Abstract

This research works on the optimization of pairwise testing techniques for cloud application development by focusing on their effectiveness in dynamic cloud environments. The sub-research questions addressed by the study are five, namely: the effectiveness of existing combinatorial testing techniques, challenges in the generation of minimal pairwise test sets, the scalability of Testing-as-a-Service (TaaS), the comparison of existing pairwise techniques, such as IPOG, AETG, MIPOG, and ACO, and the feasibility of optimized pairwise test cases. This will quantitatively evaluate the factors using data from 2015-2023, making use of statistical analyses in hypothesis testing to check the hypotheses set forth for testing efficiency, fault detection, and scalability. The results validate the need for optimization of present combinatorial methods, reveal advancements in algorithms used for test set generation, and emphasize the contribution of TaaS towards the scalability. The study also sheds comparative light on existing techniques and displays benefits in optimized test cases to the detection of faults and testing efficiency. Findings fill in literature gaps by suggesting a tailored approach toward cloud testing and paving way for future innovations in the realm of efficient methodologies related to testing cloud applications.