Automated generation of state abstraction functions using data invariant inference
Automated generation of state abstraction functions using data invariant inference
复制标题
使用数据不变推理自动生成状态抽象函数
DOI：
--
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
M. Harman
中科院分区：
文献类型：
--
作者：
P. Tonella;Duy Cu Nguyen;A. Marchetto;Kiran Lakhotia;M. Harman
基于模型的测试取决于可以手动或通过模型推理技术来定义的模型的可用性。要生成包含有意义状态抽象的模型，模型推理需要一组抽象功能作为输入。但是，它们的规格很困难，并且涉及大量的手动努力。在本文中，我们研究了一种自动推断出进行状态抽象所需的抽象功能和基于此类抽象的有限状态模型所必需的抽象功能。所提出的方法结合了聚类，不变推理和遗传算法的组合，以优化沿三个质量属性的抽象函数，这些质量属性表征了所得模型：大小，确定性和接收行为的可比性。小型电子商务应用程序的初步结果非常令人鼓舞，因为自动生产的模型包括一组手动定义的金标准模型。
Model based testing relies on the availability of models that can be defined manually or by means of model inference techniques. To generate models that include meaningful state abstractions, model inference requires a set of abstraction functions as input. However, their specification is difficult and involves substantial manual effort. In this paper, we investigate a technique to automatically infer both the abstraction functions necessary to perform state abstraction and the finite state models based on such abstractions. The proposed approach uses a combination of clustering, invariant inference and genetic algorithms to optimize the abstraction functions along three quality attributes that characterize the resulting models: size, determinism and infeasibility of the admitted behaviors. Preliminary results on a small e-commerce application are extremely encouraging because the automatically produced models include the set of manually defined gold standard models.