Towards workflow verification
Bibliographic record
Abstract
Workflow Management Systems (WfMS) that help the design and deployment of automated business processes as well as aid their execution and monitoring continue to evolve. Many WfMS use Workflow Patterns as their basic modeling constructs; however, the absence of verification facilities in most WfMS causes the resulting implementation to be at risk of undesirable runtime executions. Model Checking can facilitate the verification of workflow models, provided that we can conveniently implement the workflow model and provide the resources to handle the space requirement of the model. DiVinE is a distributed and parallel Model Checker that can effectively handle the well-known state explosion problem of this domain. In this paper, we present a translation of a collection of established Workflow Patterns into DVE, the input specification language of DiVinE. Thus, by assembling the corresponding DVE translated patterns into a whole model, we can verify properties of workflow models. We discuss the difficulties we have experienced with this approach and explain how that led to the development of an automatic translator tool from YAWL to DVE. We present two case studies and some ongoing work in our research group.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".