Invisible Strings of Developer Programming Behavior with AI

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.