Bibliographic record
Abstract
Object-sensitivity has emerged as an excellent context abstraction for points-to analysis in object-oriented languages. Despite its practical success, however, object-sensitivity is poorly understood. For instance, for a context depth of 2 or higher, past scalable implementations deviate significantly from the original definition of an object-sensitive analysis. The reason is that the analysis has many degrees of freedom, relating to which context elements are picked at every method call and object creation. We offer a clean model for the analysis design space, and discuss a formal and informal understanding of object-sensitivity and of how to create good object-sensitive analyses. The results are surprising in their extent. We find that past implementations have made a sub-optimal choice of contexts, to the severe detriment of precision and performance. We define a "full-object-sensitive" analysis that results in significantly higher precision, and often performance, for the exact same context depth. We also introduce "type-sensitivity" as an explicit approximation of object-sensitivity that preserves high context quality at substantially reduced cost. A type-sensitive points-to analysis makes an unconventional use of types as context: the context types are not dynamic types of objects involved in the analysis, but instead upper bounds on the dynamic types of their allocator objects. Our results expose the influence of context choice on the quality of points-to analysis and demonstrate type-sensitivity to be an idea with major impact: It decisively advances the state-of-the-art with a spectrum of analyses that simultaneously enjoy speed (several times faster than an analogous object-sensitive analysis), scalability (comparable to analyses with much less context-sensitivity), and precision (comparable to the best object-sensitive analysis with the same context depth).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".