Abstract program slicing on dependence condition graph

Abstract Many slicing techniques have been proposed based on the traditional Program Dependence Graph (PDG) representation. In traditional PDGs, the notion of dependency between statements is based on syntactic presence of a variable in the definition of another variable or on a conditional expression. Mastroeni and Zanardini first introduced the notion of semanticsbased data dependency, both at concrete and abstract domains, that helps in converting the traditional syntactic PDGs into more refined semanticsbased (abstract) PDGs by disregarding some false dependences from them. As a result, the slicing techniques based on these semantics-based (abstract) PDGs result into more precise slices. In this paper, we strictly improve this approach by (i) introducing the notion of semantic relevancy of statements, and (ii) combining it with conditional dependency. This allows us to transform syntactic PDGs into semantics-based (abstract) Dependence Condition Graphs (DCGs) that enable to identify the conditions for dependences between program points