Bibliographic record
Abstract
One of the major challenges in aspect-oriented programming is that aspects may have unintended impacts on a base program. Thus, it is important to develop techniques and tools that can both summarize the impacts and provide information about the causes of the impacts. This thesis presents impact analyses for AspectJ. Our approach detects different ways advice and inter-type declarations interact and interfere with the base program and focuses on four kinds of impacts, \emph{state impacts} which cause changes of state in the base program, \emph{computation impacts} which cause changes in functionality by adding, removing or replacing computations of the base program, \emph{shadowing impacts} which cause changes of field reference in the base program, and \emph{lookup impacts} which cause changes of method lookup in the base program. We provide a classification scheme for these kinds of impacts and then develop a set of static analyses to estimate these impacts. A key feature of our approach is the use of points-to analysis to provide more accurate estimates. Further, our analysis results allow us to trace back to find the causes of the impacts. We have implemented our techniques in the AspectBench compiler. By implementing them in an AspectJ compiler, all kinds of pointcuts, advice and inter-type declarations can be analyzed. We also have integrated these analyses into an AspectJ IDE and provided a two-way navigation between impacts and program source code. In addition, we have carried out experiments on example programs and benchmarks to investigate the results of our analyses.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".