The Bernays-Schönfinkel-Ramsey Class of Separation Logic with Uninterpreted Predicates

International audienceThis paper investigates the satisfiability problem for Separation Logic with k record fields, with unrestricted nesting of separating conjunctions and implications. It focuses on prenex formulae with a quantifier prefix in the language ∃ * ∀ * , that contain uninterpreted (heap-independent) predicate symbols. In analogy with first-order logic, we call this fragment Bernays-Schönfinkel-Ramsey Separation Logic [BSR(SL k)]. In contrast with existing work on Separation Logic, in which the universe of possible locations is assumed to be infinite, we consider both finite and infinite universes in the present paper. We show that, unlike in first-order logic, the (in)finite satisfiability problem is undecidable for BSR(SL k). Then we define two non-trivial subsets thereof, for which the finite and infinite satisfiability problems are PSPACE-complete, respectively, assuming that the maximum arity of the uninterpreted predicate symbols does not depend on the input. These fragments are defined by controlling the polarity of the occurrences of separating implications, as well as the occurrences of universally quantified variables within their scope. These decidability results have natural applications in program verification, as they allow to automatically prove lemmas that occur in e.g. entailment checking between inductively defined predicates and validity checking of Hoare triples expressing partial correctness conditions

The Complexity of Prenex Separation Logic with One Selector

We first show that infinite satisfiability can be reduced to finite satisfiability for all prenex formulas of Separation Logic with $k\geq1$ selector fields (\seplogk{k}). Second, we show that this entails the decidability of the finite and infinite satisfiability problem for the class of prenex formulas of \seplogk{1}, by reduction to the first-order theory of one unary function symbol and unary predicate symbols. We also prove that the complexity is not elementary, by reduction from the first-order theory of one unary function symbol. Finally, we prove that the Bernays-Sch\"onfinkel-Ramsey fragment of prenex \seplogk{1} formulae with quantifier prefix in the language $\exists^*\forall^*$ is \pspace-complete. The definition of a complete (hierarchical) classification of the complexity of prenex \seplogk{1}, according to the quantifier alternation depth is left as an open problem

Internal Calculi for Separation Logics

We present a general approach to axiomatise separation logics with heaplet semantics with no external features such as nominals/labels. To start with, we design the first (internal) Hilbert-style axiomatisation for the quantifier-free separation logic SL(?, -*). We instantiate the method by introducing a new separation logic with essential features: it is equipped with the separating conjunction, the predicate ls, and a natural guarded form of first-order quantification. We apply our approach for its axiomatisation. As a by-product of our method, we also establish the exact expressive power of this new logic and we show PSpace-completeness of its satisfiability problem

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 22nd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2019. The 29 papers presented in this volume were carefully reviewed and selected from 85 submissions. They deal with foundational research with a clear significance for software science

Programming with Specifications

This thesis explores the use of specifications for the construction of correct programs. We go beyond their standard use as run-time assertions, and present algorithms, techniques and implementations for the tasks of 1) program verification, 2) declarative programming and 3) software synthesis. These results are made possible by our advances in the domains of decision procedure design and implementation. In the first part of this thesis, we present a decidability result for a class of logics that support user-defined recursive function definitions. Constraints in this class can encode expressive properties of recursive data structures, such as sortedness of a list, or balancing of a search tree. As a result, complex verification conditions can be stated concisely and solved entirely automatically. We also present a new decision procedure for a logic to reason about sets and constraints over their cardinalities. The key insight lies in a technique to decompose con- straints according to mutual dependencies. Compared to previous techniques, our algorithm brings significant improvements in running times, and for the first time integrates reasoning about cardinalities within the popular DPLL(T ) setting. We integrated our algorithmic ad- vances into Leon, a static analyzer for functional programs. Leon can reason about constraints involving arbitrary recursive function definitions, and has the desirable theoretical property that it will always find counter-examples to assertions that do not hold. We illustrate the flexibility and efficiency of Leon through experimental evaluation, where we used it to prove detailed correctness properties of data structure implementations. We then illustrate how program specifications can be used as a high-level programming construct ; we present Kaplan, an extension of Scala with first-class logical constraints. Kaplan allows programmers to create, manipulate and combine constraints as they would any other data structure. Our implementation of Kaplan illustrates how declarative programming can be incorporated into an existing mainstream programming language. Moreover, we examine techniques to transform, at compile-time, program specifications into efficient executable code. This approach of software synthesis combines the correctness benefits of declarative programming with the efficiency of imperative or functional programming

Improving Model Finding for Integrated Quantitative-qualitative Spatial Reasoning With First-order Logic Ontologies

Many spatial standards are developed to harmonize the semantics and specifications of GIS data and for sophisticated reasoning. All these standards include some types of simple and complex geometric features, and some of them incorporate simple mereotopological relations. But the relations as used in these standards, only allow the extraction of qualitative information from geometric data and lack formal semantics that link geometric representations with mereotopological or other qualitative relations. This impedes integrated reasoning over qualitative data obtained from geometric sources and “native” topological information – for example as provided from textual sources where precise locations or spatial extents are unknown or unknowable. To address this issue, the first contribution in this dissertation is a first-order logical ontology that treats geometric features (e.g. polylines, polygons) and relations between them as specializations of more general types of features (e.g. any kind of 2D or 1D features) and mereotopological relations between them. Key to this endeavor is the use of a multidimensional theory of space wherein, unlike traditional logical theories of mereotopology (like RCC), spatial entities of different dimensions can co-exist and be related. However terminating or tractable reasoning with such an expressive ontology and potentially large amounts of data is a challenging AI problem. Model finding tools used to verify FOL ontologies with data usually employ a SAT solver to determine the satisfiability of the propositional instantiations (SAT problems) of the ontology. These solvers often experience scalability issues with increasing number of objects and size and complexity of the ontology, limiting its use to ontologies with small signatures and building small models with less than 20 objects. To investigate how an ontology influences the size of its SAT translation and consequently the model finder’s performance, we develop a formalization of FOL ontologies with data. We theoretically identify parameters of an ontology that significantly contribute to the dramatic growth in size of the SAT problem. The search space of the SAT problem is exponential in the signature of the ontology (the number of predicates in the axiomatization and any additional predicates from skolemization) and the number of distinct objects in the model. Axiomatizations that contain many definitions lead to large number of SAT propositional clauses. This is from the conversion of biconditionals to clausal form. We therefore postulate that optional definitions are ideal sentences that can be eliminated from an ontology to boost model finder’s performance. We then formalize optional definition elimination (ODE) as an FOL ontology preprocessing step and test the simplification on a set of spatial benchmark problems to generate smaller SAT problems (with fewer clauses and variables) without changing the satisfiability and semantic meaning of the problem. We experimentally demonstrate that the reduction in SAT problem size also leads to improved model finding with state-of-the-art model finders, with speedups of 10-99%. Altogether, this dissertation improves spatial reasoning capabilities using FOL ontologies – in terms of a formal framework for integrated qualitative-geometric reasoning, and specific ontology preprocessing steps that can be built into automated reasoners to achieve better speedups in model finding times, and scalability with moderately-sized datasets