Transforming workflow models into automated end-to-end acceptance test cases
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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