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.
VeriBugBench: An Empirically Grounded Framework for Constructing Verilog RTL Debugging Benchmarks
School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University（上海理工大学计算机与信息工程学院人工智能研究院）；Guangzhou College of Commerce（广州商学院）；Chongqing University（重庆大学）；National University of Defense Technology（国防科技大学）；Academy of Military Sciences（军事科学院）
Exploring Dependence, Overreliance, and Addiction Related Behaviors Associated with Large Language Model Use Among Software Engineers
University of Calgary（卡尔加里大学）；University of Hawaii at Manoa（夏威夷大学马诺阿分校）；Universidade Federal Rural de Pernambuco (UFRPE)（伯南布哥联邦农村大学）；Radboud University（拉德堡德大学）
Staying or Leaving? How Job Satisfaction, Embeddedness and Antecedents Predict Turnover Intentions of Software Professionals
Dalhousie University（达尔豪斯大学）；University of Helsinki（赫尔辛基大学）；Northwestern University（西北大学）；University of Calgary（卡尔加里大学）；Mahidol University（玛希隆大学）；New York Institute of Technology（纽约理工学院）；King Saud University（沙特国王大学）；NADI, University of Namur（南纳尔大学 NADI 研究所）；Shinshu University（信州大学）；Heidelberg University（海德堡大学）；University of Bayreuth（拜罗伊特大学）；Bilkent University（比尔肯特大学）
Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining
ByteDance Seed（字节跳动种子）；Fudan University（复旦大学）；M-A-P；ByteDance TikTok（字节跳动TikTok）；Shanghai Innovation Institute（上海创新研究所）；The Hong Kong University of Science and Technology（香港科技大学）
The Software Infrastructure Attitude Scale (SIAS): A Questionnaire Instrument for Measuring Professionals' Attitudes Toward Technical and Sociotechnical Infrastructure
Dalhousie University（达尔豪斯大学）；University of Helsinki（赫尔辛基大学）；Northwestern University（西北大学）；University of Calgary（卡尔加里大学）；Mahidol University（玛希隆大学）；New York Institute of Technology（纽约理工学院）；King Saud University（沙特国王大学）；NADI, University of Namur（纳穆尔大学 NADI）；Shinshu University（信州大学）；Heidelberg University（海德堡大学）；University of Bayreuth（拜罗伊特大学）；Bilkent University（比尔肯特大学）
What Characterizes a Software Leader? Identifying Leadership Practices from Practitioners Social Media
Atlantico Institute（大西洋研究所）；State University of Ceará（塞阿拉州立大学）；University of Molise（莫利塞大学）；Federal University of Cariri（卡里里联邦大学）；Federal Institute of Ceará（塞阿拉联邦学院）
