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Metamorphic testing is an effective technique for testing systems that do not have test oracles, for which it is practically impossible to know the correct output of an arbitrary test input. In metamorphic testing, instead of checking the correctness of a test output, the satisfaction of metamorphic relation among test outputs is checked. If a violation of the metamorphic relation is found, the system implementation must have some defects. However, a randomly or accidently generated incorrect output may satisfy a metamorphic relation as well. Therefore, checking only metamorphic relations is not good enough to ensure the testing quality. In this paper, we propose a self-checked metamorphic testing approach, which integrates structural testing into metamorphic testing, to detect subtle defects in a system implementation. In our approach, metamorphic testing results are further verified by test coverage information, which is automatically produced during the metamorphic testing. The effectiveness of the approach has been investigated through testing an image processing program.

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Objective: Exploring resting-state functional networks using functional magnetic resonance imaging (fMRI) is a hot topic in the field of brain functions. Previous studies suggested that the frequency dependence between blood oxygen level dependent (BOLD) signals may convey meaningful information regarding interactions between brain regions.Methods: In this article, we introduced a novel frequency clustering analysis method based on Hilbert-Huang Transform (HHT) and a label-replacement procedure. First, the time series from multiple predefined regions of interest (ROIs) were extracted. Second, each time series was decomposed into several intrinsic mode functions (IMFs) by using HHT. Third, the improved k-means clustering method using a label-replacement method was applied to the data of each subject to classify the ROIs into different classes.Results: Two independent resting-state fMRI dataset of healthy subjects were analyzed to test the efficacy of method. The results show almost identical clusters when applied to different runs of a dataset or to different datasets, indicating a stable performance of our framework.Conclusions and Significance: Our framework provided a novel measure for functional segregation of the brain according to time-frequency characteristics of resting state BOLD activities.