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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".