从软件执行数据中发现组件行为

Cong Liu, B. V. Dongen, Nour Assy, Wil M.P. van der Aalst
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引用次数: 41

摘要

在软件执行过程中可以记录大量的数据。这为软件运行时分析提供了有价值的信息。可能会发生许多崩溃和异常,理解软件的行为方式是一个真正的挑战。软件通常由各种组件组成。组件是软件中几乎独立的部分,它完成了一个明确的功能。过程挖掘旨在通过从事件日志中提取知识来发现、监控和改进实际过程。本文提出了一种利用过程挖掘作为工具来发现软件的真实行为并对其进行分析的方法。非结构化的软件执行数据可能过于复杂,涉及多个交错的组件等。应用现有的流程挖掘技术会产生类似意大利面的模型,没有清晰的结构,也没有最终容易理解的有价值的信息。在本文中,我们首先观察到软件是由组件组成的,我们使用这些信息通过发现每个组件的行为模型来将问题分解为更小的独立问题。通过实验分析,我们证明了所提出的方法有助于发现更易于理解的软件模型。所有提出的方法都已在开源过程挖掘工具包ProM中实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Component behavior discovery from software execution data
Tremendous amounts of data can be recorded during software execution. This provides valuable information on software runtime analysis. Many crashes and exceptions may occur, and it is a real challenge to understand how software is behaving. Software is usually composed of various components. A component is a nearly independent part of software that full-fills a clear function. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs. This paper presents an approach to utilize process mining as a tool to discover the real behavior of software and analyze it. The unstructured software execution data may be too complex, involving multiple interleaved components, etc. Applying existing process mining techniques results in spaghetti-like models with no clear structure and no valuable information that can be easily understood by end. In this paper, we start with the observation that software is composed of components and we use this information to decompose the problem into smaller independent ones by discovering a behavioral model per component. Through experimental analysis, we illustrate that the proposed approach facilitates the discovery of more understandable software models. All proposed approaches have been implemented in the open-source process mining toolkit ProM.
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