State-Based Tests Suites Automatic Generation Tool (STAGE-1)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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