A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques

Noha Medhat,Sherin M Moussa,Nagwa Lotfy Badr,Mohamed F Tolba

doi:10.1109/access.2020.3039931

Noha Medhat, Sherin M Moussa + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3039931

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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.

Highlights

The Internet of Things (IoT)-based systems are increasingly penetrating all business industries, in which the main characteristic of these systems is the heterogeneity of their components and technologies
The results indicate that Genetic Algorithms (GAs) works more accurate with large datasets, since it is a global Search Based Testing (SBT) technique that pursues a population of values rather than singular neighbor values as in the case of local SBT algorithms like the Simulated Annealing (SA) algorithm
Challenges are usually faced during the integration and regression testing of IoT systems, in which the nature of these systems requires continuous testing

Summary

INTRODUCTION

The Internet of Things (IoT)-based systems are increasingly penetrating all business industries, in which the main characteristic of these systems is the heterogeneity of their components and technologies. The selection and prioritization of test suites are vitally needed to execute the related TCs only during regression testing, avoiding redundancy and irrelevant TCs. On the other hand, Search Based Testing (SBT) techniques have been considered for TCs prioritization and reduction, such as Genetic Algorithms (GAs), Hill climbing, Ant Colony and Simulated Annealing (SA) techniques [8]. A scalable framework for prioritizing and selecting TCs in IoT-based systems (IoT-CIRTF) is introduced based on deep learning and SBT techniques This framework is intended to support continuous integration testing and regression testing for IoT-based systems by providing an optimized self-adaptive prioritized set of TCs to select from to continuously ensure the overall reliability of these systems upon the addition or removal of their independent components. The matched TCs are executed with respect to the change requests in the requirements -for regression testing - or to the newly added modules of the specific IoT system -for integration testing

RELATED WORK

Generate n number of TCs

IOT TEST CASES SELECTION AND PRIORITIZATION EXPERIMENTAL RESULTS

Findings

CONCLUSION