Traffic-aware Stress Testing of Distributed Real-Time Systems Based on UML Models in the Presence of Time Uncertainty
Bibliographic record
Abstract
In a previous work, we reported and experimented with a stress testing methodology to detect network traffic- related real-time (RT) faults in distributed real-time systems (DRTSs) based on the design UML models. The stress methodology, referred to as time-shifting stress test methodology (TSSTM), aimed at increasing chances of discovering RT faults originating from network traffic overloads in DRTSs. The TSSTM uses the UML 2.0 model of a system under test (SUT), augmented with timing information, and is based on an analysis of the control flow in UML sequence diagrams. In order to devise deterministic test requirements (from time point of view) that yield the maximum stress test scenario in terms of network traffic in a SUT, the TSSTM methodology requires that the timing information of messages in sequence diagrams is available and as precise as possible. In reality, however, the timing information of messages is not always available and precise. As we demonstrate using a case study in this work, the effectiveness of the stress test cases generated by TSSTM is very sensitive to such time uncertainty. In other words, TSSTM might generate imprecise and not necessarily maximum stressing test cases in the presence of such time uncertainty and, thus, it might not be very effective in revealing RT faults. To address the above limitation of TSSTM, we present in this article a modified testing methodology which can be used to stress test systems when the timing information of messages is imprecise or unpredictable. The stress test results of applying the new test methodology to a prototype DRTS indicate that, in the presence of uncertainty in timing information of messages, the new methodology is more effective in detecting RT faults when compared to our previous methodology (i.e., TSSTM) and also test cases based on an operational profile.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".