Interaction Models Matter in the Evaluation of Quality of Conceptual Models
Bibliographic record
Abstract
Conceptual models are key artefacts in software production processes that are based on MDD technology. These conceptual models are used as inputs in the process of code generation. Therefore, it is very important to be able to evaluate the quality of the models in order to improve the quality of the corresponding final applications. The development of an effective quality assurance technique requires knowing what kind of defects may occur in practice in the conceptual models used in MDD approaches. Conventional Conceptual Modeling approaches focus on the detection of defects that comes from either the data perspective or the process perspective. However, the interaction perspective also matters! This paper presents a list of technical defects that can be identified when performing the interaction modeling of an MDD environment. This list of defects provides an initial approach to evaluate the completeness of Interaction Models with respect to their use for the automatic generation of a final application. This paper also presents an example that illustrates how the completeness of an Interaction Model can be evaluated through defect detection.
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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 | 0.060 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".