Optimising aspectJ
Bibliographic record
Abstract
AspectJ, an aspect-oriented extension of Java, is becoming increasingly popular. However, not much work has been directed at optimising compilers for AspectJ. Optimising AOP languages provides many new and interesting challenges for compiler writers, and this paper identifies and addresses three such challenges.First, compiling around advice efficiently is particularly challenging. We provide a new code generation strategy for around advice, which (unlike previous implementations) both avoids the use of excessive inlining and the use of closures. We show it leads to more compact code, and can also improve run-time performance. Second, woven code sometimes includes run-time tests to determine whether advice should execute. One important case is the cflow pointcut which uses information about the dynamic calling context. Previous techniques for cflow were very costly in terms of both time and space. We present new techniques to minimise or eliminate the overhead of cflow using both intra- and inter-procedural analyses. Third, we have addressed the general problem of how to structure an optimising compiler so that traditional analyses can be easily adapted to the AOP setting.We have implemented all of the techniques in this paper in abc , our AspectBench Compiler for AspectJ, and we demonstrate significant speedups with empirical results. Some of our techniques have already been integrated into the production AspectJ compiler, ajc 1.2.1.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".