Measuring Requirement Quality to Predict Testability
Measuring Requirement Quality to Predict Testability
复制标题
衡量需求质量以预测可测试性
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Clinton Woodson
中科院分区：
文献类型：
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作者：
J. Hayes;Wenbin Li;Tingting Yu;Xue Han;Mark Hays;Clinton Woodson
在软件驱动的社会中，软件缺陷会增加消费者的拥有成本，并可能导致毁灭性的失败。软件测试包括功能测试和结构测试，是发现软件系统故障和评估软件系统可靠性的常用方法。为了提高测试的有效性，开发的工件（需求，代码）必须设计成可测试的。以前的工作已经开发了许多方法来解决代码的可测试性时，适用于结构测试，但迄今为止，没有工作考虑的方法进行评估和预测的可测试性的要求，以帮助功能测试。在这项工作中，我们从需求的可理解性和质量的角度使用机器学习和统计分析方法来解决需求的可测试性。我们首先使用需求度量来实证研究各个度量与需求可测性之间的相关关系。然后，我们评估预测需求可测试性的相关要求措施。我们检查了两个数据集，每个数据集由需求和代码工件组成。我们发现，一些措施有助于划分可测试和不可测试的需求，并发现轶事证据表明，可测试性的学习模型可以用来指导其他（非培训）系统的需求评估。
Software bugs contribute to the cost of ownership for consumers in a software-driven society and can potentially lead to devastating failures. Software testing, including functional testing and structural testing, remains a common method for uncovering faults and assessing dependability of software systems. To enhance testing effectiveness, the developed artifacts (requirements, code) must be designed to be testable. Prior work has developed many approaches to address the testability of code when applied to structural testing, but to date no work has considered approaches for assessing and predicting testability of requirements to aid functional testing. In this work, we address requirement testability from the perspective of requirement understandability and quality using a machine learning and statistical analysis approach. We first use requirement measures to empirically investigate the relevant relationship between each measure and requirement testability. We then assess relevant requirement measures for predicting requirement testability. We examined two datasets, each consisting of requirement and code artifacts. We found that several measures assist in delineating between the testable and non-testable requirements, and found anecdotal evidence that a learned model of testability can be used to guide evaluation of requirements for other (non-trained) systems.