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Record W2098606386 · doi:10.1109/ase.2002.1114991

Generative design patterns

2002· article· en· W2098606386 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceGenerative grammarGenerative DesignSoftware design patternObject-oriented designUSableDesign patternConstruct (python library)Structural patternRepresentation (politics)Set (abstract data type)Software designProgramming languageSoftware engineeringObject (grammar)Code (set theory)Human–computer interactionObject-oriented programmingSoftwareArtificial intelligenceSoftware developmentEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

A design pattern encapsulates the knowledge of object-oriented designers into re-usable artifacts. A design pattern is a descriptive device that fosters software design re-use. There are several reasons why design patterns are not used as generative constructs that support code re-use. The first reason is that design patterns describe a set of solutions to a family of related design problems and it is difficult to generate a single body of code that adequately solves each problem in the family. A second reason is that it is difficult to construct and edit generative design patterns. A third major impediment is the lack of a tool-independent representation. A common representation could lead to a shared repository to make more patterns available. We describe a new approach to generative design patterns that solves these three difficult problems. We illustrate this approach using tools called CO/sub 2/P/sub 2/S and Meta-CO/sub 2/P/sub 2/S but our approach is tool-independent.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.385
Threshold uncertainty score0.233

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 science0.0000.000
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 head0.144
GPT teacher head0.283
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