A case study using the round-trip strategy for state-based class testing
Bibliographic record
Abstract
A number of strategies have been proposed for state-based class testing. An important proposal made by Chow (1978), that was subsequently adapted by Binder (1999), consists in deriving test sequences covering all round-trip paths in a finite state machine (FSMs). Based on a number of (rather strong) assumptions, and for traditional FSMs, it can be demonstrated that all operation and transfer errors in the implementation can be uncovered. Through experimentation, this paper investigates this strategy when used in the context of UML statecharts. Based on a set of mutation operators proposed for object-oriented code we seed a significant number of faults in an implementation of a specific container class. We then investigate the effectiveness of four test teams at uncovering faults, based on the round-trip path strategy, and analyze the faults that seem to be difficult to detect. Our main conclusion is that the round-trip path strategy is reasonably effective at detecting faults (87% average as opposed to 69% for size-equivalent, random test cases) but that a significant number of faults can only exhibit a high detection probability by augmenting the round-trip strategy with a traditional black-box strategy such as category-partition testing. This increases the number of test cases to run -and therefore the cost of testing- and a cost-benefit analysis weighting the increase of testing effort and the likely gain in fault detection is necessary.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".