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Record W2808848181 · doi:10.1109/compsac.2018.10345

Traceability in Systems Engineering: An Avionics Case Study

2018· article· en· W2808848181 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
TopicSoftware Reliability and Analysis Research
Canadian institutionsCarleton University
FundersUniversity of Nottingham
KeywordsTraceabilityRequirements traceabilityTRACE (psycholinguistics)Computer scienceAvionicsSoftware engineeringSemantics (computer science)Systems engineeringRequirements engineeringProgramming languageEngineeringSoftware

Abstract

fetched live from OpenAlex

In Systems Engineering (SE), development of complex systems involves a collaboration of expertise from different domains. Heterogeneous artifacts are generated using different modeling tools. Capturing the traceability information among these artifacts helps serve many purposes, including change impact analysis; validation and verification; and requirements tracking. However, creating trace links among these heterogeneous artifacts is problematic. No precise semantics exist for the trace links that relate them. This paper shows how to capture traceability information in a system with heterogeneous artifacts, illustrated here using an avionics case study that uses a traceability model and a trace links taxonomy that we constructed and published previously.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.036
GPT teacher head0.329
Teacher spread0.293 · 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