On the Maintainability of Aspect-Oriented Software: A Concern-Oriented Measurement Framework
Bibliographic record
Abstract
Aspect-oriented design needs to be systematically assessed with respect to modularity flaws caused by the realization of driving system concerns, such as tangling, scattering, and excessive concern dependencies. As a result, innovative concern metrics have been defined to support quantitative analyses of concern's properties. However, the vast majority of these measures have not yet being theoretically validated and managed to get accepted in the academic or industrial settings. The core reason for this problem is the fact that they have not been built by using a clearly-defined terminology and criteria. This paper defines a concern-oriented framework that supports the instantiation and comparison of concern measures. The framework subsumes the definition of a core terminology and criteria in order to lay down a rigorous process to foster the definition of meaningful and well-founded concern measures. In order to evaluate the framework generality, we demonstrate the framework instantiation and extension to a number of concern measures suites previously used in empirical studies of aspect-oriented software maintenance.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".