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Record W4308731055 · doi:10.1145/3550356.3563130

Towards the adoption of model based system safety engineering in the automotive industry

2022· article· en· W4308731055 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpreadsheets and End-User Computing
Canadian institutionsMcMaster UniversityGeneral Motors (Canada)
Fundersnot available
KeywordsAutomotive industrySoftware engineeringFault tree analysisComputer scienceProcess (computing)Leverage (statistics)Systems engineeringEngineeringReliability engineeringOperating system

Abstract

fetched live from OpenAlex

Model-Driven Engineering techniques are becoming increasingly common for use in automotive software engineering, particularly to enable architectural modeling as well as safety analysis, especially fault tree analysis (FTA). One common MDE tool is Medini Analyze from Ansys, which has many tool suites and interfaces that allow for building complex models and performing safety analysis on them. However it can be tedious, error-prone and repetitive to have to construct models by hand. In this paper we introduce strategies for adopting Medini in industry, streamlining the model generation process, and enabling engineers to focus more on safety analysis. The strategies introduced leverage the already existing technologies in Medini, specifically the Javascript API that it enables, as well as interfaces with Microsoft Excel.

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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.225
Teacher spread0.197 · 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

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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