Properties of a predicate transformer of the VRS system

Abstract

Models specified in the language of basic protocols are considered. These models are attribute transition systems, and their states are defined by formulas of multisort first-order predicate calculus over system attributes. Attributes of simple numeric and symbolic types, functional types, and queues are allowed. Assignment operators, queue update operators, and arbitrary formulas are used in postconditions of basic protocols. To pass from one state to another, a predicate transformer is constructed as a function of formula transformation. The following main property of the predicate transformer is proved: it calculates the strongest postcondition for symbolic states.

Approach to behavior scenarios debugging

“…Каждый переход осуществляется с помощью предикатного трансформера. Су-ществуют прямой (Forward) [8] и обратный (Backward) [9] предикатные трансфор-меры. Действие Forward предикатного трансформера обозначается стрелкой слева направо и реализует алгоритм нахождения сильнейшего постусловия базового про-токола и соответствующего состояния окружающей среды для логических формул первого порядка.…”

“…4). Основным преимуществом такого подхода является автоматический поиск протоко-ла, в котором значения переменных изменяются неожиданным способом, не учтен-ным в гиде [8,9].…”

Symbolic Modeling Approach in Verification and Testing

“…The function pt is called predicate transformer and it computes the strongest postcondition provided that precondition before computation was sα(x). The details for computing the predicate transformer for formulas with quantifiers can be found in [16]. Generating traces from histories is made in a similar way as for concrete model but instantiation is needed only for the names of agents, which are used to name the instances in MSC diagrams.…”

Formation of Similarity-Reflecting Binary Vectors with Random Binary Projections

“…Many types of information used for this purpose can be represented in vector format. This concerns not only data in the form of homogeneous attributes and fields [6][7][8] but also complex structures with hierarchical systems of relations [9][10][11][12][13][14][15][16][17], which are applied in knowledge bases (including modeling of analogical reasoning [1, 4,11,16,17]). Vector operations can be easily parallelized, which allows using distributed computing systems for handling (for example, Grid [18,19]).…”