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Developing Non-Functional Requirements for a Service-Oriented Application Platform

2011· book-chapter· en· W2484809186 on OpenAlex
Daniel Groß, Eric Yu, Xiping Song

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

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNon-functional requirementComputer scienceTerminologyDomain (mathematical analysis)Systems engineeringSoftware engineeringFunctional requirementSoftware deploymentService (business)Requirements analysisMetric (unit)Software developmentEngineeringSoftwareOperating systemOperations management

Abstract

fetched live from OpenAlex

The challenges in developing non-functional requirements (NFRs) for an application platform go much beyond those for a single application system. To derive platform NFRs from NFR specifications of different domain applications, requirements analysts must deal with much variation of domain specific NFRs, with different deployment configurations and load conditions, with different NFR related trade-offs, as well as with different terminology and metric definitions. This chapter presents a platform NFR development method that supports dealing with the aforementioned challenges. The presented method offers a goal- and scenario-oriented modeling and analysis technique that supports dealing with qualitative and quantitative NFRs during platform NFR development in an integrated way. The platform NFR development method was used to develop NFRs of a service-oriented application platform for three different application domains in an industrial setting.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.152
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
Bibliometrics0.0000.000
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 head0.104
GPT teacher head0.304
Teacher spread0.200 · 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