Improving Reliability and Safety by Trading off Software Failure Criticalities
Bibliographic record
Abstract
A number of voters have been proposed for n-version programming diversity designed software systems. The knowledge about various software failure criticalities is not incorporated in the decisions of these voters. Moreover, failure classes contradict among each other with respect to their fault tolerance requirements, as a result, current voters either consider different failures equally or they mask only certain types of failures. Therefore, the voters need to consider system criticalities to different failures based on their fault tolerance requirements trade-off. We propose an approach for trading off system criticalities to different failures. In this approach, we introduce two implementation parameters: the voter constraint hardness and the number of participants in the voting process. We use failure criticalities trade-off to determine the optimal values of these two parameters. This trade-off enhances the ability of a voter to consider different failure criticalities. It also decreases the rate of performance failures. We provide an analysis for the relationships between the implementation parameters and the failure occurrence rate of each failure class. We derive system reliability and safety based on our approach, and we show gains in both of them. The proposed approach can be used to build fault tolerant systems based on n-version programming that use any generic or hybrid voter.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".