Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper reports on a new approach to solving a subset-based points-to analysis for Java using Binary Decision Diagrams (BDDs). In the model checking community, BDDs have been shown very effective for representing large sets and solving very large verification problems. Our work shows that BDDs can also be very effective for developing a points-to analysis that is simple to implement and that scales well, in both space and time, to large programs.The paper first introduces BDDs and operations on BDDs using some simple points-to examples. Then, a complete subset-based points-to algorithm is presented, expressed completely using BDDs and BDD operations. This algorithm is then refined by finding appropriate variable orderings and by making the algorithm propagate sets incrementally, in order to arrive at a very efficient algorithm. Experimental results are given to justify the choice of variable ordering, to demonstrate the improvement due to incrementalization, and to compare the performance of the BDD-based solver to an efficient hand-coded graph-based solver. Finally, based on the results of the BDD-based solver, a variety of BDD-based queries are presented, including the points-to query.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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