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9.5.2 New Opportunities for Architecture Measurement

2013· article· en· W2042678439 on OpenAlex
Ronald S. Carson, Paul V. Kohl

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

VenueINCOSE International Symposium · 2013
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsArchitectureNexus (standard)SuiteComputer scienceSystems engineeringPlan (archaeology)Reference architectureSystems architectureSoftware engineeringArchitecture frameworkProcess managementEngineering managementEngineeringSoftware architectureSoftwareEmbedded system

Abstract

fetched live from OpenAlex

Abstract Recent industry progress in architecture definition, architecting tools, model‐based systems engineering, and customer policy has created both a nexus of demand and opportunities to advance the state of architecture measurement. In this paper we describe recent industry working group results and propose architecture measures intended to meet the needs of systems engineers and their program managers as leading indicators of system development health. The measures are documented using the PSM (Practical Software and Systems Measurement) methodology. General techniques are proposed that take advantage of the opportunities afforded by the current architecture modeling environment to provide a basic measurement plan for architecture, including leveraging existing measurement concepts found within the Systems Engineering Leading Indicators. The result is a comprehensive and tailorable suite of measures that can provide decision‐making data to program managers and technical program leaders.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.051
GPT teacher head0.267
Teacher spread0.216 · 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