Bibliographic record
Abstract
Aspect-Oriented Programming (AOP) is an emerging paradigm to modularize crosscutting concerns. A series of guidelines to refactor a software system into a common core and a set of variable functionalities have become known as Horizontal Decomposition (HD). In this paper we apply the HD principles to the Prevayler main memory database management system. The objective is to evaluate and refine these principles and to extract patterns of their use through a case study on a popular open-source software system. Our evaluation shows that HD reveals six crosscutting functionalities. The refactoring of these concerns yield 36 different configurations of the Prevayler system which were previously not possible. The refactoring also reduces the core Prevayler code size by 53%, demonstrates a decrease of coupling between core functionality components by 43%, and reduces the lack of cohesion of the core system by 71%. Given the heterogeneous nature of crosscutting displayed in Prevayler, the size and separation of concern metrics have not reduced for the overall refactored system, i.e., for the core composed with the aspects. A posterior analysis of the re-engineering process reveals 22 refactoring patterns that could be readily used by an automatic aspect refactoring tool.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".