Automated model repair for distributed programs
Bibliographic record
Abstract
Model repair is a formal method that aims at fixing bugs in models automatically. Typically, these models are finite state automata that can be compactly represented using guarded commands or variations thereof. The bugs in these models can be identified using traditional techniques, such as verification, testing, or runtime monitoring. However, these techniques do not assist in fixing bugs automatically. The goal in model repair is to automatically transform an input model into another model that satisfies additional properties (e.g., a property that the original model fails to satisfy). Moreover, such transformation should preserve the existing specification of the input model. In this article, we review the efforts in the past decade on developing model repair algorithms in different domains. These domains include distributed computing, fault-tolerance and self-stabilization, and real-time systems. We present the results on complexity analysis, techniques for tackling intractability of the problem and scalability, and related tools. The techniques and tools discussed in this article demonstrate the feasibility of automated synthesis of well-known protocols such as Byzantine agreement, token ring, fault-tolerant mutual exclusion, etc.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".