Automatic Inference of Frame Axioms Using Static Analysis
Bibliographic record
Abstract
Many approaches to software verification are currently semi-automatic: a human must provide key logical insights - e.g., loop invariants, class invariants, and frame axioms that limit the scope of changes that must be analyzed. This paper describes a technique for automatically inferring frame axioms of procedures and loops using static analysis. The technique builds on a pointer analysis that generates limited information about all data structures in the heap. Our technique uses that information to over-approximate a potentially unbounded set of memory locations modified by each procedure/loop; this over- approximation is a candidate frame axiom. We have tested this approach on the buffer-overflow benchmarks from ASE 2007. With manually provided specifications and invariants/axioms, our tool could verify/falsify 226 of the 289 benchmarks. With our automatically inferred frame axioms, the tool could verify/falsify 203 of the 289, demonstrating the effectiveness of our approach.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".