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arXiv:2206.15000cs.HC

Grounded Copilot: How Programmers Interact with Code-Generating Models

Shraddha Barke, Michael B. James, Nadia Polikarpova

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

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--with a range of prior experience using the assistant--as they solve diverse programming tasks across four languages. Our main finding is that interactions with programming assistants are bimodal: in acceleration mode, the programmer knows what to do next and uses Copilot to get there faster; in exploration mode, the programmer is unsure how to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations for improving the usability of future AI programming assistants.

arXiv comments
Journal reference
DOI
Subjects
cs.HCHuman-Computer Interaction cs.PLProgramming Languages

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Jan 2023 to Jan 2023. Found in 1 HN submission from Jan 2023 to Jan 2023. Open submission discussions.

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  1. It's plausible, but is it true? 1 comment

    • It's an anecdote which is meant to illustrate a widely observed behavior among many humans: we're better at accounting for frequent errors than we are at accounting for rare ones. This is well established in the field of human reliability analysis (HRA). Take a look at the HRA…

      elil17View on HNParent context

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