Bibliographic record
Abstract
Concurrent systems are becoming more and more popular. Improving the qualities of these systems is an important issue we are facing. It is well known that developing concurrent software is a challenging task, mainly because of the non-determinism behavior involved in the system. One promising way to help the designer in this task is providing formal verification methods that can detect concurrency-related errors at the design stage. In this thesis, we present our study on model checking concurrency-related correctness of design artifacts for concurrent and distributed systems. We construct our model for design artifacts using a network of synchronizing finite state machines (NSFSM), which provides well known synchronization mechanisms from programming languages. The formal verification is performed by SPIN, which is based on visiting all the global system states reachable from a given initial state to check if some properties hold. The input language of SPIN is PROMELA, which features a C-like syntax, dynamic creation of processes, and various interprocess communication models. In order to apply SPIN, we provide an automatic translation from NSFSMs to PROMELA programs. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .L5. Source: Masters Abstracts International, Volume: 40-03, page: 0724. Adviser: Xiaojun Chen. Thesis (M.Sc.)--University of Windsor (Canada), 2001.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".