Supervision of real-time software systems using optimistic path prediction and rollbacks
Bibliographic record
Abstract
Real time supervision is a technique for automatically detecting and reporting failures in the external behaviour of real time software systems. Failure detection is achieved by monitoring the target system's external inputs and outputs, in a 'black box' manner and comparing its behaviour with the formally specified behaviour of the system. The paper presents the Optimistic Path Prediction and Rollbacks (OPPR) approach to real time supervision. In this technique, the supervisor predicts a single likely behaviour of the target system and, if the observed behaviour does not match the prediction, rolls back and creates a new prediction of the legal behaviour. A failure is detected when the supervisor has explored all valid behaviours without matching the observed behaviour. The paper opens by introducing the field of real time supervision and examining existing techniques. The core of the paper presents the basic algorithm of the OPPR method, with an example to illustrate its operation. The paper closes by describing an evaluation system, summarizing the experimental results and examining the performance of the OPPR scheme.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".