Transforming workflow models into automated end-to-end acceptance test cases
Bibliographic record
Abstract
The User Requirements Notation is a standard published by the International Telecommunication Union that contains two complementary notations for goal and scenario/workflow modeling. Use Case Maps (UCM) - the workflow notation - focuses on the causal relationships of the steps in a workflow without requiring the specification of detailed message exchanges and data. A UCM model captures the interactions between actors and the system and typically integrates several use cases into a combined system view. This results in a high-level description of the system and its end-to-end usage scenarios. At the UCM level, scenario definitions create a regression test suite for the UCM model. This paper investigates the transformation of such workflow models into end-to-end acceptance test cases that can be automated with the JUnit testing framework. For that purpose, the UCM model is enriched with (i) input data types and expected results, (ii) a code-level description of system behavior as needed for the workflow, and (iii) testing logic including assertions. Based on this specification, the proposed approach uses boundary value analysis of the input data and Myer's test selection heuristics to determine a set of test cases for the described workflow. Coverage criteria may be specified at the UCM model level. Results from a case study of a small data management system indicate a reduction of the number of lines of code that need to be specified in the workflow model vs. the test implementation by an order of magnitude.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".