On the Contributions of an End-to-End AOSD Testbed
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
Aspect-Oriented Software Development (AOSD) techniques are gaining increased attention from both academic and industrial organisations. In order to promote a smooth adoption of such techniques it is of paramount importance to perform empirical analysis of AOSD to gather a better understanding of its benefits and limitations. In addition, the effects of aspect-oriented (AO) mechanisms on the entire development process need to be better assessed rather than just analysing each development phase in isolation. As such, this paper outlines our initial effort on the design of a testbed that will provide end-to-end systematic comparison of AOSD techniques with other mainstream modularisation techniques. This will allow the proponents of AO and non- AO techniques to compare their approaches in a consistent manner. The testbed is currently composed of: (i) a benchmark application, (ii) an initial set of metrics suite to assess certain internal and external software attributes, and (in) a "repository" of artifacts derived from AOSD approaches that are assessed based on the application of (i) and (ii). This paper mainly documents a selection of techniques that will be initially applied to the benchmark. We also discuss the expected initial outcomes such a testbed will feed back to the compared techniques. The applications of these techniques are contributions from different research groups working on AOSD.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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