An experimental scrutiny of visual design modelling: VCL up against UML+OCL
Bibliographic record
Abstract
Abstract The graphical nature of prominent modelling notations, such as the standards UML and SysML, enables them to tap into the cognitive benefits of diagrams. However, these notations hardly exploit the cognitive potential of diagrams and are only partially graphical with invariants and operations being expressed textually. The Visual Contract Language (VCL) aims at improving visual modelling; it tries to (a) maximise diagrammatic cognitive effectiveness, (b) increase visual expressivity, and (c) level of rigour and formality. It is an alternative to UML that does largely pictorially what is traditionally done textually. The paper presents the results of a controlled experiment carried out four times in different academic settings and involving 43 participants, which compares VCL against UML and OCL and whose goal is to provide insight on benefits and limitations of visual modelling. The paper’s hypotheses are evaluated using a crossover design with the following tasks: (i) modelling of state space, invariants and operations, (ii) comprehension of modelled problem, (iii) detection of model defects and (iv) comprehension of a given model. Although visual approaches have been used and advocated for decades, this is the first empirical investigation looking into the effects of graphical expression of invariants and operations on modelling and model usage tasks. Results suggest VCL benefits in defect detection, model comprehension, and modelling of operations, providing some empirical evidence on the benefits of graphical software design.
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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 | 0.021 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".