Modeling Fault Tolerance Tactics with Reusable Aspects
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
This paper is part of a larger research project aiming to integrate dependability analysis in the early phases of the software development process, by generating and analyzing Stochastic Reward Net (SRN) models from UML software models. The paper is focused on adding fault tolerance to software designs by using Aspect-Oriented Modeling. More specifically, single-version fault tolerance tactics are modeled as generic reusable aspects annotated with dependability attributes. The paper describes how the generic aspects are instantiated, bound to the context and composed with the original UML software model. Since an SRN analysis model is generated from the UML model, the paper discusses what kind of transformation rules are necessary for translating fault tolerance tactics from UML to SRN, giving as an example the transformation rule for checkpoint synchronization. A case study illustrates the proposed approach.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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