Failure Type-Aware Reliability Assessment with Component Failure Dependency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most of the existing reliability assessment techniques assume that components fail independently and consider different types of failures equally. By disregarding component failure dependency, these techniques assume inappropriately that a component failure does not affect any other component and it directly leads to a system failure. Also, by considering different failure types equally, reliability assessment disregards various criticality levels or severities of different failures. In practice, component failures affect other system components and vary with respect to their criticality levels. Recently, some propagation-based techniques incorporate component failure dependency in the reliability measure through failure propagation analysis by focusing only on value failures. Therefore, other failures (e.g., silent and performance) are not considered in the current failure propagation analysis. In this paper, we present an approach for reliability assessment of fault tolerant component-based software systems considering component failure dependency and enabling failure type-awareness. We incorporate component failure dependency in the reliability quantification by analyzing failure propagation among system components. We enable failure type-awareness by analyzing the propagation of different failure types in fault tolerant components and the architectural service routes among them. Finally, we aggregate the impacts of these failure types on system reliability.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it