A benchmark for NuSMV extensions to BeepBeep 3
Bibliographic record
Abstract
This lab studies a formalization of event stream processing pipelines as Kripke structures that can be handled by a model checker. More specifically, the BeepBeep event stream processing library has been modified in order to export chains of processors as input files for the NuSMV model checking tool. This makes it possible to formally verify properties on these pipelines, and opens the way to the use of such pipelines directly within a model checker as an extension of its specification language. The goal of this benchmark is to perform an experimental evaluation of the proposed implementation, by measuring the execution time and memory consumption of NuXmv on a number of BeepBeep pipelines and for a sample of generic properties, with a special focus on the impact of parameters Q (the size of the internal queues in each processor) and N (the size of the domain for numerical variables). This repository contains an instance of LabPal, an environment for running experiments on a computer and collecting their results in a user-friendly way. The author of this archive has set up a set of experiments, which typically involve running scripts on input data, processing their results and displaying them in tables and plots. LabPal is a library that wraps around these experiments and displays them in an easy-to-use web interface. The principle behind LabPal is that all the necessary code, libraries and input data should be bundled within a single self-contained JAR file, such that anyone can download and easily reproduce someone else's experiments. Detailed instructions can be found on the LabPal website, [https://liflab.github.io/labpal]. The bundled JAR file in this artifact contains all the source code, documentation, and compiled Java files to run, display and modify the set of experiments mentioned in the related publication.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".