Abstract The two-watched literal scheme, a core component of efficient CDCL (Conflict-Driven Clause Learning) implementations for propositional logic, is extended to first-order logic. Given a set of first-order clauses and a set of ground literals, our lifted two-watched literal scheme efficiently detects all propagating and false clauses with respect to the ground literals. We present the algorithm as a system of rules and prove its soundness and completeness. Additionally, we provide an implementation of the two-watched literal scheme, which outperforms a standard dynamic programming approach for detecting propagatable literals and conflicts, especially when dealing with long clauses.

Abstract Recently, it has been demonstrated that SCL(FOL) can simulate ground ordered resolution [6]. We revisit this result and provide a new formal proof in Isabelle/HOL. The existing pen-and-paper proof is monolithic and challenging to comprehend. In order to improve clarity, we develop an alternative proof structured as eleven (bi)simulation steps between the two calculi, transitioning from ordered resolution to SCL(FOL). A key simulation lemma ensures that, under certain conditions, one simulation direction can be automatically lifted to the other. Consequently, for each of the eleven steps, it suffices to establish only one direction of simulation. The complete proof is included in the "Image missing" .

Abstract Given a finite consistent set of ground literals, we present an algorithm that generates a complete first-order logic interpretation, i.e., an interpretation for all ground literals over the signature and not just those in the input set, that is also a model for the input set. The interpretation is represented by first-order linear literals. It can be effectively used to evaluate clauses. A particular application are SCL stuck states. The SCL (Simple Clause Learning) calculus always computes with respect to a finite number of ground literals. It then finds either a contradiction or a stuck state being a model with respect to the considered ground literals. Our algorithm builds a complete literal interpretation out of such a stuck state model that can then be used to evaluate the clause set. If all clauses are satisfied an overall model has been found. If it does not satisfy some clause, this information can be effectively explored to extend the scope of ground literals considered by SCL.

We propose a translation from eBPF (extended Berkeley Packet Filter) code to CHC (Constrained Horn Clause sets) over the combined theory of bitvectors and arrays. eBPF is in particular used in the Linux kernel where user code is executed under kernel privileges. In order to protect the kernel, a well-known verifier statically checks the code for any harm and a number of research efforts have been performed to secure and improve the performance of the verifier. This paper is about verifying the functional properties of the eBPF code itself. Our translation procedure bpfverify is precise and covers almost all details of the eBPF language. Functional properties are automatically verified using z3. We prove termination of the procedure and show by real world eBPF code examples that full-fledged automatic verification is actually feasible.

Abstract We present an Isabelle/HOL formalization of Simple Clause Learning for first-order logic without equality: SCL(FOL). The main results are formal proofs of soundness, non-redundancy of learned clauses, termination, and refutational completeness. Compared to the unformalized version, the formalized calculus is simpler and more general, some results such as non-redundancy are stronger and some results such as non-subsumption are new. We found one bug in a previously published version of the SCL Backtrack rule. Compared to related formalizations, we introduce a new technique for showing termination based on non-redundant clause learning.

Abstract We show that SCL(FOL) can simulate the derivation of non-redundant clauses by superposition for first-order logic without equality. Superposition-based reasoning is performed with respect to a fixed reduction ordering. The completeness proof of superposition relies on the grounding of the clause set. It builds a ground partial model according to the fixed ordering, where minimal false ground instances of clauses then trigger non-redundant superposition inferences. We define a respective strategy for the SCL calculus such that clauses learned by SCL and superposition inferences coincide. From this perspective the SCL calculus can be viewed as a generalization of the superposition calculus.

The family of SCL (Clause Learning from Simple Models) calculi learns clauses with respect to a partial model assumption, similar to CDCL (Conflict Driven Clause Learning). The partial model always consists of ground first-order literals and is built by decisions and propagations. In contrast to propositional logic where propagation chains are always finite, in first-order logic they can become infinite. Therefore, the SCL family does not require exhaustive propagation and the size of the partial model is always finitely bounded. Any partial model not leading to a conflict constitutes a model for the respective finitely bounded ground clause set. We show that all potential partial models can be explored as part of the SCL calculus for first-order logic without equality and that any overall model is an extension of a partial model considered. Furthermore, SCL turns into a semi-decision procedure for first-order logic by extending the finite bound for any partial model not leading to a conflict.

Abstract In a previous paper, we have shown that clause sets belonging to the Horn Bernays-Schönfinkel fragment over simple linear real arithmetic (HBS(SLR)) can be translated into HBS clause sets over a finite set of first-order constants. The translation preserves validity and satisfiability and it is still applicable if we extend our input with positive universally or existentially quantified verification conditions (conjectures). We call this translation a Datalog hammer. The combination of its implementation in SPASS-SPL with the Datalog reasoner VLog establishes an effective way of deciding verification conditions in the Horn fragment. We verify supervisor code for two examples: a lane change assistant in a car and an electronic control unit of a supercharged combustion engine. In this paper, we improve our Datalog hammer in several ways: we generalize it to mixed real-integer arithmetic and finite first-order sorts; we extend the class of acceptable inequalities beyond variable bounds and positively grounded inequalities; and we significantly reduce the size of the hammer output by a soft typing discipline. We call the result the sorted Datalog hammer. It not only allows us to handle more complex supervisor code and to model already considered supervisor code more concisely, but it also improves our performance on real world benchmark examples. Finally, we replace the before file-based interface between SPASS-SPL and VLog by a close coupling resulting in a single executable binary.

We present several new techniques for linear arithmetic constraint solving. They are all based on the linear cube transformation, a method presented here, which allows us to efficiently determine whether a system of linear arithmetic constraints contains a hypercube of a given edge length. Our first findings based on this transformation are two sound tests that find integer solutions for linear arithmetic constraints. While many complete methods search along the problem surface for a solution, these tests use cubes to explore the interior of the problems. The tests are especially efficient for constraints with a large number of integer solutions, e.g., those with infinite lattice width. Inside the SMT-LIB benchmarks, we have found almost one thousand problem instances with infinite lattice width. Experimental results confirm that our tests are superior on these instances compared to several state-of-the-art SMT solvers. We also discovered that the linear cube transformation can be used to investigate the equalities implied by a system of linear arithmetic constraints. For this purpose, we developed a method that computes a basis for all implied equalities, i.e., a finite representation of all equalities implied by the linear arithmetic constraints. The equality basis has several applications. For instance, it allows us to verify whether a system of linear arithmetic constraints implies a given equality. This is valuable in the context of Nelson–Oppen style combinations of theories.