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

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.

The Internet of Things and Architectures of Big Data Analytics: Challenges of Intersection at Different Domains

“…Further, it could support self-monitoring architectural models for high fault tolerance. Tracking testing progress technique can be applied to validate the testing process and ensure tests completeness [103]. 4) Domain-Specific Language Handler: As per our study, the SW functional suitability has become one of the major SWE challenges that has not been addressed yet in IoT-based systems to the best of our knowledge.…”

Enhancing Test Cases Prioritization for Internet of Things based systems using Search-based Technique

“…The challenges of testing in IoT-based systems were addressed in [1] on the different testing levels, where a lackage was indicated when testing IoT-based systems during the selection and prioritization of the IoT system TCs . In [6], a testing technique was proposed in order to effectively apply integration and regression testing over IoT based systems, which was by merging deep learning LSTM algorithm for the IoT TCs selection. LSTM classifier was applied in order to classify the IoT system requirements into the main IoT system components which are the user devices, sensors and actuators, data processing, and protocols and gateways.…”

“…This paper presents an enhanced framework for integration and regression testing in IoTbased systems by integrating the Hill Climping (HC) algorithm as one of the SBTs for TCs prioritization on the top of the deep learning LSTM algorithm at the IoT-based Continuous Integration and Regression Testing Framework (IoT-ECIRTF) [6], as shown in Fig. 1.…”

“…IoT-CIRTF in [6] prioritizes IoT-based TCs using SA (Local SBT) and GA (Global SBT) algorithms. In this module, the Hill Climbing (HC) algorithm is proposed to provide higher accuracy than using SA for IoT-based TCs prioritization to enhance the framework efficiency.…”

“…Both the selection and prioritization of TCs for integration or regression testing face diverse challenges because of the increasing volume of the needed TCs to examine [5]. TCs selection for IoT-based systems was investigated in [6] using the Long Short Term Memory (LSTM) deep learning classifier [7] for reduction purposes. Moreover, a representation of TCs prioritization was discussed in [7] using Genetic algorithm (GA) and Simulated Annealing (SA) algorithm, resulted in a low accuracy level.…”