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Record W2395866526

Exploring Domain Requirements and Technology Solutions: A Goal Modeling Approach

2014· article· en· W2395866526 on OpenAlex
Davide Calvaresi, Arnon Sturm, Eric Yu, Aldo Franco Dragoni

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

VenueUniversità Politecnica delle Marche (Università Politecnica delle Marche) · 2014
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDomain (mathematical analysis)Domain engineeringComputer scienceDomain analysisDomain modelVariety (cybernetics)Requirements elicitationRequirements analysisFeature-oriented domain analysisRequirements engineeringSystems engineeringSoftware engineeringUser requirements documentHuman–computer interactionEngineeringDomain knowledgeArtificial intelligenceSoftware developmentProgramming languageSoftware
DOInot available

Abstract

fetched live from OpenAlex

Abstract. As a requirement engineering technique i * has been used to model requirements for a single system. In this paper, we consider whether i * can be used to explore and map user needs and requirements for an entire application domain rather than for a single system. A domain-wide requirements model can be used to assess the suitability of various technology architectures and solutions for that domain. The domain of Ambient Assisted Living (AAL) is characterized by a large variety of stakeholders with different professional and socio-cultural backgrounds. The domain is highly heterogeneous and thus suitable for our purpose of demonstrating domain exploration. We discuss the challenges in mapping that domain, and our attempts to adapt i * concepts and usage for this purpose. 1.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0040.006
Research integrity0.0010.001
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 head0.123
GPT teacher head0.262
Teacher spread0.139 · 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