SSTCSC:测试用例和源代码的语义结构

Muhammad Bilal Bashir, T. Malik, T. Afzal
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引用次数: 0

摘要

如果把万维网看作是包含生活方方面面数据的信息的大山,那么语义网就是它的扩展版本,它构建了信息并使其能够被机器处理。语义网具有许多优点,是构建多用途领域数据的一种创新方法。它的标注特性使其能够提取用户所需的精确信息。软件测试是评估系统行为以确保其是否满足用户需求的过程。在软件工程中,特别是在软件测试领域,对相关工件进行语义结构化是一种新的方法。研究人员为许多语言和方法提供了基于XML的表示,但是当我们试图将其应用于软件测试领域时,它们的上下文是不同的。在本文中,我们着重讨论了软件测试领域在需要对其相关活动进行语义表示时所存在的不足。我们提出了一种新的方法,测试用例和源代码的语义结构(SSTCSC),它将满足白盒测试覆盖标准语句覆盖的基于RDF的测试用例与基于RDF的目标java类联系起来。在执行语义上呈现的信息(针对程序的测试用例)之后,将进一步通过两个阶段,即代码分类和代码可视化,这两个阶段以视觉方式显示分类代码段的最终输出。该工具支持该方法的整个过程,可以轻松地自动执行相关步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SSTCSC: A Semantic Structure of Test Cases and Source Code
If the World Wide Web is taken as a mountain of information containing data regarding every aspect of life, then Semantic Web is its extended version, that structures the information and makes it machine process able. Semantic Web has many advantages which have proved it as an innovative approach to structure the data for multipurpose domains. Its characteristics of annotation enable the extraction of precise information which is required by the user. Software testing is the process which evaluates a systems behavior to ensure whether it meets user's requirement or not. In software engineering, specifically in the software testing domain, semantic structuring the relevant artifacts is new. Researchers provided XML based representations for many languages and approaches but their context is different when we try to apply it on the software testing domain. In this paper, we focus on the shortcomings of the software testing domain when its concerned activities are required to represent semantically. We present a novel approach, Semantic Structure of Test Cases and Source Code (SSTCSC) which relates the RDF based test cases satisfying white box testing coverage criterion statement coverage, with the RDF based target java class(s). After the execution of semantically presented information (test cases against program) is then passes through two phases further i.e. Code Categorization and Code Visualization which shows the final output of the classified code segments visually. Whole process of the presented approach is supported by the tool which performs the related steps with ease automatically.
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