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

Test cases prioritization has been excessively considered for continious regression and integration testing in Internet of Things based systems to apply multilevel activities. Various number devices, sensors acctuators are connected together through the internet using different technologies, which requires extensively effeciency these components transferred data between them. Due dramatically increased, causing a direct proportional increase test cases.Studies that handle augmentation traditional lack when applied systems. In this paper, we introduce an enhancement Hill Climbing algorithm as local search technique, adapted achieve tangible effeciency. It is integrated with LSTM deep learning classification purposes. The results evaluated precision, where it achieved 80% 97% testing, 93% 88% two Things-based system datasets.