Program Slicing is a well-known decomposition technique that transforms a large program into a smaller one that contains only statements relevant to the computation of a selected function. In this paper, we present a novel predicate-based dynamic slicing algorithm for message passing programs. Unlike the more traditional slicing criteria that focus only on the parts of the program that influence a variable of interest at a specific position in the program, a predicate focuses on those parts of the program that influence the predicate. The dynamic predicate slice captures some global requirements or suspected error properties of a distributed program and computes all statements that are relevant. This article presents an algorithm and a sample computation to illustrate how the predicate slice can be computed. Additionally, we introduce a predicate trace to classify the relevance of statement executions based on the predicate slice. A compressed predicate trace can be used to reveal those instances of statement execution that turn the global predicate true, among others.

this paper, we present a new approach to studying program-integration algorithms. In particular, we introduce

Introduction Program slicing is a technique for simplifying programs by focusing on selected aspects of semantics. The process of slicing deletes those parts of the program which can be determined to have no effect upon the semantics of interest. Slicing has applications in testing and debugging, re-engineering, program comprehension and software measurement. For example, in debugging, there is little point in the (human) debugger analysing sections of the source code which cannot have caused the bug. Slicing avoids this by removing these parts of the program, focusing attention on those parts of the program which may contain a fault. This article reviews three semantic paradigms for slicing: static, dynamic and conditioned and two syntactic paradigms: syntax-preserving and amorphous. Slicing has been applied to many software development problems including testing, reuse, maintenance and evolution. This paper describes the main forms of program slice and some of the applicati

We describe a new tool that automatically identifies impact of customization changes, i.e., how changes affect software behavior. As opposed to existing static analysis tools that aim at aiding programmers or improve performance, our tool is designed for endusers without prior knowledge in programming. We utilize stateof-the-art static analysis algorithms for the programs within an Enterprise Resource Planning system (ERP). Key challenges in analyzing real world ERP programs are their significant size and the interdependency between programs. In particular, we describe and compare three customization change impact analyses for real-world programs, and a balancing algorithm built upon the three independent analyses. This paper presents PanayaImpactAnalysis (PanayaIA), a web on-demand tool, providing ERP professionals a clear view of the impact of a customization change on the system. In addition we report empirical results of PanayaIA when used by end-users on an ERP system of tens of millions LOCs.