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Software product lines: a case study

2000· article· en· W2051221173 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.

Bibliographic record

VenueSoftware Practice and Experience · 2000
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceSoftware product lineSoftware engineeringExploitTime to marketAbstractionProduct (mathematics)Domain (mathematical analysis)JavaDocumentationDomain analysisDomain engineeringSoftwareNew product developmentProcess (computing)Software developmentProgramming languageSoftware constructionComputer security

Abstract

fetched live from OpenAlex

A software product line is a family of products that share common features to meet the needs of a market area. Systematic processes have been developed to dramatically reduce the cost of a product line. Such product-line engineering processes have proven practical and effective in industrial use, but are not widely understood. The Family-Oriented Abstraction, Specification and Translation (FAST) process has been used successfully at Lucent Technologies in over 25 domains, providing productivity improvements of as much as four to one. In this paper, we show how to use FAST to document precisely the key abstractions in a domain, exploit design patterns in a generic product-line architecture, generate documentation and Java code, and automate testing to reduce costs. The paper is based on a detailed case study covering all aspects from domain analysis through testing. Copyright © 2000 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.004
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 head0.040
GPT teacher head0.345
Teacher spread0.305 · 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