Unveiling Developers' Mindset Barriers to Software Modeling Adoption
Bibliographic record
Abstract
This paper presents a qualitative empirical study that seeks to delve into and illuminate the mindset barriers confronted by software developers that hinder the acceptance and use of software modeling techniques. Although software modeling provides acknowledged advantages, such as improving quality, facilitating superior comprehension of system behavior, and enhancing stakeholder communication, it remains significantly underutilized among developers. The primary objective of this research is to understand these barriers not as a result of just technological challenges, but rather as deeply-rooted mindset barriers entrenched within the developer community. Utilizing a grounded theory approach, we conducted extensive interviews with a broad range of developers from various backgrounds. The data were analyzed to identify common themes and patterns. The findings provide insights into the developers' perspectives, revealing key mindset barriers such as tooling and support concerns, challenges posed by modern work paradigms, behavioral resistance, lack of perceived value, knowledge deficiency and skill deficiency. The results of this study serve as a stepping stone towards developing strategies to overcome these mindset barriers, with the aim of fostering a wider acceptance of software modeling techniques. The study contributes to our understanding of the human factors at play in the broader software engineering field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".