MUNCH - Automated Reasoner for Sets and Multisets

This system description provides an overview of the MUNCH reasoner for sets and multisets. MUNCH takes as the input a formula in a logic that supports expressions about sets, multisets, and integers. Constraints over collections and integers are connected using the cardinality operator. Our logic is a fragment of logics of popular interactive theorem provers, and MUNCH is the first fully automated reasoner for this logic. MUNCH reduces input formulas to equisatisfiable linear integer arithmetic formulas. MUNCH reasoner is publicly available. It is implemented in the Scala programming language and currently uses the SMT solver Z3 to solve the generated integer linear arithmetic constraints

Combinatory Array Logic with Sums

We prove an NP upper bound on a theory of integer-indexed integer-valued arrays that extends combinatory array logic with an ordering relation on the index set and the ability to express sums of elements. We compare our fragment with seven other fragments in the literature in terms of their expressiveness and computational complexity

Scaling Up Automated Verification: A Case Study and a Formalization IDE for Building High Integrity Software

Component-based software verification is a difficult challenge because developers must specify components formally and annotate implementations with suitable assertions that are amenable to automation. This research investigates the intrinsic complexity in this challenge using a component-based case study. Simultaneously, this work also seeks to minimize the extrinsic complexities of this challenge through the development and usage of a formalization integrated development environment (F-IDE) built for specifying, developing, and using verified reusable software components. The first contribution is an F-IDE built to support formal specification and automated verification of object-based software for the integrated specification and programming language RESOLVE. The F-IDE is novel, as it integrates a verifying compiler with a user-friendly interface that provides a number of amenities including responsive editing for model-based mathematical contracts and code, assistance for design by contract, verification, responsive error handling, and generation of property-preserving Java code that can be run within the F-IDE. The second contribution is a case study built using the F-IDE that involves an interplay of multiple artifacts encompassing mathematical units, component interfaces, and realizations. The object-based interfaces involved are specified in terms of new mathematical models and non-trivial theories designed to encapsulate data structures and algorithms. The components are designed to be amenable to modular verification and analysis

On Decision Procedures for Ordered Collections

We describe a decision procedure for a logic that supports 1) finite collections of elements (sets or multisets), 2) the cardinality operator, 3) a total order relation on elements, and 4) min and max operators on entire collections. Among the applications of this logic are 1) reasoning about the externally observable behavior of data structures such as random access priority queues, 2) specifying witness functions for synthesis problems of set algebra, and 3) reasoning about constraints on orderings arising in termination proofs

Automating Change of Representation for Proofs in Discrete Mathematics (Extended Version)

Representation determines how we can reason about a specific problem. Sometimes one representation helps us find a proof more easily than others. Most current automated reasoning tools focus on reasoning within one representation. There is, therefore, a need for the development of better tools to mechanise and automate formal and logically sound changes of representation. In this paper we look at examples of representational transformations in discrete mathematics, and show how we have used Isabelle's Transfer tool to automate the use of these transformations in proofs. We give a brief overview of a general theory of transformations that we consider appropriate for thinking about the matter, and we explain how it relates to the Transfer package. We show our progress towards developing a general tactic that incorporates the automatic search for representation within the proving process

CONJURE: automatic generation of constraint models from problem specifications

Funding: Engineering and Physical Sciences Research Council (EP/V027182/1, EP/P015638/1), Royal Society (URF/R/180015).When solving a combinatorial problem, the formulation or model of the problem is critical tothe efficiency of the solver. Automating the modelling process has long been of interest because of the expertise and time required to produce an effective model of a given problem. We describe a method to automatically produce constraint models from a problem specification written in the abstract constraint specification language Essence. Our approach is to incrementally refine the specification into a concrete model by applying a chosen refinement rule at each step. Any nontrivial specification may be refined in multiple ways, creating a space of models to choose from. The handling of symmetries is a particularly important aspect of automated modelling. Many combinatorial optimisation problems contain symmetry, which can lead to redundant search. If a partial assignment is shown to be invalid, we are wasting time if we ever consider a symmetric equivalent of it. A particularly important class of symmetries are those introduced by the constraint modelling process: modelling symmetries. We show how modelling symmetries may be broken automatically as they enter a model during refinement, obviating the need for an expensive symmetry detection step following model formulation. Our approach is implemented in a system called Conjure. We compare the models producedby Conjure to constraint models from the literature that are known to be effective. Our empirical results confirm that Conjure can reproduce successfully the kernels of the constraint models of 42 benchmark problems found in the literature.Publisher PDFPeer reviewe

Fractional Collections with Cardinality Bounds, and Mixed Integer Linear Arithmetic with Stars

We present decision procedures for logical constraints that support reasoning about collections of elements such as sets, multisets, and fuzzy sets. Element membership in such collections is given by a characteristic function from a finite universe (of unknown size) to a subset of rational numbers specified by user-defined constraints in mixed linear integer-rational arithmetic. Our logic supports standard operators such as union, intersection, difference, or any operation defined pointwise using mixed linear integer-rational arithmetic. Moreover, it supports the notion of cardinality of the collection. Deciding formulas in such logic has application in verification of data structures. Our decision procedure reduces satisfiability of formulas with collections to satisfiability of formulas in an extension of mixed linear integer-rational arithmetic with an “unbounded sum” operator. Such extension is also interesting in its own right because it can encode reachability problems for a simple class of transition systems. We present a decision procedure for the resulting extension, using a satisfiability-preserving transformation that eliminates the unbounded sum operator. Our decidability result subsumes previous special cases for sets and multisets

Building a Calculus of Data Structures

Techniques such as verification condition generation, predicate abstraction, and expressive type systems reduce software verification to proving formulas in expressive logics. Programs and their specifications often make use of data structures such as sets, multisets, algebraic data types, or graphs. Consequently, formulas generated from verification also involve such data structures. To automate the proofs of such formulas we propose a logic (a “calculus”) of such data structures. We build the calculus by starting from decidable logics of individual data structures, and connecting them through functions and sets, in ways that go beyond the frameworks such as Nelson-Oppen. The result are new decidable logics that can simultaneously specify properties of different kinds of data structures and overcome the limitations of the individual logics. Several of our decidable logics include abstraction functions that map a data structure into its more abstract view (a tree into a multiset, a multiset into a set), into a numerical quantity (the size or the height), or into the truth value of a candidate data structure invariant (sortedness, or the heap property). For algebraic data types, we identify an asymptotic many-to-one condition on the abstraction function that guarantees the existence of a decision procedure. In addition to the combination based on abstraction functions, we can combine multiple data structure theories if they all reduce to the same data structure logic. As an instance of this approach, we describe a decidable logic whose formulas are propositional combinations of formulas in: weak monadic second-order logic of two successors, two-variable logic with counting, multiset algebra with Presburger arithmetic, the Bernays-Schönfinkel-Ramsey class of first-order logic, and the logic of algebraic data types with the set content function. The subformulas in this combination can share common variables that refer to sets of objects along with the common set algebra operations. Such sound and complete combination is possible because the relations on sets definable in the component logics are all expressible in Boolean Algebra with Presburger Arithmetic. Presburger arithmetic and its new extensions play an important role in our decidability results. In several cases, when we combine logics that belong to NP, we can prove the satisfiability for the combined logic is still in NP