Automating comprehensive safety analysis of concurrent programs using verisoft and TXL
Bibliographic record
Abstract
In run-time safety analysis the executions of a concurrent program are monitored and analyzed with respect to safety properties. Similar to testing, run-time analysis is quite efficient, but it also tends to be incomplete. The results pertain only to the observed executions which may constitute just a small subset of all possible executions. In this paper, we describe a tool called ViP which uses the software model checker VeriSoft to perform comprehensive run-time safety analyses of concurrent C/C++ programs. A ViP analysis proceeds in three fully automated steps: First, the input program is prepared for a VeriSoft analysis through instrumentation. Next, VeriSoft is invoked to generate the traces corresponding to all possible executions of the program. Then, the traces are checked efficiently for specification violations. The instrumentation is based on the source code transformation language TXL. TXL allows for the instrumentation to be described in terms of rewrite rules and gives ViP a remarkable amount of flexibility. The paper describes ViP together with its use of VeriSoft and TXL. Several sample analyses are discussed to illustrate the use of ViP.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".