Designing and implementing different use cases of aspect-oriented programming with AspectJ for developing mobile applications
Bibliographic record
Abstract
The separation of concerns as a conceptual paradigm, aims to manage the complexity of the software systems by dividing them into different concerns and aspects. The benefits of this paradigm such as adaptability, reuse and maintenance, have been key drivers of its adoption and usability. These quality attributes have been discussed in aspect-oriented programming (AOP) which is a complement to traditional programming methods, whether object-oriented programming or procedural programming. The main concept of AOP is to gather the treatments related to a specific concern (problem) in a centralized unit called aspect. In this paper, two case studies of AOP are conducted with AspectJ: i) through three different implementations, addressing three distinct issues: Logging, Adding features and Using the Observer design pattern, and ii) through the design of aspect mobile application that is easy to use and quickly accessible and especially through Android devices. Also, the different technologies of AspectJ are used to design our application and illustrate the modularity and the composition of components (database manager, development software, and plugins). For each case study, we present the advantages and disadvantages to better understand and decompose the applications using this programming paradigm. Potential implementation examples are provided to support the explanation of the different situations and justify the use of the AOP to solve these issues. OSGi framework can also be used in the future work to offer dynamic manner of mobile applications.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".