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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Developer Troubleshooting Experience Study - grounded theory coded interview data

Authors: Starr, Arty; Storey, Margaret;

Developer Troubleshooting Experience Study - grounded theory coded interview data

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)

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Keywords

Software/trends, Software development

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average