Proceedings of the 9th International Conference on Aspect-Oriented Software Development
Bibliographic record
Abstract
It is our pleasure to welcome you to the 9th ACM International Conference on Aspect-Oriented Software Development (AOSD.10). This volume comprises the final versions of the research and industry papers presented at the AOSD.10 conference held in Rennes and Saint-Malo, France, March 15-19, 2010. The proceedings also contain the abstracts of the keynote presentations. AOSD.10 is the ninth edition in a series of annual AOSD conferences. The series started in 2002 in Enschede (The Netherlands), followed by Boston, Massachusetts (USA) in 2003, Lancaster (UK) in 2004, Chicago, Illinois (USA) in 2005, Bonn (Germany) in 2006, Vancouver (Canada) in 2007, Brussels (Belgium) in 2008, Charlottesville, Virginia (USA) in 2009 and now Rennes and Saint-Malo, France in 2010. This year's conference continues the tradition of being the premier forum for presentation of research and industry results on leading-edge issues in aspect-oriented software development. The conference reflects the remarkable growth in scale and complexity of the systems now being developed, where modularity and abstraction are essential, not only in code, but across many kinds of software artifacts. Following its predecessors, its focus has been broadening to include other activities of software and systems development, such as software and systems specification, architecture, and adaptation. The mission of this edition is to fulfill the needs of heterogeneous applications and environments, and to identify new directions for future research and development. AOSD.10 gives academia and industry a unique opportunity to share perspectives with others who are interested in the various forms of modularity and abstraction.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.048 |
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".