Streamlining Temporal Formal Verification over Columnar Databases

Abstract

Recent findings demonstrate how database technology enhances the computation of formal verification tasks expressible in linear time logic for finite traces (LTLf). Human-readable declarative languages also help the common practitioner to express temporal constraints in a straightforward and accessible language. Notwithstanding the former, this technology is in its infancy, and therefore, few optimization algorithms are known for dealing with massive amounts of information audited from real systems. We, therefore, present four novel algorithms subsuming entire LTLf expressions while outperforming previous state-of-the-art implementations on top of KnoBAB, thus postulating the need for the corresponding, leading to the formulation of novel xtLTLf-derived algebraic operators.

DECLARE<em>d</em>: A Polytime LTL_f Fragment

“…Verified Artificial Intelligence [1] calls for exact procedures ascertaining whether a model of the system S abides by the specifications in Φ through yes or no answers (S ⊨ Φ) when written in a formalism for efficient computations, either for verifying the compliance of a system to a specification (formal verification [2]) or for producing a system abiding by a given specification (formal synthesis [3]). This can be determined after a specification mining phase used to extract Φ from a system S [4]; these considerations bridge temporal reasoning with artificial intelligence, as in both we can extract a specification from the data that can be used to determine decision problems.…”

DECLARE<em>d</em>: A Polytime LTL_f Fragment

“…Verified Artificial Intelligence [1] calls for exact procedures ascertaining whether a model of the system S abides by the specifications in Φ through yes or no answers (S Φ) when written in a formalism for efficient computations, either for verifying the compliance of a system to a specification (formal verification [2]) or for producing a system abiding by a given specification (formal synthesis [3]). This can be determined after a specification mining phase used to extract Φ from a system S [4]; these considerations bridge temporal reasoning with artificial intelligence, as in both we can extract a specification from the data that can be used to determine decision problems.…”