TY - GEN
T1 - Adaptive fault detection for testing tenant applications in multi-tenancy SaaS systems
AU - Tsai, W. T.
AU - Li, Qingyang
AU - Colbourn, Charles J.
AU - Bai, Xiaoying
PY - 2013
Y1 - 2013
N2 - SaaS (Software-as-a-Service) often uses multi-tenancy architecture (MTA) where tenant developers compose their applications online using the components stored in the SaaS database. Tenant applications need to be tested, and combinatorial testing can be used. While numerous combinatorial testing techniques are available, most of them produce static sequences of test configurations and their goal is often to provide sufficient coverage such as 2-way interaction coverage. But the goal of SaaS testing is to identify those compositions that are faulty for tenant applications. This paper proposes an adaptive test configuration generation algorithm AR (Adaptive Reasoning) that can rapidly identify those faulty combinations so that those faulty combinations cannot be selected by tenant developers for composition. The AR algorithm has been evaluated by both simulation and real experimentation using a MTA SaaS sample running on GAE (Google App Engine). Both the simulation and experiment showed show that the AR algorithm can identify those faulty combinations rapidly. Whenever a new component is submitted to the SaaS database, the AR algorithm can be applied so that any faulty interactions with new components can be identified to continue to support future tenant applications.
AB - SaaS (Software-as-a-Service) often uses multi-tenancy architecture (MTA) where tenant developers compose their applications online using the components stored in the SaaS database. Tenant applications need to be tested, and combinatorial testing can be used. While numerous combinatorial testing techniques are available, most of them produce static sequences of test configurations and their goal is often to provide sufficient coverage such as 2-way interaction coverage. But the goal of SaaS testing is to identify those compositions that are faulty for tenant applications. This paper proposes an adaptive test configuration generation algorithm AR (Adaptive Reasoning) that can rapidly identify those faulty combinations so that those faulty combinations cannot be selected by tenant developers for composition. The AR algorithm has been evaluated by both simulation and real experimentation using a MTA SaaS sample running on GAE (Google App Engine). Both the simulation and experiment showed show that the AR algorithm can identify those faulty combinations rapidly. Whenever a new component is submitted to the SaaS database, the AR algorithm can be applied so that any faulty interactions with new components can be identified to continue to support future tenant applications.
KW - Adpative testing
KW - Combinatorial testing
KW - SaaS (Software-as-a-Service)
KW - Testing tenant applications
UR - https://www.scopus.com/pages/publications/84881126484
U2 - 10.1109/IC2E.2013.44
DO - 10.1109/IC2E.2013.44
M3 - 会议稿件
AN - SCOPUS:84881126484
SN - 9780769549453
T3 - Proceedings of the IEEE International Conference on Cloud Engineering, IC2E 2013
SP - 183
EP - 192
BT - Proceedings of the IEEE International Conference on Cloud Engineering, IC2E 2013
T2 - 1st IEEE International Conference on Cloud Engineering, IC2E 2013
Y2 - 25 March 2013 through 28 March 2013
ER -