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arXiv · 2604.05203

Decision-Oriented Programming with Aporia

Saketh Ram Kasibatla·Raven Rothkopf·Hila Peleg·Benjamin C. Pierce·Sorin Lerner·Harrison Goldstein·Nadia Polikarpova
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Abstract

AI agents allow developers to express computational intent abstractly, reducing cognitive effort and helping achieve flow during programming. Increased abstraction, however, comes at a cost: developers cede decision-making authority to agents, often without realizing that important design decisions are being made without them. We aim to bring these decisions to the foreground in a paradigm we dub decision-oriented programming. In DOP, (1) decisions are explicit and structured, serving as the shared medium between the programmer and the agent; (2) decisions are co-authored interactively, with the agent proactively eliciting them from the programmer; and (3) each decision is traceable to code. As a step towards this vision, we have built Aporia, a design probe that tracks decisions in a persistent, editable Decision Bank; elicits them by asking programmers design questions; and encodes each decision as an executable test suite that can be used to validate the implementation. In a user study of 14 programmers, Aporia increased engagement in the design process and scaffolded both exploration and validation. Participants also gained a more accurate understanding of their implementations, with their mental models 5x less likely to disagree with the code than a baseline coding agent.

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Saketh Ram Kasibatla, Raven Rothkopf, Hila Peleg, Benjamin C. Pierce, Sorin Lerner, Harrison Goldstein, Nadia Polikarpova. 2026-04-06. Decision-Oriented Programming with Aporia. https://arxiv.org/abs/2604.05203

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