On FSM-Based Testing: An Empirical Study: Complete Round-Trip Versus Transition Trees
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
Finite state machines being intuitively understandable and suitable for modeling in many domains, they are adopted by many software designers. Therefore, testing systems that are modeled with state machines has received genuine attention. Among the studied testing strategies are complete round-trip paths and transition trees that cover round-trip paths in a piece wise manner. We present an empirical study that aims at comparing the effectiveness of the complete round-trip paths test suites to the transition trees test suites in one hand, and comparing the effectiveness of the different techniques used to generate transition trees (breadth first traversal, depth first traversal, and random traversal) on the other hand. We also compare the effectiveness of all the testing trees generated using each single traversal criterion. This is done through conducting an empirical evaluation using four case studies from different domains. Effectiveness is evaluated with mutants. Experimental results are presented and analyzed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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