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Record W4283204998 · doi:10.1109/ms.2022.3164872

Software Design Trends Supporting Multiconcern Assurance

2022· article· en· W4283204998 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

VenueIEEE Software · 2022
Typearticle
Languageen
FieldEngineering
TopicSafety Systems Engineering in Autonomy
Canadian institutionsCarleton University
Fundersnot available
KeywordsDependabilitySoftware security assuranceComputer scienceLife-critical systemComputer securityRisk analysis (engineering)Variety (cybernetics)SoftwareAvionics softwareSystems engineeringEngineeringSoftware developmentSoftware qualitySoftware engineeringBusinessInformation securitySecurity service

Abstract

fetched live from OpenAlex

Complex software systems have become increasingly entwined in a wide variety of systems, such as critical infrastructure, industrial control systems, medical devices, automobiles, airplanes, and spacecraft. Assuring the security and safety, as well as other dependability concerns, such as availability, robustness, and reliability, of software-intensive systems remains among the top priorities for governments and providers of critical systems and services. Manufacturers, owners, and operators of the components and devices that make up these software systems strive to ensure that they have adequately addressed emerging concerns related to safety hazards, security threats, and performance challenges, among others.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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