Bibliographic record
Abstract
Precise pointer information is essential for dataflow analysis. While flow-sensitive pointer analysis yields highly precise results, it introduces more computations. To provide a better trade-off between precision and performance, prior work has developed LEVPA, a pointer analysis that processes pointers according to their pointer level, generally accelerating the analysis. However, LevPA still performs redundant computations for intermediate variables that are generated during Static Single Assignment (SSA) transformation. For real-world programs, we have observed that those variables may constitute up to 87 % of all analyzed variables. These unnecessary computations cause LEVPA to timeout for large programs. To address these limitations, we present Level-based Intermediate variable Skipping Pointer Analysis (LisPA), an enhancement of LevPA that operates on a def-use graph without computing points-to set of intermediate variables introduced during SSA transformation. The key insight behind LISPA is that intermediate variables do not introduce new pointers into the analysis, because they always alias with variables in the original program. Therefore, there is no need to compute their points-to set in a fixed-point computation. Across 27 real-world C/C++ programs, we show that LISPA is on average$2.14 \times$faster than LevPA, while using$2.38 \times$less memory.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".