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Record W2005865760 · doi:10.1145/1501434.1501526

From stakeholder goals to product features

2006· article· en· W2005865760 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoUniversity of Ontario Institute of Technology
KeywordsComputer scienceStakeholderIdentification (biology)Product (mathematics)Variation (astronomy)SoftwareNew product developmentArchitectureSoftware architectureProcess managementSoftware engineeringSystems engineeringBoundary (topology)Product designData scienceRisk analysis (engineering)EngineeringBusinessMathematics

Abstract

fetched live from OpenAlex

Variability in complex software systems arises from the diverse characteristics, views, preferences, and goals of stakeholders. Recent variability research focuses on stakeholders' goals, using models, to analyze the space of alternative solutions for software functionalities. In our study we extend the goals-requirements-features approach by considering variability along multiple product development stages. We depict variability at the early and late requirements definition stage, architecture design, detailed design, and runtime, proposing a role-based framework for variability analysis. Variability design involves the placement of a decision boundary to identify the space of alternative features that can be left 'open' for the next stage of product decisions. We also analyze softgoals variability to allow early identification of variation points in the product architecture.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.431
Threshold uncertainty score0.374

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 head0.048
GPT teacher head0.281
Teacher spread0.233 · 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