Points-to analysis with efficient strong updates
Why this work is in the frame
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Bibliographic record
Abstract
This paper explores a sweet spot between flow-insensitive and flow-sensitive subset-based points-to analysis. Flow-insensitive analysis is efficient: it has been applied to million-line programs and even its worst-case requirements are quadratic space and cubic time. Flow-sensitive analysis is precise because it allows strong updates, so that points-to relationships holding in one program location can be removed from the analysis when they no longer hold in other locations. We propose a "Strong Update" analysis combining both features: it is efficient like flow-insensitive analysis, with the same worst-case bounds, yet its precision benefits from strong updates like flow-sensitive analysis. The key enabling insight is that strong updates are applicable when the dereferenced points-to set is a singleton, and a singleton set is cheap to analyze. The analysis therefore focuses flow sensitivity on singleton sets. Larger sets, which will not lead to strong updates, are modelled flow insensitively to maintain efficiency. We have implemented and evaluated the analysis as an extension of the standard flow-insensitive points-to analysis in the LLVM compiler infrastructure.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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