Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change
\nthe face of programming forever. But what is this new face of programming? We present the first grounded
\ntheory analysis of how programmers interact with Copilot, based on observing 20 participants\u2014with a range of
\nprior experience using the assistant\u2014as they solve diverse programming tasks across four languages. Our main
\nfinding is that interactions with programming assistants are bimodal: in acceleration mode, the programmer
\nknows what to do next and uses Copilot to get there faster; in exploration mode, the programmer is unsure
\nhow to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations
\nfor improving the usability of future AI programming assistants.<\/p>\n","protected":false},"excerpt":{"rendered":"
Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change the face of programming forever. But what is this new face of programming? We present the first grounded theory analysis of how programmers interact with Copilot, based on observing 20 participants\u2014with a range of prior experience using the assistant\u2014as they 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