A Grounded Theory of Debugging in Professional Software Engineering Practice

2025-09-09 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering · Depth: Expert, extended

Summary

A qualitative study by Michael Coblenz (2026) presents a grounded theory of debugging in professional software engineering, derived from observing seven developers and five live-coding streamers across 17 real-world debugging tasks. The research identifies debugging as a structured, iterative diagnostic process where programmers continuously update a mental model of the system. This process involves alternating navigation and execution strategies, utilizing both forward and backward tracing modes, and adapting approaches based on codebase complexity and familiarity. Developers also gather external resources and employ a "knowledge avoidance" strategy, learning just enough to resolve the issue, often relying on uncertainty and speculation rather than strict hypothesis testing. Mental model development consumes 57% of debugging time.

Key takeaway

For software engineers tackling complex bugs, your debugging strategy should focus on iteratively refining a "good-enough" mental model rather than striving for exhaustive system understanding. Embrace "knowledge avoidance" by learning just enough to progress, and systematically gather information through alternating navigation and execution, forward/backward tracing, and external resources like colleagues or AI tools. This adaptive approach will significantly improve your diagnostic efficiency.

Key insights

Professional debugging is an iterative, human-centered process of updating a "good-enough" mental model through adaptive information gathering.

Principles

• Debugging is an iterative mental model refinement.
• Prioritize "knowledge avoidance" for efficiency.
• Integrate socio-technical and external resources.

Method

Debugging follows four steps: initial bug reproduction, mental model development, fix development, and fix validation, with mental model development consuming the most time.

In practice

• Design tools to track uncertainty and exploration.
• Integrate socio-technical resources into debugging tools.
• Teach managing uncertainty in complex codebases.

Topics

• Debugging Strategies
• Software Engineering Research
• Mental Models
• Codebase Navigation
• AI in Debugging
• Socio-technical Systems

Best for: Software Engineer, Research Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.