Improving the coverage criteria of UML state machines using data flow analysis
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Bibliographic record
Abstract
Abstract A number of coverage criteria have been proposed for testing classes and class clusters modeled with state machines. Previous research has revealed their limitations in terms of their capability to detect faults. As these criteria can be considered to execute the control flow structure of the state machine, we are investigating how data flow information can be used to improve them in the context of UML state machines. More specifically, we investigate how such data flow analysis can be used to further refine the selection of a cost‐effective test suite among alternative, adequate test suites for a given state machine criterion. This paper presents a comprehensive methodology to perform data flow analysis of UML state machines—with a specific focus on identifying the data flow from OCL guard conditions and operation contracts—and applies it to a widely referenced coverage criterion, the round‐trip path (transition tree) criterion. It reports on two case studies whose results show that data flow information can be used to select the best transition tree, in terms of cost effectiveness, when more than one satisfies the transition tree criterion. The results also suggest that different trees are complementary in terms of the data flow that they exercise, thus, leading to the detection of intersecting but distinct subsets of faults. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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