Testing Big Data (Assuring quality of big data)

Abstract

Internet of Things (IoT) systems is fast evolving nowadays, in which huge amounts of data are produced rapidly from heterogeneous sources. The nature of IoT-based systems implies many challenges, in terms of operation, security, quality control and data management. Thus, testing such systems is a key element to their success. We present in this thesis a comprehensive study for the main testing techniques and tools that have been considered for the IoT-based systems. Detailed comparison and analytical criticism are conducted, identifying the different testing types that have been applied for the main application domains. The research gaps are addressed, which highlight the future directions that can be adopted. Studies that handle the augmentation of the number of test cases for traditional systems lack efficiency when applied for IoT-based systems.

Tremendous systems are rapidly evolving based on the trendy Internet of Things (IoT) in various domains. Different technologies are used for communication between the massive connected devices through all layers of the IoT systems, causing many security and performance issues. Regression and integration testing are considered repeatedly, in which the vast costs and efforts associated with the frequent execution of these inflated test suites hinder the adequate testing of such systems. This necessitates the focus on exploring innovative scalable testing approaches for large test suites in IoT-based systems.

A scalable framework for continuous integration and regression testing in IoT-based systems (IoT-CIRTF) is proposed, based on IoT-related criteria for test case prioritization and selection. The framework utilizes