A System-Level Approach for Model-Based Verification of Distributed Software Systems
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
A major challenge in design of distributed software systems is predicting and avoiding unexpected behaviors at the run time. Detecting those behaviors after the system is implemented can be very costly and detecting them during design and implementation stages is a cost effective alternative. Therefore, model-based verification at early design stages is an important step in designing distributed systems. Most of the existing verification techniques analyze system behaviors by going from specifications to state machines that model individual components' behaviors. Although those methods are shown to be effective in detecting unexpected behaviors for each component, they fail to detect the unexpected behaviors that occur at the system level. There exist a few ad-hoc methods to combine components' behavior into system level behavior. In this paper, we devise a method that considers interactions among components, and propose an algorithm to combine the behavior models of interacting components. The proposed algorithm can be used to perform automated system-level verification. A case study is developed to validate the efficiency of the proposed algorithm in detecting the implied scenarios for distributed system.
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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 | 0.001 |
| 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 | 0.000 |
| 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