Automatic failure detection with Conditional-Belief supervisors
Bibliographic record
Abstract
Failures of a software system are detected by a supervisor, a separate unit which observes the inputs and outputs of the system and reports its failures in real-time. The supervisor determines whether a failure has occurred by comparing the observed and the specified behavior. The specification of behavior is assumed to be expressed in a formalism based on communicating extended finite state machines (specifically, ITU-T SDL). The supervisor must tolerate legal behavioral alternatives resulting from nondeterminisms in the specification. The computational costs of considering such alternatives can be fairly high. The paper presents the Conditional-Belief (CB) theory that reduces the cost of consideration of alternatives by using conditional-beliefs to represent sets of legal behavioral alternatives. The paper reviews belief-based supervision, introduces the CB theory, and outlines an algorithm for conversion of a class of SDL specification to a CB supervisor model. It describes a demonstration system developed to evaluate CB supervision, and summarizes failure detection and computational cost results for the supervisor of the control program of a small telephone exchange.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".