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Test case priority ranking method based on neuron activation frequency analysis

A technology of prioritization and test cases, applied in neural learning methods, neural architecture, software testing/debugging, etc., can solve problems such as no longer applicable

Pending Publication Date: 2020-04-24
BEIJING UNIV OF TECH +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, the test case prioritization techniques of traditional software are no longer applicable to computer software for deploying deep learning systems.

Method used

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  • Test case priority ranking method based on neuron activation frequency analysis
  • Test case priority ranking method based on neuron activation frequency analysis
  • Test case priority ranking method based on neuron activation frequency analysis

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Embodiment Construction

[0025] The invention proposes a test case priority sorting method based on the analysis of neuron activation frequency, which can sort the test cases in the test set from high to low according to the degree of easy recognition error by neural network.

[0026] The test case prioritization method proposed by the present invention will be described in detail below in conjunction with specific implementation. Taking 5 test cases as an example, the clustering algorithm used in this case is K-Means. The workflow of this method is as follows:

[0027] Among them, the test set is {t 1 ,t 2 ,t 3 ,t 4 ,t 5 ,t 6}, the set of neurons in the penultimate layer of the neural network is {n 1 ,n 2 ,n 3 ,n 4 ,n 5}. test set t 1 ,t 2 ,t 3 for category k 1 , t 4 ,t 5 ,t 6 for category k 2 . The neurons activated by each test case are {n 1 ,n 2 ,n 3},{n 1 ,n 2 ,n 3 ,n 4},{n 1 ,n 2 ,n 3 ,n 4 ,n 5},{n 1 ,n 4 ,n 5},{n 2 ,n 3 ,n 4 ,n 5},{n 1 ,n 2 ,n 3 ,n 4 ,...

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Abstract

The invention discloses a test case priority ranking method based on neuron activation frequency analysis, the input is a neural network to be tested, historical data and a test set, and the output isa test set subjected to test case priority ranking. The main idea of the method is to divide a neuron set in a neural network into a frequently activated neuron set and a non-frequently activated neuron set, and sort test cases by calculating the number of frequently activated neurons activated by new test cases and the number of non-frequently activated neurons activated by the new test cases. The method comprises the following specific steps of (1) determining a neuron frequently-activated neuron subset and a non-frequently-activated neuron subset of each type of data, and (2) performing priority ranking on test cases according to the number of neurons in the frequently-activated subsets and the non-frequently-activated subsets of to-be-ranked data.

Description

technical field [0001] The invention relates to the field of computer software testing, in particular to a test case prioritization method based on neuron activation frequency analysis. Background technique [0002] With the rapid development of technologies such as deep neural networks, deep learning systems have been widely used and widely deployed in areas such as autonomous driving, speech recognition, and image recognition. In order to ensure the safety and reliability of these computer software applying deep learning technology, computer software must be fully tested before it is put into use. However, due to the difficulty of explaining the deep learning system, the cost of testing the computer software that deploys the deep learning system is very expensive. On the one hand, in order to cover the huge input space of the deep learning system, it is necessary to collect as many test cases as possible. The process may take a long time and consume a lot of resources; on...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36G06N3/04G06N3/08
CPCG06F11/3684G06F11/3688G06N3/08G06N3/045
Inventor 张凯张永泰严俊晏荣杰高红雨苏航
Owner BEIJING UNIV OF TECH
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