Abstract Program synthesis is the task of automatically deriving a program that has been specified by a user in advance. Combining automated theorem proving with program synthesis enables the automated construction of proven-to-be-correct programs, thereby ensuring software reliability. In this paper, we consider the superposition-based calculus extended to support synthesis of recursion-free programs allowing reasoning with uncomputable symbols. We present cases where the calculus fails and refine it to solve them. We prove that the refined calculus is sound. Finally, we also prove completeness in the following sense: if at least one computable program satisfying the given specification exists, we show that the modified calculus finds one.

Abstract During the past decade of continuous development, the theorem prover Vampire has become an automated solver for the combined theories of commonly-used data structures. Vampire now supports arithmetic, induction, and higher-order logic. These advances have been made to meet the demands of software verification, enabling Vampire to effectively complement SAT/SMT solvers and aid proof assistants. We explain how best to use Vampire in practice and review the main changes Vampire has undergone since its last tool presentation, focusing on the engineering principles and design choices we made during this process.

Abstract Program synthesis is the task of constructing a program conforming to a given specification. We focus on deductive synthesis, and in particular on synthesis problems with specifications given as $$\forall \exists $$ ∀ ∃ -formulas, expressing the existence of an output corresponding to any input. So far there has been no canonical benchmark set for deductive synthesis using the $$\forall \exists $$ ∀ ∃ -format and supporting the so-called uncomputable symbol restriction. This work presents such a data set, composed by complementing existing benchmarks by new ones. Our data set is dynamically growing and should motivate future developments in the theory and practice of automating synthesis.

Abstract We turn saturation-based theorem proving into an automated framework for recursive program synthesis. We introduce magic axioms as valid induction axioms and use them together with answer literals in saturation. We introduce new inference rules for induction in saturation and use answer literals to synthesize recursive functions from these proof steps. Our proof-of-concept implementation in the Vampire theorem prover constructs recursive functions over algebraic data types, while proving inductive properties over these types.

Abstract Proof by induction is commonplace in modern mathematics and computational logic. This paper overviews and discusses our recent results in turning saturation-based first-order theorem proving into a powerful framework for automating inductive reasoning. We formalize applications of induction as new inference rules of the saturation process, add instances of appropriate induction schemata to the search space, and use these rules and instances immediately upon their addition for the purpose of guiding induction. Our results show, for example, that many problems from formal verification and mathematical theories can now be solved completely automatically using a first-order theorem prover.

Abstract We present an automated reasoning framework for synthesizing recursion-free programs using saturation-based theorem proving. Given a functional specification encoded as a first-order logical formula, we use a first-order theorem prover to both establish validity of this formula and discover program fragments satisfying the specification. As a result, when deriving a proof of program correctness, we also synthesize a program that is correct with respect to the given specification. We describe properties of the calculus that a saturation-based prover capable of synthesis should employ, and extend the superposition calculus in a corresponding way. We implemented our work in the first-order prover Vampire, extending the successful applicability of first-order proving to program synthesis.

The need to solve non-linear arithmetic constraints presents a major obstacle to the automatic verification of smart contracts. In this case study we focus on the two overapproximation techniques used by the industry verification tool Certora Prover: overapproximation of non-linear integer arithmetic using linear integer arithmetic and using non-linear real arithmetic. We compare the performance of contemporary SMT solvers on verification conditions produced by the Certora Prover using these two approximations against the natural non-linear integer arithmetic encoding. Our evaluation shows that the use of the overapproximation methods leads to solving a significant number of new problems.

Abstract Integers are ubiquitous in programming and therefore also in applications of program analysis and verification. Such applications often require some sort of inductive reasoning. In this paper we analyze the challenge of automating inductive reasoning with integers. We introduce inference rules for integer induction within the saturation framework of first-order theorem proving. We implemented these rules in the theorem prover Vampire and evaluated our work against other state-of-the-art theorem provers. Our results demonstrate the strength of our approach by solving new problems coming from program analysis and mathematical properties of integers.

Functional programs over inductively defined data types, such as lists, binary trees and naturals, can naturally be defined using recursive equations over recursive functions. In first-order logic, function definitions can be considered as universally quantified equalities. Verifying functional program properties therefore requires inductive reasoning with both theories and quantifiers. In this paper we propose new extensions and generalizations to automate induction with recursive functions in saturation-based first-order theorem proving, using the superposition calculus. Instead of using function definitions as first-order axioms, we introduced new simplification rules for treating function definitions as rewrite rules. We guide inductive reasoning and strengthen induction schema using recursively defined functions. Our experimental results show that handling recursive definitions in superposition reasoning significantly improves automated reasoning with induction.