Traffic-aware stress testing of distributed systems based on UML models
Why this work is in the frame
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Bibliographic record
Abstract
A stress test methodology aimed at increasing chances of discovering faults related to network traffic in distributed systems is presented. The technique uses the UML 2.0 model of the distributed system under test, augmented with timing information, and is based on an analysis of the control flow in sequence diagrams. It yields stress test requirements that are made of specific control flow paths along with time values indicating when to trigger them. Different variants of our stress testing technique already exist (they stress different aspects of a distributed system) and we focus here on one variant that is designed to identify and to stress test the system at the instant when data traffic on a network is maximal. Using a real-world distributed system specification, we design and implement a prototype distributed system and describe, for that particular system, how the stress test cases are derived and executed using our methodology. The stress test results indicate that the technique is significantly more effective at detecting network traffic-related faults when compared to test cases based on an operational profile.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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