The Effect of the Distributed Test Architecture on the Power of Testing
Bibliographic record
Abstract
There has been much interest in testing from finite-state machines (FSMs). If the system under test can be modelled by the (minimal) FSM N then testing from an (minimal) FSM M is testing to check that N is isomorphic to M. In the distributed test architecture, there are multiple interfaces/ports and there is a tester at each port. This can introduce controllability/synchronization and observability problems. This paper shows that the restriction to test sequences that do not cause controllability problems and the inability to observe the global behaviour in the distributed test architecture, and thus relying only on the local behaviour at remote testers, introduces fundamental limitations into testing. There exist minimal FSMs that are not equivalent, and so are not isomorphic, and yet cannot be distinguished by testing in this architecture without introducing controllability problems. Similarly, an FSM may have non-equivalent states that cannot be distinguished in the distributed test architecture without causing controllability problems: these are said to be locally s-equivalent and otherwise they are locally s-distinguishable. This paper introduces the notion of two states or FSMs being locally s-equivalent and formalizes the power of testing in the distributed test architecture in terms of local s-equivalence. It introduces a polynomial time algorithm that, given an FSM M, determines which states of M are locally s-equivalent and produces minimal length input sequences that locally s-distinguish states that are not locally s-equivalent. An FSM is locally s-minimal if it has no pair of locally s-equivalent states. This paper gives an algorithm that takes an FSM M and returns a locally s-minimal FSM M′ that is locally s-equivalent to M.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".