Transforming Automatically BPMN Models to Smart Contracts with Nested Trade Transactions (TABS+)
Bibliographic record
Abstract
Development of blockchain smart contracts is more difficult than mainstream software development, because the underlying blockchain infrastructure poses additional complexity. To ease the developer's task of writing smart contract, we use Business Process Model and Notation (BPMN) modeling to describe application requirements for trade of goods and services and then transform automatically the BPMN model into the methods of a smart contract. In our previous research, we described our approach and a tool to Transform Automatically BPMN models into Smart contracts (TABS). In this article, we describe how the TABS approach is augmented with the support for a BPMN trade transaction that is a collaboration by several actors. Our approach analyzes the BPMN model to determine which patterns in the BPMN model are suitable for use as trade transactions and show those patterns to the developer who decides which ones should be deployed as trade transactions. We describe how our approach automatically transforms the BPMN model into a smart contract that provides a transaction mechanism to enforce the transactional properties of the nested transactions. Our approach greatly reduces the developer's task as synchronization of collaborative activities is provided by our approach, so that the developer needs to code only isolated tasks with well-defined inputs and outputs. We also overview the TABS+ tool we built as a proof of concept to show that our approach is feasible, and we provide estimates on the cost of supporting the nested trade transactions.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".