YAWL2DVE: An Automated Translator for Workflow Verification
Bibliographic record
Abstract
Workflow management systems (WfMSs) have gained increasing attention recently as an important technology to improve information system development in dynamic and distributed organizations. However the absence of verification facilities in most WfMSs causes the resulting implementation of large and complex workflow models to be at risk of undesirable runtime executions. This problem of design validation ensuring the correctness of the design at the earliest stage possible is a major challenge for any responsible system development process, and the activities intended for its solution occupy an ever increasing portion of the development cycle cost and time budgets. Model checking is a popular technique to systematically and automatically verify system properties, but it requires a substantial effort to convert the system design into a specific model checking program. In this paper, we present an automated translator (YAWL2DVE) which can convert a graphical workflow model into DVE, the input language of DiVinE. DiVinE is a distributed and parallel model checker, which can effectively handle the well known "state explosion problem" of this domain. We show the effectiveness of this translator with a case study on a real world health care workflow model.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".