On the Effect of Counters in Guard Conditions When State-Based Multi-objective Testing
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Bibliographic record
Abstract
During test case generation from an extended finite state machine (EFSM), the counter problem is caused by the presence of guard conditions that refer to counter variables. Because such variables are initialized and updated by transitions in the EFSM, every traversal of the state machine graph is not necessarily feasible, i.e., executable. The problem manifests itself by the fact that a transition, a sequence of transitions, or a more complex behavior in the state machine, has to be repeatedly triggered to eventually trigger a specific behavior (another transition). In this paper we define different manifestations of the counter problem and experiment with a new search based solution for that problem. We also investigate how the counter problem affects a multi-objective genetic algorithm that generates test suites from an EFSM. We evaluate our solution and compare it with an existing one, using three different case studies.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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