When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
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AI powered code-recommendation systems, such as Copilot and CodeWhisperer, provide code suggestions inside a programmer's environment (e.g., an IDE) with the aim to improve their productivity. Since, in these scenarios, programmers accept and reject suggestions, ideally, such a system should use this feedback in furtherance of this goal. In this work we leverage prior data of programmers interacting with Copilot to develop interventions that can save programmer time. We propose a utility theory framework, which models this interaction with programmers and decides when and which suggestions to display. Our framework Conditional suggestion Display from Human Feedback (CDHF) is based on predictive models of programmer actions. Using data from 535 programmers we build models that predict the likelihood of suggestion acceptance. In a retrospective evaluation on real-world programming tasks solved with AI-assisted programming, we find that CDHF can achieve favorable tradeoffs. Our findings show the promise of integrating human feedback to improve interaction with large language models in scenarios such as programming and possibly writing tasks.
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