Improving Statechart Testing Criteria Using Data Flow Information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Empirical studies have shown there is wide variation in cost (e.g., of devising and executing test cases) and effectiveness (at finding faults) across existing state-based coverage criteria. As these criteria can be considered as executing the control flow structure of the statechart, we are attempting to investigate how data flow information can be used to improve their cost-effectiveness. This article presents a comprehensive methodology to perform data flow analysis of UML statecharts, applies it to the round-trip path (transition tree) coverage criterion and reports on two case studies. The results of the case studies show that dataflow information can be used to select the best cost-effective transition tree when more than one satisfies the transition tree criterion. We further propose a more optimal strategy for the transition tree criterion, in terms of cost and effectiveness. The improved tree strategy is evaluated through the two case studies and the results suggest that it is a cost-effective strategy that would fit into many practical situations
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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