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Not on the strength of AI-generated code alone. Tests and type checks can provide useful evidence, but they do not establish every property a program is supposed to have. A formal proof can give stronger assurance about a precisely stated property of analyzed code—provided the specification is right, the proof actually covers that code, and the assumptions and verification tools are understood. Bend 2 and Ada/SPARK offer different ways to seek that assurance; neither turns a green result into a guarantee that an entire system is correct or secure.
What does “trust” mean when AI writes the code?
Trust is not a yes-or-no property attached to code because a model produced it. The useful question is: Which claim about this code has been checked, against what specification, and within what boundary?
For example, a team might want to establish that a function never divides by zero, that it returns a result satisfying a stated postcondition, or that certain data-flow constraints hold. A proof can support such a claim for the analyzed program under the tool’s assumptions. It does not establish that the claim captures the real requirement, that unexamined dependencies behave correctly, or that the program has no other defects.
- Requirement: What must the software do or avoid doing?
- Specification: How is that requirement expressed as a property the tool can analyze?
- Proof boundary: Which source code, components, and interfaces are included?
- Trusted base: Which checker, kernel, compiler, libraries, and assumptions does the conclusion rely on?
AI changes who may produce the code and annotations; it does not remove the need to answer these questions. A model can generate an incorrect implementation, an incomplete specification, or both.
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What a proof establishes—and what it does not
A successful proof establishes the proposition that was encoded, for the analyzed code and within the verification model. The proposition is not automatically the same as “the software does what users need.” People still have to choose relevant properties, formalize them accurately, and check that the modeled code and assumptions match the system they intend to deploy.
A proof result is therefore evidence with a scope, not a universal certificate. It does not by itself establish fitness for purpose, security against every threat, correctness of unverified dependencies, or the absence of every possible bug. Testing, review, integration checks, and security analysis remain relevant for properties outside the proof boundary.
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Tests and type checks are useful, but answer different questions
Tests show how a program behaved on selected inputs and conditions; they cannot alone cover every possible execution. Type checking rules out certain classes of mistakes according to a language’s type system, but it does not usually prove that a function meets a detailed behavioral requirement. Formal verification can target stronger properties, but only when those properties are expressed and the analysis succeeds.
An unproved obligation is not a pass
If a prover cannot discharge a check, that does not automatically mean the code is wrong: the property may be difficult for the prover, the specification may need strengthening, or an invariant may be missing. But it also does not mean the code is safe. Treat the obligation as unresolved until the team investigates it—by correcting a defect, improving the specification or proof, or documenting a justified limitation. Do not silently count an unproved check as verified.
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How Bend 2 and Ada/SPARK approach verification
Both approaches support formal reasoning, but they use different languages and workflows. Bend’s project documentation describes Bend 2 as a new language built around laws and corresponding proof code for checked properties. Ada/SPARK uses Ada contracts and SPARK annotations with GNATprove to analyze selected properties of code marked for analysis.
| Question | Bend 2 | Ada/SPARK |
|---|---|---|
| How are properties expressed? | Laws are written in Bend, with corresponding proof code required for checked properties, according to Bend project documentation. | Ada contracts and SPARK annotations can specify preconditions, postconditions, data flow, and other properties for GNATprove, as described by AdaCore. |
| What can analysis establish? | That checked laws hold for the modeled code when the relevant proof succeeds and the checker and assumptions are trusted. | For analyzed SPARK code, GNATprove can analyze flow and initialization, prove targeted run-time safety properties, and check specified contracts under its analysis assumptions. |
| What remains a human responsibility? | Choose relevant laws, formalize them correctly, inspect assumptions and coverage, and address what is outside the proof. | Mark code for analysis, state relevant contracts, add invariants where needed, inspect assumptions, and resolve or account for unproved checks. |
| What maturity caveat applies? | The Bend project calls Bend 2 new and lists limitations. It says the checker itself has no proof, while --verdict uses a proven kernel. |
AdaCore documents a contract-based workflow, while also noting prover limitations, unsupported properties, and that stronger functional proofs can take substantial effort. |
Bend 2: laws and proof code in a new language
Bend’s project documentation explicitly calls Bend 2 “a new language” and describes limitations and missing ecosystem features. That matters when evaluating assurance: the project distinguishes its checker, which it says has no proof, from the proven kernel used by --verdict. A team should understand which part of its verification path relies on which component rather than treating any proof-related command as a blanket guarantee.
