Bibliographic record
Abstract
This lab contains a set of experiments that compare two test sequence generation algorithms based on finite-state machine (FSM) specifications: a random greedy algorithm, which is used as a baseline, and a new algorithm based on the concept of triaging functions and Cayley graphs. The lab focuses on two important dimensions of the problem, quality and performance. Quality is measured by the size of the generated test suites and their associated coverage ratio, for various specifications and coverage metrics. Performance is the ability of a test generation technique to produce results in reasonable time, and to scale well to large specifications. The specifications used for testing have been gathered from various sources. This includes temporal specification patterns introduced by Dwyer et. al. and which occur commonly in the specification of concurrent and reactive systems; all the finite-state specifications that could be obtained from related works on test sequence generation (less than a dozen); classical examples of specifications such as the Qui-Donc protocol and the microwave FSM; FSMs obtained from common regular expressions, such as validating e-mail addresses and date formats; and a collection of FSM from the Büchi Store. The coordination of the experiments and the generation of the results is done through the use of the LabPal library.
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.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".