Developer Troubleshooting Experience Study - grounded theory coded interview data
Bibliographic record
Abstract
This repository contains a dataset from a research study on developer troubleshooting experiences conducted by researchers at University of Victoria, used to construct a Theory of Troubleshooting as the developer's cognitive experience of overcoming confusion. As a central research question, we asked “What is the developer thinking, feeling, and striving for during the experience of troubleshooting?" We define troubleshooting as the cognitive problem-solving process of identifying, understanding, and constructing a mental model of the cause of an unexpected system behavior, and consider troubleshooting (cognitive process) to be an integral part of the activity of debugging. The study included 27 semi-structured interviews asking software developers to reflect on their experiences of troubleshooting, talking through both specific experiences and general impressions, both individually and collaboratively. We used a Constructivist Grounded Theory (CGT) approach to the analysis, reviewing the interview transcripts line by line, interpreting what is happening in the developer's experience, creating initial grounded codes that are low-level and interpretive, then sorting and grouping to raise the abstraction level with higher-level focus codes and connecting to theoretical categories. After a broader analysis of the data, we narrowed our focus to eight theoretical categories centered around the developer's experience of overcoming confusion: Confusion Experience Trouble in the Creation Process Trying to Gain Clarity Poking and Seeing Elucidating the Problem Frustration vs Confidence Experiential Intuition Figuring It Out The dataset includes: Eight theoretical category reports (prefixed "category_report_") that include 681 initial grounded codes and corresponding participant numbers across all 27 interviews, that we used to construct our theoretical models. For example, the category_report_confusion_experience.csv includes 117 examples of experiences related to confusion. 16 emerging question reports (prefixed "rq_") that includes a broad set of 1032 initial grounded codes sorted by emerging question with corresponding participant numbers across the first 12 interviews before we reached theoretical saturation and narrowed our theoretical focus. A summary of emerging questions and Miro board links by emerging question which includes the 1032 initial grounded codes sorted and grouped into higher level focus codes (Miro_boards_per_emerging_question.pdf) The developer interview protocol that generated the dataset (dev_interview_protocol.pdf) The developer follow up interview protocol that we used to validate and refine the models and test for resonance, showing an early version of the model diagrams prior to refinement (dev_interview_protocol_followup.pdf) Demographics data by participant (demographics.csv), with gender summarized for anonymity (the participants include 8 women, 1 non-binary, and 18 men)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".