Proceedings of the 3rd workshop on Testing aspect-oriented programs
Bibliographic record
Abstract
Aspect-Oriented Programming (AOP) is a new paradigm for dealing with concerns that cut cross system components. It aims to encapsulate crosscutting concerns into aspects with the advice invoked at the designated points of program execution. While AOP improves the modularity of crosscutting concerns, the features and mechanisms of AOP also yield new fault types. New strategies, techniques, and practices are required for the quality assurance of aspect-oriented programs. The 2007 Workshop on Testing Aspect-Oriented Programs (WTAOP'07), in conjunction with the Six International Conference on Aspect-Oriented Software Development in Vancouver, British Columbia, Canada, is the third workshop to focus on the issues associated with the topic of testing and quality assurance for aspect-oriented programs. The workshop continues the work of the previous WTAOP workshops. The first WTAOP workshop occurred on March 15, 2005, in conjunction with the Fourth International Conference on Aspect-Oriented Software Development in Chicago, Illinois. The workshop was organized by Roger T. Alexander from Colorado State University and Anneliese Andrews from Washington State University. The second WTAOP workshop on July 20, 2006 in conjunction with the International Symposium on Software Testing and Analysis, was organized by Dehla Sokenou from GEBIT Solutions GmbH, Stephan Herrmann from Technische Universitat Berlin and Roger T. Alexander from Washington State University. The WTAOP workshop series brings together researchers and practitioners that have interests in this important area, with the goal being to establish a research agenda that focuses on the challenges and issues associated with testing aspect-oriented programs. The WTAOP'07 workshop received a total of six submissions. Each submission was reviewed by two or three members of the international program committee. Five of the submissions were selected for presentation at the workshop. The next section gives a brief introduction to the selected papers included in these workshop proceedings.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.023 |
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".