Proceedings of the 6th workshop on Foundations of aspect-oriented languages
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.024 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".