Bibliographic record
Abstract
Mutation analysis is extensively used for the comparison of state-based testing methods that work from a finite state machine (FSM); It consists in seeding faults in the FSM model using mutation operators, one fault at a time, executing already constructed test suites on the correct FSM and the mutated FSMs and comparing executions to identify proportions of mutants revealed by test suites, a.k.a. the mutation score. Although a common experimental practice, there is still a lot to discover about FSM mutation operators so that we can adequately rely on experimental results that employ them, starting with the identification of a common set of operators. Indeed, published results that rely on FSM mutants employ a varied, incomplete, and sometimes ill-defined set of operators, making comparisons of empirical results difficult. In this paper, we report on our effort to coalesce a complete set of precisely defined operators. The paper also illustrates, with the W method for test case construction, and several real-world FSMs, how the operators can be used.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".