Using Machine Learning on Testing IoT Applications: a systematic mapping

L. M. Freitas, Valéria Lelli
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Abstract

Internet of Things (IoT) devices are increasingly present in people’s daily lives. Thus has increased research interest in investigating strategies that can ensure that these applications work as expected considering specific and vital characteristics of IoT, for example, security, performance and interoperability. In a testing point of view, there is a need to optimize and define an efficient strategy, from its planning to its execution. Considering all the steps that can be taken to test an IoT application, this process, if performed manually, can demand great effort and time. Machine learning (ML) algorithms have been applied in several areas of computing in order to optimize and automate processes that involve large volumes of data. In this paper, we present a systematic mapping resulting in 40 studies that highlights techniques or approaches that use machine learning algorithms for the most diverse goals within the IoT application testing process, such as the use of neural networks for predicting the cost of time in the preparation and execution of tests; identification of security attacks; and automatic generation of test cases from textual language. We also identified that the vast majority of testing techniques are focused on a specific IoT characteristic (e.g., security, performance), specially security, and apply the machine learning algorithm in two ways: directly in the algorithm, called predictive maintenance, or during the execution of planned tests, both of them bring difficulties related to extracting and defining data to train ML algorithms.
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使用机器学习测试物联网应用:系统映射
物联网(IoT)设备越来越多地出现在人们的日常生活中。因此,考虑到物联网的特定和重要特征,例如安全性、性能和互操作性,研究策略可以确保这些应用按预期工作的研究兴趣增加了。从测试的角度来看,需要优化和定义一个有效的策略,从它的计划到它的执行。考虑到测试物联网应用程序可以采取的所有步骤,如果手动执行此过程,可能需要大量的精力和时间。机器学习(ML)算法已经应用于计算的几个领域,以优化和自动化涉及大量数据的过程。在本文中,我们提出了一个系统的映射,导致40项研究,突出了在物联网应用测试过程中使用机器学习算法实现最多样化目标的技术或方法,例如使用神经网络来预测准备和执行测试的时间成本;识别安全攻击;从文本语言自动生成测试用例。我们还发现,绝大多数测试技术都专注于特定的物联网特征(例如,安全性,性能),特别是安全性,并以两种方式应用机器学习算法:直接在算法中应用,称为预测性维护,或者在执行计划测试期间,这两种方法都带来了与提取和定义数据以训练ML算法相关的困难。
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