GitHub CoPilot speeds the transition from Windows Solutions to ARM

More than 25% of developers already use AI-assisted tools for tasks like code generation, test case creation, bug fixes, and security detection, which has significantly improved productivity. ARM’s extension has been released for GitHub Copilot, the world’s most widely deployed AI developer tool, making ARM-based technology more accessible and developer-friendly for millions of Copilot users.

In a recent report, Accenture found GitHub Copilot increases coding speed by 55% and developer confidence by 85%. With more than a million paid users across 20,000+ enterprises, Copilot users average a staggering 3.4 days of usage per week.

ARM designed the extension for GitHub Copilot to simplify migration to ARM architecture, reducing development time and costs. The extension is initially focused on servers and cloud environments, with plans to extend to use cases including mobile, IoT, software-defined vehicles and Windows on ARM development.

A complete CI/CD pipeline – featuring GitHub Actions, ARM Runners, and the ARM extension for GitHub Copilot – is fully available on ARM for free to developers everywhere. Simply call @ARM within GitHub Copilot Chat and gain access to curated code examples, best practices and migration strategies, and performance optimization tips designed for ARM-based development.

Rinat Shagisultanov, VP of Technology, Modern Dev & DevOps Services, at InfoMagnus, pointed out that developers “Can now take advantage of ARM intrinsics and ARM architectural features to make their software more performant and efficient. The extension will make it easier than ever for our customers to migrate their workloads to the lower-cost, power-efficient ARM servers we are seeing everywhere in the cloud.”

ARM intends to continue to update the intelligence platform with ARM-curated data and best practices supporting a wide range of applications including:

• Generative AI: Simplify the creation and deployment of AI applications.
• Mobile and Gaming: Optimize apps for performance on Arm-based platforms.
• IoT and Software-Defined Vehicles: Seamless workflows for cloud-to-edge and cloud-to-car deployment.

As Edge solutions become more prevalent, the need to reduce latency is critical for AIoT.