This book introduces readers to an advanced combinatorial testing approach and its application in the cloud environment. Based on test algebra and fault location analysis, the proposed combinatorial testing method can support experiments with 250 components (with 2 * (250) combinations), and can detect the fault location based on the testing results. This function can efficiently decrease the size of candidate testing sets and therefore increase testing efficiency. The proposed solution’s effectiveness in the cloud environment is demonstrated using a range of experiments.
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Wei-Tek Tsai is currently a professor at both the School of Computing Informatics and Decision Systems Engineering at Arizona State University, USA and the School of Computer Science and Engineering at Beihang University, China. He received his PhD and MS in Computer Science from the University of California at Berkeley, and his BS in Computer Science and engineering from MIT. He has produced over 400 papers in various journals and conferences, received two Best Paper awards, and awarded several Guest Professorships. His work has been supported by the US Department of Defense, Department of Education, National Science Foundation, the EU, and industrial companies such as Intel, Fujitsu and Guidant. In the last ten years, he has focused his energies on service-oriented computing and SaaS, as well as various aspects of software engineering including blockchain, architecture, testing, and maintenance.
Guanqiu Qi received his PhD in Computer Science from Arizona State University in 2014. His research interests span many aspects of software engineering, such as SaaS (Software-as-a-Service), TaaS (Testing-as-a-Service), Big Data Testing, Combinatorial Testing, and Service-Oriented Computing, as well as blockchain.
This book introduces readers to an advanced combinatorial testing approach and its application in the cloud environment. Based on test algebra and fault location analysis, the proposed combinatorial testing method can support experiments with 250 components (with 2 * (250) combinations), and can detect the fault location based on the testing results. This function can efficiently decrease the size of candidate testing sets and therefore increase testing efficiency. The proposed solution s effectiveness in the cloud environment is demonstrated using a range of experiments.
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