Finite State Machine Testing Complete Round-Trip Versus Transition Trees: On the Road of Finding the Most Effective Criterion
Bibliographic record
Abstract
Most software systems can be modeled either fully or partially using finite state machines. For this reason, many testing criteria for finite state machine models have been proposed and discussed by the research community. Among the studied testing criteria are complete round-trip paths and transition trees that cover round-trip paths in a piece wise manner. The theoretical comparison between the different proposed criteria does not provide enough evidence of effectiveness. Hence, empirical evaluation is needed to compare the criteria. In my thesis, I conduct many empirical experiments that aim 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. I also compare the effectiveness of all the testing trees generated using each single traversal criterion. Analyzing the experimental results lead to more than one hypothesis about the characteristics of the most effective among the evaluated test suites. The experimental results do not show consistent trends related to the suggested hypotheses. However, more case studies and more intuitions are to be tested to find a more effective criterion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".