On the Contributions of an End-to-End AOSD Testbed
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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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".