Reachability testing is an important approach to testing concurrent programs. It generates and exercises syn- chronization sequences automatically and on-the-fly without saving any test history. Existing reach, abilit...
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Reachability testing is an important approach to testing concurrent programs. It generates and exercises syn- chronization sequences automatically and on-the-fly without saving any test history. Existing reach, ability testing can be classified into exhaustive and t-way testing. Exhaustive testing is impractical in many cases whilie t-way testing may decrease the capability of fault detection in some cases. In this paper, we present a variable strengda reachability testing strategy, which adopts the dynamic framework of reachability testing and uses a variablestrengthcombinatorial strategy. Different parameter groups are provided with different covering strength. variablestrengthtesting covers no t-way combinations but the necessary combinations of parameters having mutual interactions in a concurrent program. It is more reasonable than t-way testing because uniform interactions between parameters do not often exist in concurrent systems. We propose a merging algorithm that implements the variable strength combinatorial testing strategy and conduct our experiment on several concurrent programs. The experimental results indicate that our variablestrength reachability testing reaches a good tradeoff between the effectiveness and efficiency. It can keep the same capability of fault detection as exhaustive reachability testing while substantially reducing the number of synchronization sequences and decreasing the execution time in most cases.
In deep neural networks (DNNs), each neuron in the post-layer receives the influence of all the neurons in the pre-layer. As we known, different connections in a DNN model have different weights. It means that, differ...
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ISBN:
(纸本)9781728108889
In deep neural networks (DNNs), each neuron in the post-layer receives the influence of all the neurons in the pre-layer. As we known, different connections in a DNN model have different weights. It means that, different combinations of pre-layer neurons have different effects on the post-layer neurons. Therefore, the variable strength combinatorial testing can reflect the effect of combination interaction of neurons in the pre-layer on the neurons in the post-layer. In this paper, we propose to adopt variable strength combinatorial testing technique on DNNs testing. In order to modeling the effect of combinatorial interaction of pre-layer neurons on the post-layer neurons, we propose three methods to construct variablestrengthcombinatorial interaction relationship for DNNs. The experimental results show that, 1) variablestrengthcombinatorial coverage criteria are discriminating to measure the adequacy of DNNs testing, and 2) there is correlation between variablestrengthcombinatorial coverage and adversarial detection.
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