The tool tracks the code and comments (descriptions or notes written in natural language) in the file that a programmer is working on, as well as other files that it links to or that have been edited in the same project, and sends all this text to the large language model behind Copilot as a prompt. (GitHub co-developed Copilot’s model, called Codex, with OpenAI. It is a large language model fine-tuned on code.) Copilot then predicts what the programmer is trying to do and suggests code to do it. This round trip between code and Codex happens multiple times a second, the prompt updating as the programmer types. At any moment, the programmer can accept what Copilot suggests by hitting the tab key, or ignore it and carry on typing. The tab button seems to get hit a lot. A study of almost a million Copilot users published by GitHub and the consulting firm Keystone Strategy in June — a year after the tool’s general release — found that programmers accepted on average around 30% of its suggestions, according to GitHub’s user data.
[…] Copilot has changed the basic skills of coding. As with ChatGPT or image makers like Stable Diffusion, the tool’s output is often not exactly what’s wanted — but it can be close. “Maybe it’s correct, maybe it’s not — but it’s a good start,” says Arghavan Moradi Dakhel, a researcher at Polytechnique Montreal in Canada who studies the use of machine-learning tools in software development. Programming becomes prompting: rather than coming up with code from scratch, the work involves tweaking half-formed code and nudging a large language model to produce something more on point.
