Last June, Microsoft-owned GitHub and OpenAI launched Copot, a service that provides suggestions for whole lines of code inside development environments like Microsoft Visual Studio. Avaable as a downloadable extension, Copot is powered by an AI model called Codex that's trained on blions of lines of public code to suggest additional lines of code and functions given the context of existing code. Copot can also surface an approach or solution in response to a description of what a developer wants to accomplish (e.g., “Say hello world”), drawing on its knowledge base and current context.
Copot was previously only avaable in technical preview. But after signaling that the tool would reach generally avaabity this summer, GitHub today announced that Copot is now avaable to all developers. As previously detaed, it’ll be free for students as well as “verified” open source contributors — starting with roughly 60,000 developers selected from the community and students in the GitHub Education program.
GitHub says that 1.2 mlion people signed up during the preview period. Copot is now suggesting 40% of newly written code, according to the company — up from 35% earlier this year.
“Over the past year, we've continued to iterate and test workflows to help drive the ‘magic’ of Copot,” Ryan J. Salva, VP of product at GitHub, told technewss via ema. “We not only used the preview to learn how people use GitHub Copot but also to scale the service safely.”
With Copot, developers can cycle through suggestions for Python, JavaScript, TypeScript, Ruby, Go and dozens of other programming languages and accept, reject or manually edit them. Copot adapts to the edits developers make, matching particular coding styles to autofl boerplate or repetitive code patterns and recommend unit tests that match implementation code.
Copot extensions are avaable for Noevim and JetBrains in addition to Visual Studio Code, or in the cloud on GitHub Codespaces.
One new feature coinciding with the general release of Copot is Copot Explain, which translates code into natural language descriptions. Described as a research project, the goal is to help novice developers or those working with an unfamiar codebase.
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“Whe it's clear that Copot helps developers complete tasks faster, we're continuing to explore updates that go beyond that by helping developers stay in the flow, focus on more satisfying work, and conserve mental energy even as they save time,” Salva said. “As an example of the impact we've observed, it's worth sharing early results from a study we are conducting. In the experiment, we are asking developers to write an HTTP server — half using Copot and half without. Preliminary data suggests that developers are not only more likely to complete their task when using Copot, but they also do it in roughly half the time.”
Owing to the complicated nature of AI models, Copot remains an imperfect system. GitHub said that it's implemented fters to block emas when shown in standard formats, and offensive words, and that it's in the process of buding a fter to help detect and suppress code that's repeated from public repositories. But the company acknowledges that Copot can produce insecure coding patterns, bugs and references to outdated APIs, or idioms reflecting the less-than-perfect code in its training data.
“This is just the beginning of AI-powered development tools, so it'll be exciting to see how developers use Copot over the next few months and years from now — and in tandem, how we advance the product,” Salva continued.