Bibliographic record
Abstract
Points-to analysis for Java benefits greatly from context sensitivity. CFL-reachability and k -limited context strings are two approaches to obtaining context sensitivity with different advantages: CFL-reachability allows local reasoning about data-value flow and thus is suitable for demand-driven analyses, whereas k -limited analyses allow object sensitivity which is a superior calling context abstraction for object-oriented languages. We combine the advantages of both approaches to obtain a context-sensitive analysis that is as precise as k -limited context strings, but is more efficient to compute. Our key insight is based on a novel abstraction of contexts adapted from CFL-reachability that represents a relation between two calling contexts as a composition of transformations over contexts. We formulate pointer analysis in an algebraic structure of context transformations, which is a set of functions over calling contexts closed under function composition. We show that the context representation of context-string-based analyses is an explicit enumeration of all input and output values of context transformations. CFL-reachability-based pointer analysis is formulated to use call-strings as contexts, but the context transformations concept can be applied to any context abstraction used in k -limited analyses, including object- and type-sensitive analysis. The result is a more efficient algorithm for computing context-sensitive results for a wide variety of context configurations.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".