Decision-Oriented Programming with Aporia

TL;DR

This paper introduces decision-oriented programming (DOP), a paradigm that makes design decisions explicit and traceable in AI-assisted coding, exemplified by the Aporia tool which enhances programmer engagement and understanding.

Contribution

The paper proposes DOP as a new programming paradigm emphasizing explicit, co-authored decisions, and presents Aporia, a tool that implements this approach to improve developer insight and control.

Findings

Aporia increased programmer engagement in design.

Participants' mental models were 5x more aligned with code.

Aporia facilitated exploration and validation during coding.

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.