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Record W2912259397

Proceedings of the 6th workshop on Foundations of aspect-oriented languages

2007· article· en· W2912259397 on OpenAlexaboutno aff
William Harrison

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsnot available
Fundersnot available
KeywordsProgramming languageComputer scienceAspect-oriented programmingProgramming paradigmProgramming language theorySeparation of concernsSemantics (computer science)Software engineeringFifth-generation programming languageSoftware
DOInot available

Abstract

fetched live from OpenAlex

Aspect-oriented programming is a paradigm in software engineering and programming languages that promises better support for separation of concerns. The sixth Foundations of Aspect-Oriented Languages (FOAL) workshop was held at the Sixth International Conference on Aspect-Oriented Software Development in Vancouver, Canada, on March 13, 2007. This workshop was designed to be a forum for research in formal foundations of aspect-oriented programming languages. The call for papers announced the areas of interest for FOAL as including: semantics of aspect-oriented languages, specification and verification for such languages, type systems, static analysis, theory of testing, theory of aspect composition, and theory of aspect translation (compilation) and rewriting. The call for papers welcomed all theoretical and foundational studies of foundations of aspect-oriented languages. The goals of this FOAL workshop were to: • Make progress on the foundations of aspect-oriented programming languages. • Exchange ideas about semantics and formal methods for aspect-oriented programming languages. • Foster interest within the programming language theory and types communities in aspect-oriented programming languages. • Foster interest within the formal methods community in aspect-oriented programming and the problems of reasoning about aspect-oriented programs. The workshop was organized by Curtis Clifton (Rose-Hulman Institute of Technology), Gary T. Leavens (Iowa State University), and Mira Mezini (Darmstadt University of Technology). The program committee was chaired by Shmuel Katz (Technion--Israel Institute of Technology).

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.

How this classification was reachedexpand

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.905
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.324
Teacher spread0.292 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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