Bibliographic record
Abstract
Precise, flow-sensitive analyses of pointer relationships often represent each object using the set of local variables that point to it (the alias set), possibly augmented with additional predicates. Many such analyses are difficult to scale due to the size of the abstraction and due to flow sensitivity. The focus of this paper is on efficient representation and manipulation of the alias set. Taking advantage of certain properties of static single assignment (SSA) form, we propose an efficient data structure that allows much of the representations of sets at different points in the program to be shared. The transfer function for each statement, instead of creating an updated set, makes only local changes to the existing data structure representing the set. The key enabling properties of SSA form are that every point at which a variable is live is dominated by its definition, and that the definitions of any set of simultaneously live variables are totally ordered according to the dominance relation. We represent the variables pointing to an object using a list ordered consistently with the dominance relation. Thus, when a variable is newly defined to point to the object, it need only be added to the head of the list. A back edge at which some variables cease to be live requires only dropping variables from the head of the list. We prove that the analysis using the proposed data structure computes the same result as a set-based analysis. We empirically show that the proposed data structure is more efficient in both time and memory requirements than set implementations using hash tables and balanced trees.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".