Anindya Banerjee

Verifying relations between programs arises as a task in various verification contexts, such as optimizing transformations, relating new versions of programs with older versions (regression verification), and noninterference. However, relational verification for programs acting on dynamically allocated mutable state is not well supported by existing tools, which provide a high level of automation at the cost of restricting the programs considered. Auto-active tools, on the other hand, require more user interaction, but enable verification of a broader class of programs. This article presents WhyRel, a tool for the auto-active verification of relational properties of pointer programs based on relational region logic. WhyRel is evaluated through verification case studies, relying on SMT solvers orchestrated by the Why3 platform on which it builds. Case studies include establishing representation independence of ADTs, showing noninterference, and challenge problems from recent literature.

We present Zar: a formally verified compiler pipeline from discrete probabilistic programs with unbounded loops in the conditional probabilistic guarded command language (cpGCL) to proved-correct executable samplers in the random bit model. We exploit the key idea that all discrete probability distributions can be reduced to unbiased coin-flipping schemes. The compiler pipeline first translates cpGCL programs into choice-fix trees, an intermediate representation suitable for reduction of biased probabilistic choices. Choice-fix trees are then translated to coinductive interaction trees for execution within the random bit model. The correctness of the composed translations establishes the sampling equidistribution theorem: compiled samplers are correct wrt. the conditional weakest pre-expectation semantics of cpGCL source programs. Zar is implemented and fully verified in the Coq proof assistant. We extract verified samplers to OCaml and Python and empirically validate them on a number of illustrative examples.

Verifying relations between programs arises as a task in various verification contexts such as optimizing transformations, relating new versions of programs with older versions (regression verification), and noninterference. However, relational verification for programs acting on dynamically allocated mutable state is not well supported by existing tools, which provide a high level of automation at the cost of restricting the programs considered. Auto-active tools, on the other hand, require more user interaction but enable verification of a broader class of programs. This article presents WhyRel, a tool for the auto-active verification of relational properties of pointer programs based on relational region logic. WhyRel is evaluated through verification case studies, relying on SMT solvers orchestrated by the Why3 platform on which it builds. Case studies include establishing representation independence of ADTs, showing noninterference, and challenge problems from recent literature.

Verifying relations between programs arises as a task in various verification contexts such as optimizing transformations, relating new versions of programs with older versions (regression verification), and noninterference. However, relational verification for programs acting on dynamically allocated mutable state is not well supported by existing tools, which provide a high level of automation at the cost of restricting the programs considered. Auto-active tools, on the other hand, require more user interaction but enable verification of a broader class of programs. This article presents WhyRel, a tool for the auto-active verification of relational properties of pointer programs based on relational region logic. WhyRel is evaluated through verification case studies, relying on SMT solvers orchestrated by the Why3 platform on which it builds. Case studies include establishing representation independence of ADTs, showing noninterference, and challenge problems from recent literature.

Dedicated to Tony Hoare. In a paper published in 1972, Hoare articulated the fundamental notions of hiding invariants and simulations. Hiding: invariants on encapsulated data representations need not be mentioned in specifications that comprise the API of a module. Simulation: correctness of a new data representation and implementation can be established by proving simulation between the old and new implementations using a coupling relation defined on the encapsulated state. These results were formalized semantically and for a simple model of state, though the paper claimed this could be extended to encompass dynamically allocated objects. In recent years, progress has been made toward formalizing the claim, for simulation, though mainly in semantic developments. In this article, hiding and simulation are combined with the idea in Hoare’s 1969 paper: a logic of programs. For an object-based language with dynamic allocation, we introduce a relational Hoare logic with stateful frame conditions that formalizes encapsulation, hiding of invariants, and couplings that relate two implementations. Relations and other assertions are expressed in first-order logic. Specifications can express a wide range of relational properties such as conditional equivalence and noninterference with declassification. The proof rules facilitate relational reasoning by means of convenient alignments and are shown sound with respect to a conventional operational semantics. A derived proof rule for equivalence of linked programs directly embodies representation independence. Applicability to representative examples is demonstrated using an SMT-based implementation.

We present a formal proof in Lean of probably approximately correct (PAC) learnability of the concept class of decision stumps. This classic result in machine learning theory derives a bound on error probabilities for a simple type of classifier. Though such a proof appears simple on paper, analytic and measure-theoretic subtleties arise when carrying it out fully formally. Our proof is structured so as to separate reasoning about deterministic properties of a learning function from proofs of measurability and analysis of probabilities.