Assessing, Comparing, and Combining State Machine-Based Testing and Structural Testing: A Series of Experiments
Bibliographic record
Abstract
A large number of research works have addressed the importance of models in software engineering. However, the adoption of model-based techniques in software organizations is limited since these models are perceived to be expensive and not necessarily cost-effective. Focusing on model-based testing, this paper reports on a series of controlled experiments. It investigates the impact of state machine testing on fault detection in class clusters and its cost when compared with structural testing. Based on previous work showing this is a good compromise in terms of cost and effectiveness, this paper focuses on a specific state-based technique: the round-trip paths coverage criterion. Round-trip paths testing is compared to structural testing, and it is investigated whether they are complementary. Results show that even when a state machine models the behavior of the cluster under test as accurately as possible, no significant difference between the fault detection effectiveness of the two test strategies is observed, while the two test strategies are significantly more effective when combined by augmenting state machine testing with structural testing. A qualitative analysis also investigates the reasons why test techniques do not detect certain faults and how the cost of state machine testing can be brought down.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".