Using XBDDs and ZBDDs in points‐to analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Binary decision diagrams (BDDs) have recently become widely accepted as a space‐efficient method of representing relations in points‐to or reference analyses. When BDDs are used to represent relations, each element of a domain is assigned a bit pattern to represent it, but not every bit pattern represents an element. The circuit design, model checking, and verification communities have achieved significant reductions in BDD sizes using several techniques to reduce the overhead of these don't‐care bit patterns. We adapt these techniques to BDD‐based program analysis, and we study their effect on the BDD size in this context. Specifically, we compare the effectiveness of Coudert and Madre's restrict operation and the use of zero‐suppressed BDDs (ZBDDs) to represent relations. Using don't‐care BDDs (XBDDs) and ZBDDs to reduce the size of the relations allows a compiler or other software analysis tools to analyze larger programs with greater precision. Our experimental evaluation considers both context‐insensitive and context‐sensitive program analyses. We also provide a metric that can be used to estimate whether ZBDDs will be more compact than BDDs for a given analysis. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it