Automated model repair for distributed programs
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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