Employing aspect composition in adaptive software systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adaptive software is a closed-loop system which aims at adjusting itself at runtime in different situations. Such a system needs a set of sensors to monitor attributes of itself and its operating environment. Furthermore, it requires a set of effectors in order to make changes in its entities. These changes are essential for fulfilling system's non-functional and functional requirements. Aspect-Oriented Programming (AOP) is a promising way to develop these sensors and effectors through static and dynamic composition of advices. This paper presents the experience of employing aspect composition in engineering a sample adaptive software. The main objectives are exploring the difficulties of utilizing this approach, and investigating the effectiveness of aspect-based adaptation actions. A J2EE bookstore application, TPC-W, was selected as the case study, to instrument sensors by the aid of static aspects, and effectors using dynamic aspects. The findings are promising, and encourage us to continue this line of research for more complex systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it