English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Widespread applications of deep neural networks (DNNs) benefit from DNNtesting to guarantee their quality. In the DNN testing, numerous test cases arefed into the model to explore potential vulnerabilities, but they requireexpensive manual cost to check the label. Therefore, test case prioritizationis proposed to solve the problem of labeling cost, e.g., activation-based andmutation-based prioritization methods. However, most of them suffer fromlimited scenarios (i.e. high confidence adversarial or false positive cases)and high time complexity. To address these challenges, we propose the conceptof the activation graph from the perspective of the spatial relationship ofneurons. We observe that the activation graph of cases that triggers themodels' misbehavior significantly differs from that of normal cases. Motivatedby it, we design a test case prioritization method based on the activationgraph, ActGraph, by extracting the high-order node features of the activationgraph for prioritization. ActGraph explains the difference between the testcases to solve the problem of scenario limitation. Without mutation operations,ActGraph is easy to implement, leading to lower time complexity. Extensiveexperiments on three datasets and four models demonstrate that ActGraph has thefollowing key characteristics. (i) Effectiveness and generalizability: ActGraphshows competitive performance in all of the natural, adversarial and mixedscenarios, 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.

ActGraph: Prioritization of Test Cases Based on Deep Neural Network Activation Graph