YAWL2DVE: An Automated Translator for Workflow Verification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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 it