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Record W2166054436 · doi:10.1145/1921705.1921706

Measurement of software requirements derived from system reliability requirements

2010· article· en· W2166054436 on OpenAlexaff
Khalid T. Al‐Sarayreh, Alain Abran, Luca Santillo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Reliability and Analysis Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsReliability engineeringSoftware requirements specificationNon-functional testingComputer scienceSoftware qualityReliability (semiconductor)Functional requirementSoftware requirementsSystem requirementsSoftware systemSoftwareSoftware reliability testingSystem requirements specificationVerification and validationAvionics softwareRequirements analysisSoftware constructionSoftware developmentSoftware engineeringRequirementEngineeringOperating system

Abstract

fetched live from OpenAlex

Reliability is typically described initially as a non functional requirement at the system level. Systems engineers must subsequently apportion these system requirements very carefully as either software or hardware requirements to conform to the reliability requirements of the system. A number of concepts are provided in the ECSS, ISO 9126, and IEEE standards to describe the various types of candidate reliability requirements at the system, software, and hardware levels. This paper organizes these concepts into a generic standards-based reference model of the requirements at the software level for system reliability. The structure of this reference model is based on the generic model of software requirements proposed in the COSMIC -- ISO 19761 model, thereby allowing the measurement of the functional size of such reliability requirements implemented through software.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.042
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.042
GPT teacher head0.290
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations16
Published2010
Admission routes1
Has abstractyes

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