MétaCan
Menu
Back to cohort
Record W4402570961 · doi:10.1109/icstw60967.2024.00055

Timed Model-Based Mutation Operators for Simulink Models

2024· article· en· W4402570961 on OpenAlexafffund
Jian Chen, Manar H. Alalfi, Thomas Dean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsToronto Metropolitan UniversityQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceMutationGeneticsBiology

Abstract

fetched live from OpenAlex

Model-based mutation testing (MBMT) is a specialized area of model-based testing focused on generating faulty model versions to improve test cases. This paper introduces timed mutation operators for Matlab/Simulink (ML/SL) models, aimed at assessing tools that integrate a timed task model for enhanced support in Model-in-the-Loop (MIL) simulation. We applied these mutation operators to evaluate SimSched, a tool that incorporates model transformation to integrate scheduling into the model for real-time context validation during simulation. Results indicate SimSched’s superiority over the base-case Stateflow Scheduler in identifying time-related faults.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.208
GPT teacher head0.472
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicSimulation Techniques and ApplicationsFrench-language works237,207