An incremental method for testing timed input output automata
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
Timed input output automaton (TIOA) model has widely been used, over the last two decades, for testing time dependent systems. Consequently, several testing methods have been developed for generating test cases from TIOA. They all generate test cases from the entire specification in order to achieve certain fault coverage. However, in practice, the system requirements usually evolve throughout the lifetime of the system and the specifications are modified incrementally to accommodate these changes. Moreover, during system maintenance, designers usually change the specification of the given system due to fault detection, changes in user requirements, and/or system environment. This paper addresses incremental testing of TIOA specifications for minimizing the testing effort whenever the system requirements or the system environment change. The proposed method targets the test of only the modified parts of an evolving system while guaranteeing the same level of confidence as testing the entire specification.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 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