This model captures four dimensions of a development State: Intention, Action, Supporting Tool, and Emotion, and identifies three key patterns that carry implications for training developers and designing context-aware, multi-modal AI assistants.
Abstract
AI assistants are changing software development, yet developers' thoughts and feelings during programming remain underexplored. To explore how hidden intentions, actions, tool choices, and emotions unfold during AI-assisted tasks, we conducted a mixed-methods study with 76 developers and propose the S-IASE model. This model captures four dimensions of a development State: Intention, Action, Supporting Tool, and Emotion. Through sequential pattern mining and interviews, we identified three key patterns. First, a "Trust but Verify" workflow reordered steps in the traditional programming paradigm. Second, developers exhibited stable emotional patterns yet revealed underlying self-criticism. Finally, text-only AI responses created a modality mismatch for procedural tasks. These patterns carry implications for training developers and designing context-aware, multi-modal AI assistants.
The findings identify the developer's processing architecture as a variable the conceptual modeling tradition needs to account for in order to account for specification quality in AI development platforms.
This article argues that adaptation is imperative to defend the profession of junior software developers across three stakeholders: the junior software developers themselves, computer science educators, and employing organizations.
Initial evidence of the existence and variation of Perspective Switching is provided and its importance in the context of AI-assisted software development, where the human role shifts from code production to critical evaluation and validation, is discussed.
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