Can we refactor conditional compilation into aspects
Bibliographic record
Abstract
Systems software uses conditional compilation to manage cross-cutting concerns in a very fine-grained and efficient way, but at the expense of tangled and scattered conditional code. Refactor-ing of conditional compilation into aspects gets rid of these issues, but it is not clear yet for which patterns of conditional compila-tion aspects make sense and whether or not current aspect tech-nology is able to express these patterns. To investigate these two problems, this paper presents a graphical “preprocessor blueprint” model which offers a queryable representation of the syntactical interaction of conditional compilation and the source code. A case study on the Parrot VM shows that preprocessor blueprints are able to express and query for the four commonly known patterns of con-ditional compilation usage, and that they allow to discover seven additional important patterns. By correlating each pattern’s poten-tial for refactoring into advice and each pattern’s evolution of the number of occurrences, we show that refactoring into advice in the Parrot VM is a good alternative for three of the eleven patterns, whereas for the other patterns trade-offs have to be considered be-tween robustness and fine-grainedness of the advice.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".