State-Based Tests Suites Automatic Generation Tool (STAGE-1)
Bibliographic record
Abstract
State diagrams are widely used to model software artifacts, making state-based testing an interesting research topic. When conducting research on state-based testing for evaluating different testing criteria, often there is a need to devise numerous test suites in a systematic way according to selection criteria such as all-edges, all-transition-pairs, or the transition tree (W-method). Moreover, one also needs to satisfy each criterion in as many ways as possible to account for possible stochastic phenomena within each criterion. The main issue is then: how to automate the generation of as many, or even all, the different test suites for each criterion? This paper presents the first part of a framework, an automation tool chain that generates test trees from a state machine diagram, extracts test cases from the generated trees, and composes a test suite from each generated tree. This tool is the first to generate all possible distinctive trees using depth and breadth first graph traversal algorithms. The tool chain should be of interest to researchers in state-based testing as well as practitioners who are interested in alternative adequate test suites especially for comparing the effectiveness of the different test suites satisfying one criterion and the effectiveness of the other different criteria.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".