  Widespread applications of deep neural networks (DNNs) benefit from DNN
testing to guarantee their quality. In the DNN testing, numerous test cases are
fed into the model to explore potential vulnerabilities, but they require
expensive manual cost to check the label. Therefore, test case prioritization
is proposed to solve the problem of labeling cost, e.g., activation-based and
mutation-based prioritization methods. However, most of them suffer from
limited scenarios (i.e. high confidence adversarial or false positive cases)
and high time complexity. To address these challenges, we propose the concept
of the activation graph from the perspective of the spatial relationship of
neurons. We observe that the activation graph of cases that triggers the
models' misbehavior significantly differs from that of normal cases. Motivated
by it, we design a test case prioritization method based on the activation
graph, ActGraph, by extracting the high-order node features of the activation
graph for prioritization. ActGraph explains the difference between the test
cases to solve the problem of scenario limitation. Without mutation operations,
ActGraph is easy to implement, leading to lower time complexity. Extensive
experiments on three datasets and four models demonstrate that ActGraph has the
following key characteristics. (i) Effectiveness and generalizability: ActGraph
shows competitive performance in all of the natural, adversarial and mixed
scenarios, especially in RAUC-100 improvement (~1.40). (ii) Efficiency:
ActGraph does not use complex mutation operations and runs in less time (~1/50)
than the state-of-the-art method.
