UNDERSTANDING THE ADOPTION OF USE CASE NARRATIVES IN THE UNIFIED MODELING LANGUAGE
Bibliographic record
Abstract
This research examines the adoption of Use Case Narratives within the Unified Modeling Language (UML).Using the Technology Acceptance Model (TAM) as a framework, practitioners with UML experience were asked questions to measure their Perceived Ease of Use and Perceived Usefulness of Use Case Narratives and their Intentions to Adopt them. We extend Perceived Usefulness in the context of UML adoption to address the question “usefulness for what purpose(s)?” Generally, we find that TAM explains Use Case Narrative acceptance. More importantly, we find that Perceived Usefulness is explained by usefulness for specific software development tasks. This research provides three main contributions, beginning with an improved understanding of the role of Use Case Narratives in UML projects. Second, the study extends TAM by explaining how a technology is used rather than simply whether it is used. Third, this study provides a framework for future studies into other UML diagrams.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".