Decision-Oriented Programming with Aporia
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910107344830464 |
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| author | Kasibatla, Saketh Ram Rothkopf, Raven Peleg, Hila Pierce, Benjamin C. Lerner, Sorin Goldstein, Harrison Polikarpova, Nadia |
| author_facet | Kasibatla, Saketh Ram Rothkopf, Raven Peleg, Hila Pierce, Benjamin C. Lerner, Sorin Goldstein, Harrison Polikarpova, Nadia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05203 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Decision-Oriented Programming with Aporia Kasibatla, Saketh Ram Rothkopf, Raven Peleg, Hila Pierce, Benjamin C. Lerner, Sorin Goldstein, Harrison Polikarpova, Nadia Human-Computer Interaction 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. |
| title | Decision-Oriented Programming with Aporia |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.05203 |