Testing Graph Searching Based Path Planning Algorithms by Metamorphic Testing Path planning algorithms play critical roles in the systems of robots and unmanned aerial vehicles (UAVs). However, it is always difficult to verify the correctness of the implementations for such algorithms because the planning oracles, the expected planning results, are usually hard to be obtained for complicate planning tasks. To improve software reliability, in this paper, we present a testing technique for verifying the implementations of graph searching based path planning algorithms deployed on robots and UAVs. Our approach is based on the technique of Metamorphic Testing, which has been shown considerable effectiveness in alleviating the absence of Oracle problems. According to the characteristics of graph searching based path planning problem, we present a framework to systematically design metamorphic relations. Based on the framework, six categories of metamorphic relations are proposed. We conduct the empirical analysis on 21 implements of three different path planning algorithms applied in a released business software project. The experimental results show that our approach can effectively detect dormant faults.

MTEA4Cloud: A Methodology For testing and optimising energyaware cloud systems Abstract   Currently, using conventional techniques for checking and optimising the energy consumption in cloud systems is unpractical, due to the massive computational resources required. An appropriate test suite focusing on the parts of the cloud to be tested must be efficiently synthesised and executed, while the correctness of the test results must be checked. Additionally, alternative cloud configurations that optimise the energetic consumption of the cloud must be generated and analysed accordingly, which is challenging.  To solve these issues we present  MTEA4Cloud , a formal approach to check the correctness xe2x80x93 from an energyaware point of view xe2x80x93 of cloud systems and optimise their energy consumption. To make the checking of energy consumption practical,  MTEA4Cloud  combines metamorphic testing, evolutionary algorithms and simulation. Metamorphic testing allows to formally model the underlying cloud infrastructure in the form of metamorphic relations. We use metamorphic testing to alleviate both the reliable test set problem, generating appropriate test suites focused on the features reflected in the metamorphic relations, and the oracle problem, using the metamorphic relations to check the generated results automatically.  MTEA4Cloud  uses evolutionary algorithms to efficiently guide the search for optimising the energetic consumption of cloud systems, which can be calculated using different cloud simulators.