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The laws still have to express the properties the team cares about. Successful proofs of selected laws do not show that omitted requirements hold, nor do they automatically cover everything the software depends on.
Ada/SPARK: contracts and GNATprove analysis
SPARK is an Ada subset used with a contract-based verification workflow. AdaCore describes two aims: proving the absence of run-time errors and proving functional correctness of a piece of code. The scope depends on the analyzed code and the properties expressed in contracts; it is not an assertion that every program property is proved automatically.
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AdaCore’s practice guidance describes analysis of flow and initialization as well as targeted run-time safety properties. Functional correctness requires relevant contracts, and loops may need invariants to make the desired reasoning possible. The guidance also notes limits: some properties are hard to express, prover heuristics can fail, and the stated analysis guarantee does not include every kind of run-time error, such as Storage_Error.
How to evaluate an AI-generated proof result
- Name the property. Write down the concrete claim that matters—such as a postcondition or the absence of a specific class of run-time failure. “The AI code is correct” is not a precise proof target.
- Check the specification against the requirement. Confirm that the contract or law represents the intended behavior, including edge cases. A proof of a mistaken or incomplete specification can still succeed.
- Identify the analyzed boundary. Determine which code was analyzed and which dependencies, libraries, external interfaces, generated components, or runtime behavior are outside it.
- Inspect assumptions and trusted tools. Understand the checker or kernel, the assumptions used in the analysis, and what is or is not itself verified. For Bend, the project distinguishes its unproven checker from the proven kernel used by
--verdict. - Review every unresolved result. A failed or unproved obligation is not proof of a defect, but it is not a pass. Investigate it and make the remaining uncertainty explicit.
- Use other evidence for other claims. Test behavior that is not covered by the proof, review interfaces and dependencies, and assess security and operational risks that the formalized property does not address.
What the AI-and-SPARK figures do—and do not—show
Two reported results illustrate why verification evidence needs its context attached. They are not directly comparable and do not establish a general success rate for AI-generated software.
- 50.7% of benchmark cases: In a 2025 SciTePress paper, the authors report that Marmaragan with GPT-4o generated correct SPARK annotations in 50.7% of the cases in their benchmark. This is a result for that system and benchmark, not the probability that arbitrary AI-written code is correct or production-ready.
- 49,280 discharged proof obligations: A 2026 arXiv preprint, “The Prover Is the Judge,” reports this count for its verifier-driven Ada/SPARK project. The paper describes functional correctness for selected primitives and absence of run-time errors for the rest of that project; the count is evidence about its stated software scope, not a universal trust score.
Bend’s official site publishes project benchmark examples, but the material identified here does not provide an independent comparative evaluation of checker correctness or speed. Those examples should be treated as project claims, not third-party validation. Nor is there a controlled Bend-versus-SPARK trial in the evidence considered here, so these sources do not justify declaring one categorically superior.
Choosing between Bend 2 and SPARK
Start with the code you need to verify and the language ecosystem your team can realistically support. Bend 2 is a new language designed around laws and proof; SPARK offers an Ada subset and a GNATprove contract workflow. The choice is about fit, verification scope, and team capability—not a demonstrated universal ranking.
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Quick Recap
- Consider Bend 2 if its language and law-based approach fit the project, and the team is prepared to work within the limitations and ecosystem described by the Bend project.
- Consider Ada/SPARK if the project fits Ada and the team can write and maintain contracts, annotations, and any needed invariants while handling GNATprove’s limits and unresolved checks.
- In either case, plan for people to validate requirements, review specifications and assumptions, and assess code and system parts that are not verified.
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