Proceedings of the 2008 AOSD workshop on Early aspects
Bibliographic record
Abstract
Crosscutting concerns at the requirements and architecture level are referred to as early aspects. Analyzing early aspects is important in order to reason about the impact of crosscutting concerns on each other and later software development activities. Identifying and modularizing aspects in models developed during early phases of the software lifecycle is important because it supports the identification and analysis of aspects during design and coding phases. The series of Early Aspects Workshops has been running since AOSD 2002. The theme of the workshop at the 7th International Conference on Aspect-Oriented Software Development (AOSD.08) was early aspects and software product lines. The AOSD and software product line communities have become increasingly aware that techniques from one of these fields may be usefully applied to problems in the other field. The workshop aimed at helping the communities of software product lines, requirements engineering, software architecture design, and aspect-oriented software development to exchange ideas, identify existing problems, and discuss potential solutions that integrate AOSD and software product line techniques. The workshop was one of a series of similarly themed Early Aspects Workshops at major conferences in 2008. Other Early Aspects Workshops on early aspects and software product lines were planned for ICSE in Leipzig, Germany, in May and for SPLC in Limerick, Ireland, in September.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".