Proceedings of the 3rd international conference on Aspect-oriented software development
Bibliographic record
Abstract
AOSD '04, held at Lancaster UK, March 22-26, 2004, is the third annual conference on Aspect-oriented Software Development. This volume comprises the proceedings of AOSD '04.Over the past year, interest in AOSD has continued to grow amongst both academics and practitioners. The conference program reflects this balance between leading edge research and industrial application with a mix of technical papers, reports from industry, a student research event, an industrial panel on the topic of aspect-oriented programming in the enterprise, and demonstrations from both researchers and practitioners. The six workshops and seven tutorials held before the start of the main conference reflected the breadth of topics of interest to the AOSD community from general aspect programming language issues and software development practices to specific domains such as aspects for application-level security.The 15 papers presented as part of the technical program were selected from 82 submissions (up 17% from 2003). The program committee met on Dec. 12 and 13, 2003 at Northeastern University to select the papers. All papers were reviewed by at least three reviewers, except program committee papers that were read by at least 4 reviewers.As part of the technical program, we are also honoured to present two invited keynote speakers: Daniel Sabbah, an IBM Vice President, who will describe how IBM plans to put aspect-oriented technology into production, and Bashar Nuseibeh, a Professor at The Open University who has done pioneering work on viewpoints in requirements engineering and who will offer a research agenda for aspect-oriented requirements engineering. In addition, we are also thrilled to present an invited state-of-the-art speaker, Jonas Bontr, who will describe his novel AspectWerkz 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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.021 |
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".