Bibliographic record
Abstract
Concurrent systems, including asynchronous circuits, computer networks, and multi-threaded programs, have important applications, but they are also very complex and expensive to test. This thesis studies how to test concurrent systems through contexts consisting of queues. Queues, modeling buffers and communication delays, are an integral part of the test settings for concurrent systems. However, queues can also distort the behavior of the concurrent system as observed by the tester, so one should take into account the queues when defining conformance relations or deriving tests. On the other hand, queues can cause state explosion, so one should avoid testing them if they are reliable or have already been tested. To solve these problems, we propose two different solutions. The first solution is to derive tests using some test selection criteria such as test purposes, fault coverage, and transition coverage. The second solution is to compensate for the problems caused by the queues so that testers do not discern the presence of the queues in the first place. Unifying the presentation of the two solutions, we consider in a general testing framework partial specifications, various contexts, and a hierarchy of conformance relations. Case studies on test derivation for asynchronous circuits, communication protocols, and multi-threaded programs are presented to demonstrate the applications of the results.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".