Separating Crosscutting Concerns Across the Lifecycle: From Composition Patterns to AspectJ and Hyper/J
Bibliographic record
Abstract
Requirements that have a crosscutting impact on software (such as distribution or persistence) present many problems for software development that manifest themselves throughout the lifecycle. Inherent properties of crosscutting requirements, such as scattering (where their support is scattered across multiple classes) and tangling (where their support is tangled with elements supporting other requirements), reduce the reusability, extensibility, and traceability of the affected software artefacts. Scattering and tangling exist both in designs and code and must therefore be addressed in both. To remove scattering and tangling properties, a means to separate the designs and code of crosscutting behaviour into independent models or programs is required. This paper discusses approaches that achieve exactly that in either designs or code, and presents an investigation into a means to maintain this separation of crosscutting behaviour seamlessly across the lifecycle. To achieve this, we work with composition patterns at the design level, AspectJ and Hyper/J at the code level, and investigate a mapping between the two levels. Composition patterns are a means to separate the design of crosscutting requirements in an encapsulated, independent, reusable, and extensible way. AspectJ and Hyper/J are technologies that provide similar levels of separation for Java code. We discuss each approach, and map the constructs from composition patterns to those of AspectJ and Hyper/J. We first illustrate composition patterns with the design of the Observer pattern, and then map that design to the appropriate code. As this is achieved with varying levels of success, the exercise also serves as a case study in using those implementation techniques. Keywords Composition patterns, subject-or...
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".