScript, TypeScript, Java, and Python, and remediates more than two-thirds of found vulnerabilities with little or no editing, according to the company. Code scanning autofix leverages the CodeQL engine and a combination of heuristics and GitHub Copilot APIs to generate code suggestions. The feature builds on the November 2023 unveiling of GitHub Application Security, which provides additional security features including code scanning, secrets scanning, auto-triage rules for security alerts, and dependency reviews. These features require a GitHub Advanced Security license to run on repositories apart from public repositories on GitHub.
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GitHub has launched a new AI-powered feature to automatically fix code vulnerabilities. This tool aims to enhance security and streamline the development process for programmers.
GitHub, the popular code hosting and collaboration platform, has introduced a groundbreaking feature that leverages artificial intelligence to automatically fix code vulnerabilities. This new tool, called Code Scanning AutoFix, represents a significant leap forward in addressing security concerns in software development 1.
The AI-powered system analyzes code repositories for potential security issues and proposes fixes for identified vulnerabilities. When a security flaw is detected, the tool generates a pull request with the necessary code changes to address the problem. This automated process aims to streamline the security patching workflow and reduce the time developers spend on manual fixes 2.
Code Scanning AutoFix offers several advantages to the development community:
At present, the AI-powered fix generation is available for Python repositories and addresses a specific set of security issues, including SQL injection vulnerabilities, path traversal flaws, and missing input validation. GitHub plans to expand the tool's capabilities to cover more programming languages and a broader range of security concerns in the future 1.
Code Scanning AutoFix builds upon GitHub's existing security features, such as Dependabot for dependency management and CodeQL for code analysis. This integration creates a more comprehensive security ecosystem within the GitHub platform, enabling developers to address various aspects of code security efficiently 2.
The introduction of AI-powered code fixing tools like GitHub's Code Scanning AutoFix signals a shift in how developers approach security in their projects. As these technologies evolve, they have the potential to significantly reduce the time and effort required to maintain secure codebases, allowing development teams to focus more on innovation and feature development.
GitHub is gradually rolling out the Code Scanning AutoFix feature to its users. Initially, it will be available to a select group of developers and organizations, with plans for broader availability in the coming months. The company encourages feedback from early adopters to refine and improve the tool's capabilities 1.
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GitHub introduces new features for Copilot Autofix, integrating third-party tools to address security vulnerabilities more efficiently. This update aims to reduce security debt and streamline the development process.
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GitHub has unveiled a suite of new AI-powered features for its Copilot tool, including Agent Mode, Copilot Edits, and Project Padawan, aimed at enhancing developer productivity and automating complex coding tasks.
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GitHub introduces a free tier of its AI-powered coding assistant, Copilot, making it accessible to all developers using Visual Studio Code. This move aims to expand Copilot's reach and lower barriers for global developers.
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GitHub introduces Copilot, its AI-powered coding assistant, to Apple's Xcode IDE in a public preview, offering enhanced productivity and coding experience for Apple developers.
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GitHub announces a significant update to its AI coding assistant, Copilot, introducing multi-model support that allows developers to choose between AI models from Anthropic, Google, and OpenAI. This move aims to provide developers with more flexibility and options in their coding process.
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