动态条件响应图的迭代测试驱动建模的静态和动态技术

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Axel K.F. Christfort , Vlad Paul Cosma , Søren Debois , Thomas T. Hildebrandt , Tijs Slaats
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引用次数: 0

摘要

测试驱动的声明性流程建模将流程模型与测试跟踪相结合,并作为实现声明性方法提供的灵活性和命令式方法的可理解性的一种手段而引入。开放的测试驱动建模为测试添加了上下文的概念,指定了模型中关注的活动,并且已经作为一种支持迭代测试驱动建模的方法被引入,在迭代测试驱动建模中,模型可以扩展而不必更改所有测试,而单元测试中,测试可以定义流程部分的所需属性,而无需对整个流程的细节进行推理。然而,开放性使得检查测试的要求更高,因为在测试执行的任何点都允许上下文之外的操作,因此许多不同的跟踪可能会验证或使打开的测试无效。在本文中,我们将先前开发的用于动态条件响应图的有效开放测试驱动建模的静态技术与基于对齐检查的开放测试动态检查的新颖有效实现相结合。我们在一个基于真实的跨组织案例管理系统的示例上说明了静态技术,并对不同大小的模型和测试进行了动态检查基准测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Static and dynamic techniques for iterative test-driven modelling of Dynamic Condition Response Graphs
Test-driven declarative process modelling combines process models with test traces and has been introduced as a means to achieve both the flexibility provided by the declarative approach and the comprehensibility of the imperative approach. Open test-driven modelling adds a notion of context to tests, specifying the activities of concern in the model, and has been introduced as a means to support both iterative test-driven modelling, where the model can be extended without having to change all tests, and unit testing, where tests can define desired properties of parts of the process without needing to reason about the details of the whole process. The openness however makes checking a test more demanding, since actions outside the context are allowed at any point in the test execution and therefore many different traces may validate or invalidate an open test. In this paper we combine previously developed static techniques for effective open test-driven modelling for Dynamic Condition Response Graphs with a novel efficient implementation of dynamic checking of open tests based on alignment checking. We illustrate the static techniques on an example based on a real-life cross-organizational case management system and benchmark the dynamic checking on models and tests of varying size.
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来源期刊
Data & Knowledge Engineering
Data & Knowledge Engineering 工程技术-计算机:人工智能
CiteScore
5.00
自引率
0.00%
发文量
66
审稿时长
6 months
期刊介绍: Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
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