Efficient bottom-up heap analysis for symbolic path-based data access summaries
Why this work is in the frame
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Bibliographic record
Abstract
We propose a heap analysis for extracting data access summaries based on symbolic access paths (SAPs) of methods in object-oriented languages. The analysis takes advantage of the insight that typical programs access dynamic data structures in regular manners. We combine this insight with a bottom-up approach that computes a local summary for each basic block, loop, and method in the program, which is then encapsulated into an abstract block in order to efficiently handle the higher levels of the analysis. We solve the problem of the dependence of local analysis results on the global heap aliasing by inferring the sets of aliases on which the correctness of the local results is predicated. Experimental evaluation for Java shows that for typical programs that use dynamic data structures, our analysis runs in a fast single pass and produces useful results.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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