Comparing transition trees test suites effectiveness for different mutation operators
Why this work is in the frame
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Bibliographic record
Abstract
Research demonstrated that faults seeded mutation using operators can be representative of faults in real systems. In this paper, we study the relationship between the different operators used to insert mutants in the fault domain of the system under test and the effectiveness of different state machine test suites at killing those mutants. We are particularly interested in the effectiveness of two interrelated state machine testing strategies at finding different types of faults. Those are the round-trip paths strategy and the transition tree strategy. Using empirical evaluation, we compare the effectiveness of more than two thousand unique test suites at killing mutants seeded using eight different mutation operators. We perform experiments on four experimental objects and provide qualitative analysis of the results. We conclude that neither of the two studied strategies is more effective than the other at killing a certain type of mutants. However, the structure of the finite state machine and the nature of the system under test affect the type of faults detected by the different testing strategies.
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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 | 0.000 |
| 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 it