Copilot Workspace is GitHub's take on AI-powered software engineering

Is the future of software development an AI-powered IDE? GitHub puts forward the idea.

At its annual GitHub Universe conference in San Francisco on Monday, GitHub announced Copilot Workspace, a development environment that uses what GitHub describes as “Copilot-powered agents” to help developers brainstorm, plan, build, test and execute natural language code.

Jonathan Carter, head of GitHub Next, GitHub's software R&D team, calls Workspace a bit of an evolution of GitHub's AI-powered coding assistant Copilot into a more general-purpose tool, building on recently introduced capabilities like Copilot Chat, which lets developers ask questions about natural language code.

“Through research, we've found that the biggest problem for developers on many tasks is getting started and, most importantly, knowing how to approach a problem. [coding] problem, knowing which files to edit, and knowing how to consider multiple solutions and their tradeoffs,” Carter said. “So we wanted to build an AI assistant that could meet developers at the start of an idea or task, reduce the activation energy required to get started, and then work with them to make the necessary changes across the core base. ”

At last count, Copilot had more than 1.8 million paying individual and 50,000 business customers. But Carter foresees a much larger base, attracted by feature extensions with broad appeal, such as Workspace.

“Because developers spend a lot of time working on [coding issues]we believe we can help developers every day through a 'thought partnership' with AI,” said Carter. “You can think of Copilot Workspace as a companion experience and development environment that complements existing tools and workflows and makes it possible to simplify a class of developer tasks… We believe there is a lot of value to be delivered in an AI-native developer environment that doesn't is.” not limited by existing workflows.”

There is certainly internal pressure to make Copilot profitable.

Second pilot loses an average of $20 per month per user, according to a report from the Wall Street Journal, with some customers charging GitHub as much as $80 per month. And the number of competing services continues to grow. There's Amazon's CodeWhisperer, which the company made available for free to individual developers late last year. There are also startups, such as Magic, Tabnine, Codegen and Laredo.

Given a GitHub repository or a specific bug within a repository, Workspace – powered by OpenAI's GPT-4 Turbo model – can create a plan to (try to) suppress the bug or implement a new feature, based on an understanding of the repository's comments. release answers and a larger codebase. Developers get suggested code for the bug fix or new feature, along with a list of the things they need to validate and test that code, plus controls to edit, save, refactor, or undo it.

Image credits: GitHub

The suggested code can be run directly in Workspace and shared with team members via an external link. Once in Workspace, those team members can refine and tinker with the code as they see fit.

Perhaps the most obvious way to launch Workspace is via the new “Open in Workspace” button to the left of issues and pull requests in GitHub repositories. Clicking on it will open a field describing the software engineering task to be completed in natural language, such as “Add documentation for the changes in this pull request,” which, once submitted, will be added to a list of 'sessions' within the new dedicated workspace view.

GitHub workspace

Image credits: GitHub

Workspace systematically executes requests step by step, builds a specification, generates a plan, and then executes that plan. Developers can dive into each of these steps to get a detailed view of the proposed code and changes, and delete, redo, or reorder the steps as necessary.

“If you ask a developer where they often get stuck on a new project, you'll often hear them say they know where to start,” Carter said. “Copilot Workspace removes that burden and gives developers a plan from which to start.”

GitHub workspace

Image credits: GitHub

Workspace enters technical preview on Monday, optimized for a range of devices, including mobile.

Importantly, because it is a preview, Workspace is not covered by GitHub's IP Disclaimer Policy, which promises to help with the legal costs of customers facing claims from third parties alleging that the AI-generated code they use infringes IP. (Generative AI models are notorious outbreaks their training datasets, and GPT-4 Turbo was trained in part on copyrighted code.)

GitHub says it has not yet determined how it will produce Workspace, but that it will use the preview to “learn more about the value it delivers and how developers use it.”

I think the most important question is: will Workspace solve the existential problems surrounding Copilot and other AI-powered coding tools?

An analysis of more than 150 million lines of code captured in project repositories in recent years by GitClear, the developer of the code analysis tool of the same name, found that Copilot resulted in more incorrect code it's being pushed to codebases and more code is being re-added instead of reused and streamlined, creating headaches for code maintainers.

Elsewhere, security researchers have warned that Copilot and similar tools could strengthen existing bugs and security vulnerabilities in software projects. And Stanford researchers have found that developers accept suggestions from AI-powered coding assistants tend to produce less secure code. (GitHub emphasized to me that it uses an AI-based security system to block unsafe code, in addition to an optional code duplication filter to detect regurgitations of public code.)

Yet developers do not shy away from AI.

In a StackOverflow poll as of June 2023, 44% of developers said they are now using AI tools in their development process, and 26% plan to do so soon. Gartner predicts that 75% of enterprise software engineers will employ AI code assistants by 2028.

By emphasizing human review, Workspace may indeed help clean up some of the clutter caused by AI-generated code. We'll find out soon enough when Workspace gets into the hands of developers.

“Our primary goal with Copilot Workspace is to use AI to reduce complexity so developers can express their creativity and explore more freely,” said Carter. “We truly believe that the combination of humans and AI will always be superior to one or the other alone, and that is what we are focusing on with Copilot Workspace.”

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