Impact of Aspect-Oriented Programming on Software Performance: A Case Study of Leader/Followers and Half-Sync/Half-Async Architectures
Bibliographic record
Abstract
The aim of this work is to measure and analyze the impact of aspect-oriented programming on software performance. Thus we hypothesized as follow: adding aspects to the original base program will affect its performance because of the overhead caused by the control flow switching, and that incremental effect on performance is more obvious as the number of join points increases. To confirm (or reject) our hypotheses we carried out a case study of two concurrent software architectures: Half-Sync/Half-Asyn (HS/HA) and Leader/Followers (LFs). Aspects were extracted and encapsulated, and the aspect-enabled program was compared to the base program for performance. Our results show that aspect-oriented approach does not have significant effect on the performance and that in some cases, aspect-oriented program even outperform the non-aspect program. Additionally, introduction of a large number of joint points does not have significant effect on the performance.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".