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On the Understandability of Temporal Properties Formalized in Linear Temporal Logic, Property Specification Patterns and Event Processing Language
IEEE Transactions on Software Engineering ( IF 6.5 ) Pub Date : 2020-01-01 , DOI: 10.1109/tse.2018.2859926
Christoph Czepa , Uwe Zdun

Temporal properties are important in a wide variety of domains for different purposes. For example, they can be used to avoid architectural drift in software engineering or to support the regulatory compliance of business processes. In this work, we study the understandability of three major temporal property representations: (1) Linear Temporal Logic (LTL) is a formal and well-established logic that offers temporal operators to describe temporal properties; (2) Property Specification Patterns (PSP) are a collection of recurring temporal properties that abstract underlying formal and technical representations; (3) Event Processing Language (EPL) can be used for runtime monitoring of event streams using Complex Event Processing. We conducted two controlled experiments with 216 participants in total to study the understandability of those approaches using a completely randomized design with one alternative per experimental unit. We hypothesized that PSP, as a highly abstracting pattern language, is easier to understand than LTL and EPL, and that EPL, due to separation of concerns (as one or more queries can be used to explicitly define the truth value change that an observed event pattern causes), is easier to understand than LTL. We found evidence supporting our hypotheses which was statistically significant and reproducible.

中文翻译:

关于线性时间逻辑、属性规范模式和事件处理语言中形式化的时间属性的可理解性

出于不同目的,时间属性在各种领域中都很重要。例如,它们可用于避免软件工程中的架构漂移或支持业务流程的合规性。在这项工作中,我们研究了三种主要时间属性表示的可理解性:(1) 线性时间逻辑 (LTL) 是一种形式化且完善的逻辑,提供时间运算符来描述时间属性;(2) 属性规范模式 (PSP) 是一组重复出现的时间属性,抽象了底层的形式和技术表示;(3) 事件处理语言 (EPL) 可用于使用复杂事件处理的事件流的运行时监控。我们对总共 216 名参与者进行了两个对照实验,以使用完全随机设计和每个实验单元一个替代方案来研究这些方法的可理解性。我们假设 PSP 作为一种高度抽象的模式语言,比 LTL 和 EPL 更容易理解,而 EPL,由于关注点分离(因为一个或多个查询可用于明确定义观察到的事件的真值变化)模式原因),比 LTL 更容易理解。我们找到了支持我们假设的证据,这些证据具有统计学意义且可重复。由于关注点分离(因为一个或多个查询可用于明确定义观察到的事件模式导致的真值变化),比 LTL 更容易理解。我们找到了支持我们假设的证据,这些证据具有统计学意义且可重复。由于关注点分离(因为一个或多个查询可用于明确定义观察到的事件模式导致的真值变化),比 LTL 更容易理解。我们找到了支持我们假设的证据,这些证据具有统计学意义且可重复。
更新日期:2020-01-01
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