Review is the control point for AI-authored code: teams set the sign of AI’s effect through reviewer expertise and process design, not the technology itself.
A Grounded Theory of Debugging in Professional Software Engineering Practice
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abstract
Debugging is a central yet complex activity in software engineering. Prior studies have documented debugging strategies and tool usage, but little theory explains how experienced developers reason about bugs in large, real-world codebases. We conducted a qualitative study using a grounded theory approach. We observed seven professional developers and five professional live-coding streamers working on 17 debugging tasks in their own codebases, capturing diverse contexts of debugging. We theorize debugging as a structured, iterative diagnostic process in which programmers update a mental model of the system to guide information gathering. Developers gather information by alternating between navigation and execution strategies, employing forward and backward tracing modes of reasoning and adapting these approaches according to codebase context, complexity, and familiarity. Developers also gather external resources to complement code-based evidence, with their experience enabling them to systematically construct a mental model. We contribute a grounded theory of professional debugging that surfaces the human-centered dimensions of the practice, with implications for tool design and software engineering education.
years
2026 3representative citing papers
Targeted, evidence-rich context partitions improve causal clarity and actionability of LLM failure explanations while large undifferentiated contexts produce vaguer outputs, with higher-quality explanations correlating to better downstream repair rates.
A controlled user study with 24 programmers shows sketch-based pen input can handle breakpoint setting, step execution, and state inspection in debugging, though precision, recognition, and recall remain challenges.
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