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Granularity-Driven Dynamic Predicate Slicing Algorithms for Message Passing Systems

机译:消息传递系统的粒度驱动动态谓词切片算法

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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 two novel predicate-based dynamic slicing algorithms for message passing programs. Unlike more traditional slicing criteria that focus only on 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 slices capture some global requirements or suspected error properties of a distributed program and computes all statements that are relevant. The presented algorithms differ from each other in their computational approaches (forward versus backward) and in the granularity of information they provide. A proof of correctness of these algorithms is provided. Through the introduction of dominant states and dominant events, critical statement executions are identified that change the value of the global predicate. Under this formulation, optimizing dynamic predicate slicing becomes a meaningful goal as well. Finally, we present how predicate slices can be applied to support comprehension tasks for analyzing and maintaining distributed programs.
机译:程序切片是一种众所周知的分解技术,可以将大型程序转换为较小的程序,该程序仅包含与所选函数的计算有关的语句。在本文中,我们提出了两种新颖的基于谓词的动态切片算法来传递消息。与更传统的切片标准仅关注程序中影响程序特定位置的感兴趣变量的部分不同,谓词关注于程序中影响谓词的那些部分。动态谓词切片捕获分布式程序的某些全局要求或可疑错误属性,并计算所有相关语句。所提出的算法在计算方法(向前与向后)以及所提供信息的粒度方面彼此不同。提供了这些算法正确性的证明。通过引入显性状态和显性事件,可以识别关键语句的执行,从而改变全局谓词的值。在这种表达方式下,优化动态谓词切片也成为一个有意义的目标。最后,我们介绍如何将谓词切片应用于支持用于分析和维护分布式程序的理解任务。

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