Research Report: Not All Move Specifications Are Created Equal : A Case Study on the Formally Verified Diem Payment Network
Bibliographic record
Abstract
Software developers who are newly introduced to formal verification often have subtle but distinct interpretations on the roles of specifications (specs). Unsurprisingly, these different interpretations affect the types of specs developers tend to write, which, without coordination, can lead to fragmented assurance guarantee and ultimately impairs the overall effectiveness of the entire formal verification effort. This paper is an experience report on rolling out a formal verification system in a smart contract setting (where correctness matters financially). In this process, we discover three popular views about what “a spec is operationally” among experienced industrial developers yet novice in formal methods: 1) specs are contracts between implementation and functionalities conveyed to end users; 2) specs are extensions to the type system; and 3) specs are definitions of high-level state machines. While which interpretation is closer to the real purpose(s) of specs is a judgement call, one important view is missing: some specs are abstracting specs that lock in requirements or intention (hence the more the better), some specs are proof assistance that facilitates the refinement proof between implementation and abstracting specs and hence, should be written on an as-need basis; while other specs are denotational specs and do not add assurance from a formal methods perspective. Absence this distinction, it is easy to fall into a trap in formal verification where specs accumulate but little assurance is added holistically.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".