Adding trace matching with free variables to AspectJ
Bibliographic record
Abstract
An aspect observes the execution of a base program; when certain actions occur, the aspect runs some extra code of its own. In the AspectJ language, the observations that an aspect can make are confined to the current action: it is not possible to directly observe the history of a computation.Recently, there have been several interesting proposals for new history-based language features, most notably by Douence et al. and by Walker and Viggers. In this paper, we present a new history-based language feature called tracematches that enables the programmer to trigger the execution of extra code by specifying a regular pattern of events in a computation trace. We have fully designed and implemented tracematches as a seamless extension of AspectJ.A key innovation in our tracematch approach is the introduction of free variables in the matching patterns. This enhancement enables a whole new class of applications in which events can be matched not only by the event kind, but also by the values associated with the free variables. We provide several examples of applications enabled by this feature.After introducing and motivating the idea of tracematches via examples, we present a detailed semantics of our language design, and we derive an implementation from that semantics. The implementation has been realised as an extension of the abc compiler for AspectJ.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".