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Record W1970329742 · doi:10.1145/1408647.1408651

On-demand materialization of aspects for application development

2008· article· en· W1970329742 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 Waterloo
Fundersnot available
KeywordsComputer scienceSemantics (computer science)EclipseAdvice (programming)Aspect-oriented programmingSoftware engineeringProgram transformationInterface (matter)Programming languageApplication programming interfaceTransformation (genetics)Operating systemSoftware

Abstract

fetched live from OpenAlex

Framework-based application development requires applications to be implemented according to rules, recipes and conventions that are documented or assumed by the framework's Application Programming Interface (API), thereby giving rise to systematic usage patterns. While such usage patterns can be captured cleanly using conventional aspects, their variations, which arise in exceptional conditions, typically cannot be. In this position paper, we propose materializable aspects as a solution to this problem. A materializable aspect behaves as a normal aspect for default joinpoints, but for exceptional joinpoints, it turns into a program transformation and analysis mechanism, with the IDE transforming the advice in-place and allowing the application developer to modify the materialized advice within the semantics of the aspect. We demonstrate the need for materializable aspects through a preliminary study of open-source SWT-based applications and describe our initial implementation of materializable aspects in Eclipse.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.869
Threshold uncertainty score0.164

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 head0.042
GPT teacher head0.280
Teacher spread0.238 · 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