Comparing Three Notations for Defining Scenario-Based Model Tests: A Controlled Experiment

Bernhard Hoisl, Stefan Sobernig, Mark Strembeck
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引用次数: 27

Abstract

Scenarios are an established means to specify requirements for software systems. Scenario-based tests allow for validating software models against such requirements. In this paper, we consider three alternative notations to define such scenario tests on structural models: a semi structured natural-language notation, a diagrammatic notation, and a fully-structured textual notation. In particular, we performed a study to understand how these three notations compare to each other with respect to accuracy and effort of comprehending scenario-test definitions, as well as with respect to the detection of errors in the models under test. 20 software professionals (software engineers, testers, researchers) participated in a controlled experiment based on six different comprehension and maintenance tasks. For each of these tasks, questions on a scenario-test definition and on a model under test had to be answered. In an ex-post questionnaire, the participants rated each notation on a number of dimensions (e.g., practicality or scalability). Our results show that the choice of a specific scenario-test notation can affect the productivity (in terms of correctness and time-effort) when testing software models for requirements conformance. In particular, the participants of our study spent comparatively less time and completed the tasks more accurately when using the natural-language notation compared to the other two notations. Moreover, the participants of our study explicitly expressed their preference for the natural-language notation.
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定义基于场景的模型测试的三种符号的比较:一个控制实验
场景是一种确定软件系统需求的方法。基于场景的测试允许根据这些需求验证软件模型。在本文中,我们考虑了三种可选的符号来定义结构模型上的场景测试:半结构化的自然语言符号,图解符号和完全结构化的文本符号。特别是,我们进行了一项研究,以了解这三种符号在理解场景测试定义的准确性和努力方面是如何相互比较的,以及在测试模型中的错误检测方面。20名软件专业人员(软件工程师、测试人员、研究人员)参与了一项基于六种不同理解和维护任务的受控实验。对于这些任务中的每一个,都必须回答关于场景测试定义和被测试模型的问题。在一份事后调查问卷中,参与者根据多个维度(例如,实用性或可扩展性)对每个符号进行评分。我们的结果表明,在测试软件模型的需求一致性时,特定场景测试符号的选择可以影响生产力(就正确性和时间努力而言)。特别是,与其他两种符号相比,我们的研究参与者在使用自然语言符号时花费的时间相对较少,并且完成任务的准确性更高。此外,本研究的参与者明确表达了他们对自然语言符号的偏好。
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