الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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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 search-based techniques to provide an optimized prioritized set of test cases to select from. The selection is based on a trained prediction model for IoT standard components using supervised deep learning algorithms to continuously ensure the overall reliability of IoT-based systems. The experiments are held on two GSM datasets. The experimental results achieved prioritization accuracy up to 90% and 92% for regression testing and integration testing respectively. This provides an enhanced and efficient framework for continuous testing of IoT-based systems, as per IoT-related criteria for the prioritization and selection purposes.
Test cases prioritization has been excessively considered for continuous regression and integration testing in IoT based systems to apply multilevel testing activities. Various numbers of devices, sensors and actuators are connected together through the internet using different technologies, which requires extensively testing the efficiency of these components and the transferred data between them. Due to the nature of IoT-based systems, the number of the connected components has dramatically increased, causing a direct proportional increase in the number of test cases.
In this thesis, we introduce an enhancement for test cases prioritization using Hill Climbing algorithm as a local search based technique, adapted to achieve tangible efficiency with IoT-based systems. It is integrated with the Long Short Term Memory (LSTM) deep learning algorithm for test cases classification purposes. The results of the test cases prioritization using Hill Climbing for regression and integration testing are evaluated using precision, where it achieved 80% and 97% for regression testing, and 93% and 88% for integration testing using two IoT-based system datasets.