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

Abstract

Test cases prioritization has been excessively considered for continious regression and integration testing in Internet of Things based systems to apply multilevel testing activities. Various number of devices, sensors and acctuators are connected together through the internet using different technologies, which requires extensively testing the effeciency of these components and the transferred data between them. Due to the number of the connected components has dramatically increased, causing a direct proportional increase in the number of test cases.Studies that handle the augmentation of the number of test cases for traditional systems lack effeciency when applied for Internet of Things based systems. In this paper, we introduce an enhancement for test cases prioritization using Hill Climbing algorithm as a local search based technique, adapted to achieve tangible effeciency. It is integrated with the 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 Internet of Things-based system datasets.

DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES

“…The experimental results indicate the optimum performance of DFDP in different aspects, as: (1) ensuring the totally fresh IoT data, (2) managing IoT data impreciseness by 93.2% accuracy level, (3) achieving an average 50% and 53% of data reduction and processing time reduction respectively (4) accurately predicts IoT data with 91.8% accuracy rate. Our future work is focusing on examining the DFDP approach for multiple types of software testing in large and distributed frameworks, such as the multiagent environments [44,45], IoT-based systems [46][47][48][49][50][51][52][53] and service-oriented systems [54,55]. We also plan to improve the DFDP approach via considering IoT data heterogeneity by supporting different data structures.…”