A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques
Medhat, Noha; Sherin M. Moussa; Badr, Nagwa; Tolba, Mohamed F.;
Abstract
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 system, 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. In this paper, 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.
Other data
Title | A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques | Authors | Medhat, Noha ; Sherin M. Moussa ; Badr, Nagwa ; Tolba, Mohamed F. | Keywords | Deep learning;test case prioritization;test case selection;search-based techniques;regression testing;integration testing | Issue Date | 1-Jan-2020 | Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Journal | IEEE Access | Volume | 8 | ISSN | 2169-3536 | DOI | 10.1109/ACCESS.2020.3039931 | Scopus ID | 2-s2.0-85097184092 | Web of science ID | WOS:000597214900001 |
Attached Files
File | Description | Size | Format | |
---|---|---|---|---|
medhat2020.pdf | 3.79 MB | Unknown | View/Open |
Similar Items from Core Recommender Database
Items in Ain Shams Scholar are protected by copyright, with all rights reserved, unless otherwise indicated.