A Tale of Two Case Studies: A Unified Exploration of Rust Verification with SEABMC
Bibliographic record
Abstract
The Rust type system provides strong compile-time guarantees.However, some properties cannot be fully verified by the compiler.S pecifically,p roperties likep anic freedom and memory safety in mixed safe-unsafe code requirev erification beyond what the language enforces.We exploreh ow to verify these properties in real-world Rust code using SEABMC, a bounded model checker that ingests LLVM-IR generated by the Rust compiler.W ed emonstrate our approach throught wo case studies.In the first, we develop unit proofs forf unctional properties of four data-structurelibraries: SMALLVEC, TINYVEC, SEAV EC,a nd RESULT-TYPE from the Rust standard library.These unit proofs arec heckable by both SEABMC and KANI, as tate-of-the-art bounded model checker forR ust, and we find that SeaBMC verifies these units an order of magnitude faster than Kani.The second case study focuses on verifying panic freedom of Wasmtime'sW INCH compiler.T his applicationi s drivenb yt he requirement forh igh reliability when compiling WA SM smart-contracts in the Stellarn etwork.This case study highlights that executable counterexamples from SEABMCa re highly effective forl ocalizing issues and discovering invariants.Our main contributions are( 1) an ew tool forR ust verification which, on our benchmarks, is an order of magnitude faster than KANI,( 2) twoc ase studies with reusable benchmarking and testing infrastructure, and (3) practical guidelines stemming from our experience verifying real-world code-bases.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".