Verification methods for memory-manipulating C programs need to address not only well-typed programs that respect invariants such as the split-heap memory model, but also programs that access through pointers arbitrary memory objects such as local variables, single struct fields, or array slices. We present a logic for memory layouts that covers these applications and show how proof obligations arising during the verification can be discharged automatically using the layouts. The framework developed in this way is also suitable for reasoning about data structures manipulated by algorithms, which we demonstrate by verifying the Schorr-Waite graph marking algorithm.

Reasoning about Memory Layouts

Recursive calls over recursive data are useful for generating probability distributions, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful, but unfortunately, existing probabilistic programming languages do not perform exact inference on recursive calls over recursive data, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wide variety of recursion can be expressed naturally, and inference carried out exactly. For instance, probabilistic pushdown automata and their generalizations are easy to express, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types using program transformations related to defunctionalization and refunctionalization. These transformations are assured correct by a linear type system, and a successful choice of transformations, if there is one, is guaranteed to be found by a greedy algorithm.

/pdf/exact-recursive-probabilistic-programming-15cdkwl0.pdf

Exact Recursive Probabilistic Programming

We characterize the relation between generalized algebraic datatypes (GADTs) with pattern matching on their constructors one hand, and generalized algebraic co-datatypes (GAcoDTs) with copattern matching on their destructors on the other hand: GADTs can be converted mechanically to GAcoDTs by refunctionalization, GAcoDTs can be converted mechanically to GADTs by defunctionalization, and both defunctionalization and refunctionalization correspond to a transposition of the matrix in which the equations for each constructor/destructor pair of the (co-)datatype are organized. We have defined a calculus, \(GADT^T\), which unifies GADTs and GAcoDTs in such a way that GADTs and GAcoDTs are merely different ways to partition the program.

/pdf/dualizing-generalized-algebraic-data-types-by-matrix-agfdbmdoeh.pdf

Dualizing Generalized Algebraic Data Types by Matrix Transposition

Traditionally, formal languages are defined as sets of words. More recently, the alternative coalgebraic or coinductive representation as infinite tries, i.e., prefix trees branching over the alphabet, has been used to obtain compact and elegant proofs of classic results in language theory. In this article, we study this representation in the Isabelle proof assistant. We define regular operations on infinite tries and prove the axioms of Kleene algebra for those operations. Thereby, we exercise corecursion and coinduction and confirm the coinductive view being profitable in formalizations, as it improves over the set-of-words view with respect to proof automation.

pdf/formal-languages-formally-and-coinductively-1qy01prvwr.pdf

Formal Languages, Formally and Coinductively

The expression problem describes a fundamental trade-off in program design: Should a program's primary decomposition be determined by the way its domain objects are constructed ("functional" decomposition), or by the way they are destructed ("object-oriented" decomposition)? We argue that programming languages should not force one of these decompositions on the programmer; rather, a programming language should support both ways of decomposing a program in a symmetric way, with an easy translation between these decompositions. However, current programming languages are usually not symmetric and hence make it unnecessarily hard to switch the decomposition. We propose a language that is symmetric in this regard and allows a fully automatic translation between "functional" and "object-oriented" decomposition. We present a language with algebraic data types and pattern matching for "functional" decomposition and codata types and copattern matching for "object-oriented" decomposition, together with a bijective translation that turns a data type into a codata type ("destructorization") or vice versa ("constructorization"). We present the first symmetric programming language with support for local (co)pattern matching, which includes local anonymous function or object definitions, that allows an automatic translation as described above. We also present the first mechanical formalization of such a language and prove i) that the type system is sound, that the translations between data and codata types are ii) type-preserving, iii) behavior-preserving and iv) inverses of each other. We also extract a mechanically verified implementation from our formalization and have implemented an IDE with direct support for these translations.

https://dl.acm.org/doi/pdf/10.1145/3371098

Decomposition diversity with symmetric data and codata

Defunctionalization and refunctionalization establish a correspondence between first-class functions and pattern matching, but the correspondence is not symmetric: Not all uses of pattern matching can be automatically refunctionalized to uses of higher-order functions. To remedy this asymmetry, we generalize from first-class functions to arbitrary codata. This leads us to full defunctionalization and refunctionalization between a codata language based on copattern matching and a data language based on pattern matching. We observe how programs can be written as matrices so that they are modularly extensible in one dimension but not the other. In this representation, defunctionalization and refunctionalization correspond to matrix transposition which effectively changes the dimension of extensibility a program supports. This suggests applications to the expression problem.

Automatic refunctionalization to a language with copattern matching: with applications to the expression problem

Functional verification of low-level code requires abstractions over the memory model to be effective, since the number of side-conditions induced by byte- addressed memory is prohibitive even with modern automated reasoners. We pro- pose a flexible solution to this challenge: assertions contain explicit memory layouts which carry the necessary side-conditions as invariants. The memory-related proof obligations arising during verification can then be solved using specialized auto- matic proof procedures. The remaining verification conditions about the content of data structures directly reflect a developer's understanding. The development is formalized in Isabelle/HOL.

/pdf/high-level-proofs-about-low-level-programs-57ihziz65n.pdf

High-level Proofs about Low-level Programs

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Automatic refunctionalization to a language with copattern matching: with applications to the expression problem

High-level Proofs about Low-level Programs