Fault‐driven stress testing of distributed real‐time software based on UML models
Bibliographic record
Abstract
Abstract In a previous article, a stress testing methodology was reported to detect network traffic‐related Real‐Time (RT) faults in distributed RT systems based on the design UML model of a System Under Test (SUT). The stress methodology, referred to as Test LOcation‐driven Stress Testing (TLOST), aimed at increasing the chances of RT failures (violations in RT constraints) associated with a given stress test location (an network or a node under test). As demonstrated and experimented in this article, although TLOST is useful in stress testing different test locations (nodes and network, it does not guarantee to target (test) all RT constraints in an SUT. This is because the durations of message sequences bounded by some RT constraints might never be exercised (covered) by TLOST. A complementary stress test methodology is proposed in this article, which guarantees to target (cover) all RT constraints in an SUT and detect their potential RT faults (if any). Using a case study, this article shows that the new complementary methodology is capable of targeting the RT faults not detected by the previous test methodology. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".