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Record W1581898678

Specifying framework constraints with FCL

2004· article· en· W1581898678 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

VenueConference of the Centre for Advanced Studies on Collaborative Research · 2004
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceConstraint (computer-aided design)Programming languageObject Constraint LanguageCode (set theory)Software engineeringUnified Modeling LanguageEngineeringSoftware
DOInot available

Abstract

fetched live from OpenAlex

Object-oriented application frameworks are often misused because application developers do not know how the original framework designers intend their frameworks to be used. The goal of Framework Constraint Language (FCL) is to help detect errors in framework usage. FCL captures the intended usage of frameworks through framework constraints that describe the syntactic structures that frameworks impose on the code of framework-based applications. Framework constraints can be regarded as framework-specific typing rules conveyed by the FCL specifications and enforced by techniques analogous to those of conventional type checking. In essence, FCL is a first-order logic of the syntactic structure of programs. The feasibility and expressiveness of FCL have been evaluated by applying it to parts of frameworks, such as MFC (Microsoft Foundation Classes), and design rules such as Law of Demeter. The current version of the FCL language and associated checker is targeted at C++ frameworks.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.421
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.210
GPT teacher head0.437
Teacher spread0.227 · 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