Risk Analysis on Multi-Granular Flow Network for Software Integration Testing
Ying Wang, Zhiliang Zhu, Hai Bo Yu, Bo Yang · IEEE Transactions on Circuits & Systems II Express Briefs · 2017
This brief presents a model, a methodology, and an application scheme of risk assessment for information exchange system. The multi-granular flow network (MGFN) model serves as a basis for measuring the vulnerabilities and threats of components, and the failure consequences they bring to the system when a failure occurs. The risk factors of components are then quantified, assisted by a probabilistic risk analysis model. Furthermore, we apply the MGFN model and the risk assessment scheme in ordering class integration testing for object-oriented software system. By comparing our approach with the state-of-the-art integration test order algorithms from the perspectives of detection efficiency of severe faults and stubbing efforts, we show that classes with higher risk indexes can be tested in earlier integration steps, and that the total complexity of the established test stubs is minimized.