Abstract We present —a Dafny-style verifier for imperative programs embedded in the Lean proof assistant. Like Dafny, supports reasoning about effectful programs featuring mutable state, loops, and non-determinism. Unlike Dafny, seamlessly combines automated SMT-based proofs with the interactive proof mode of the Lean proof assistant, in which it is embedded, thus enabling multi-modal proofs. Implemented as a Lean library, enjoys interaction with the rest of the Lean ecosystem, and in particular, with its automation tactics and rich library of mathematical theories. In this paper, we give a tour of ’s features, outline the techniques underlying its implementation, and evaluate its performance and expressivity in comparison with Dafny.

Intermediate Verification Languages (IVLs) play the same role in verification as Intermediate Representations in compilation, a layer that separates a verifier’s language-specific front-end from its logic automation back-end. Successful IVL tools such as Boogie, Why3, and Viper generate Verification Conditions (VCs) that are sent to an SMT solver. The verifier output can be trusted only if these VCs are sound with respect to the formal semantics of the IVL. Formalizing the semantics of IVLs and verifying the soundness of corresponding VC Generators with respect to this semantics is challenging if one wants to model realistic features of IVLs such as mutually recursive definitions, lexical variable and control-flow labeled scopes, interpreted and uninterpreted functions, and unbounded loops. B3 is a new IVL. This paper presents a formalization of B3’s semantics, a VC Generator for the language, and a soundness proof that these two correspond. A key practical contribution of this work is that all three components are authored in the Dafny programming language and verifier. This makes it easy for a tool maintainer to maneuver between the semantic definitions, the proofs, and the VCG’s executable code. The key theoretical contribution of the work is a methodology to split the IVL’s semantic encodings into two layers of abstraction to cover realistic aspects of the semantics, while keeping the proofs amenable to automation. Optimized for Dafny-style automation, the first layer is used to verify the correctness of the VC Generator procedure. Optimized for expressiveness, the second layer is used to capture the semantics in a natural way.

Separation Logic is an established formalism for deductive verification of heap-manipulating programs. Proofs of symbolic heap entailment, an analogue of the ordinary logical implication, are amongst the most common reasoning steps in Separation Logic, and many existing heap verifiers provide automation for discharging valid heap entailments. We observe that existing techniques for automating entailment proofs in foundational Separation Logic verifiers embedded into provers such as Rocq, suffer from three main drawbacks: (a) poor performance due to metaprogramming overhead, (b) limited expressivity, and (c) restricted extensibility. To address these shortcomings, we propose lazy proof automation - an approach to entailment proofs inspired by translation validation. Our key idea is to implement an entailment checker as a combination of (1) an efficient but unverified prover, suitable for fast-paced interactive proofs, and (2) a proof reconstruction procedure that takes the prover’s trace and produces a certificate of entailment validity that can be checked a posteriori. We implemented these ideas in Yolo - a generic and extensible heap entailment prover built in Lean. We instantiate Yolo for two Lean-embedded Separation Logics and show its practical benefits, both in terms of user experience and proof-checking speed, compared with the automation available in state-of-the-art foundational Separation Logics.

Multi-modal program verification is a process of validating code against its specification using both dynamic and symbolic techniques, and proving its correctness by a combination of automated and interactive machine-assisted tools. In order to be trustworthy, such verification tools must themselves come with formal soundness proofs, establishing that any program verified in them against a certain specification does not violate the specification’s statement when executed. Verification tools that are proven sound in a general-purpose proof assistant with a small trusted core are commonly referred to as foundational . We present a framework that facilitates and streamlines construction of program verifiers that are both foundational and multi-modal. Our approach adopts the well-known idea of monadic shallow embedding of an executable program semantics into the programming language of a theorem prover based on higher-order logic, in our case, the Lean proof assistant. We provide a library of monad transformers for such semantics, encoding a variety of computational effects, including state, divergence, exceptions, and non-determinism. The key theoretical innovation of our work are monad transformer algebras that enable automated derivation of the respective sound verification condition generators. We show that proofs of the resulting verification conditions enjoy automation using off-the-shelf SMT solvers and allow for an interactive proof mode when automation fails. To demonstrate versatility of our framework, we instantiated it to embed two foundational multi-modal verifiers into Lean for reasoning about (1) distributed protocol safety and (2) Dafny-style specifications of imperative programs, and used them to mechanically verify a number of non-trivial case studies.

Abstract We present , an open-source framework for automated and interactive verification of transition systems, aimed specifically at conducting machine-assisted proofs about concurrent and distributed algorithms. is implemented on top of the proof assistant. It allows one to describe a transition system and its specification in a simple imperative language, producing verification conditions in first-order logic, to be discharged automatically via a range of SMT solvers. In case automated verification fails or if the system’s description requires statements in a higher-order logic, provides an interactive verification mode, by virtue of being embedded in a general-purpose proof assistant. We have evaluated on a large set of case studies from the distributed system verification literature, showing that its automated verification performance is acceptable for practical verification tasks, while it also allows for seamless automated/interactive verification of system specifications beyond the reach of existing automated provers.

Arrays are a fundamental abstraction to represent collections of data. It is often possible to exploit structural properties of the data stored in an array ( e.g ., repetition or sparsity) to develop a specialised representation optimised for space efficiency. Formally reasoning about correctness of manipulations with such structured data is challenging, as they are often composed of multiple loops with non-trivial invariants. In this work, we observe that specifications for structured data manipulations can be phrased as hypersafety properties, i.e ., predicates that relate traces of k programs. To turn this observation into an effective verification methodology, we developed the Logic for Graceful Tensor Manipulation (LGTM), a new Hoare-style relational separation logic for specifying and verifying computations over structured data. The key enabling idea of LGTM is that of parametrised hypersafety specifications that allow the number k of the program components to depend on the program variables . We implemented LGTM as a foundational embedding into Coq, mechanising its rules, meta-theory, and the proof of soundness. Furthermore, we developed a library of domain-specific tactics that automate computer-aided hypersafety reasoning, resulting in pleasantly short proof scripts that enjoy a high degree of reuse. We argue for the effectiveness of relational reasoning about structured data in LGTM by specifying and mechanically proving correctness of 13 case studies including computations on compressed arrays and efficient operations over multiple kinds of sparse tensors.