On Failure Propagation in Component-Based Software Systems
Bibliographic record
Abstract
Ensuring reliability in component-based software systems (CBSSs) is important for their effective applications in large scale and safety critical systems. However, only few techniques consider failure propagation in system architectures for system reliability assessment. Those techniques focus only on content failure propagation through component interfaces. Therefore, the evaluation of CBSS architectures based on the current techniques fails to consider the impacts of all failure types on system reliability. In this paper, we present a failure propagation analysis technique for CBSSs. We analyze failure propagation based on architectural service routes (ASRs). An ASR is a sequence of components that are connected through interfaces. We discuss the attributes of ASRs with respect to system components and present their impacts on failure propagation and consequently on the reliability of CBSSs. Further analysis determines upper and lower bounds of failure propagation among components and shows some relationships between system reliability and architectural attributes. Our technique is not limited to any failure type, and it considers failure scattering and masking. Therefore, unlike other works, the proposed technique demonstrates more accurate representation of the practical aspect of failure propagation in CBSSs. The technique can also be used to achieve reliable designs in the early design stages of CBSSs and to localize component faults in the operational stage. We compare different example architectures based on their impacts on system reliability.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".