Model Checking Randomized Algorithms with Java PathFinder
Bibliographic record
Abstract
On the one hand, probabilistic model checkers such as PRISM have been successfully employed to verify models of probabilistic systems. However, they are not suitable for checking properties such as uncaught exceptions of the actual code of the system. On the other hand, model checkers such as Java PathFinder (JPF) have been used with success to verify actual code of systems. However, they do not take into account the probabilities associated with the probabilistic choices of the systems. In this paper, we bridge the gap by extending JPF so that it takes those probabilities into account. We introduce a method to express a probabilistic choice in Java so that JPF can easily extract the probabilities of the alternatives of the probabilistic choice. By default, JPF traverses the state space using a depth-first search or a breadth-first search. We have implemented in JPF several search strategies which use the probabilities associated with the alternatives of probabilistic choices. To address the state explosion problem, we keep track of the amount of progress made by JPF.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".