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Test result prediction method and device, computer equipment and storage medium

A technology for test results and prediction methods, applied in computer parts, calculations, software testing/debugging, etc., can solve problems such as test blind spots, parameter combinations that cannot be found, and difficult to achieve, so as to reduce the amount of test calculation and save Test the effect of computing resources

Pending Publication Date: 2020-11-17
LYNXI TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] In automated software testing, in order to detect specific exceptions caused by certain parameter combinations, the common design method is to traverse the parameters of the object under test, but limited by computing resources, when the input parameters contained in the tested function module When there are many and each parameter has multiple values, it is difficult to realize in the actual automated testing work if the method of permutation and combination is used to traverse all possible input combinations
[0003] To solve the above problems, the existing test scheme can only test the parameter boundaries and typical parameter combinations as exhaustively as possible. This method cannot test all parameter combinations, and it is easy to cause a large number of parameter combinations that can cause abnormal results to be undiscovered. Test blind area

Method used

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  • Test result prediction method and device, computer equipment and storage medium
  • Test result prediction method and device, computer equipment and storage medium
  • Test result prediction method and device, computer equipment and storage medium

Examples

Experimental program
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Embodiment 1

[0032] figure 1 It is an implementation flow chart of a method for predicting test results provided by Embodiment 1 of the present invention. This embodiment is applicable to the case where all parameter combinations are tested through a classification model, and the method can be executed by a device for predicting test results. The device can be realized by software and / or hardware, and can generally be integrated in various computer devices capable of running neural networks (for example, desktop computers, servers or notebook computers, etc.). The method of the embodiment of the present invention specifically includes the following steps:

[0033] S110. Input the seed sequence into the object under test for calculation, and obtain a test result sequence.

[0034] Wherein, the object under test refers to the object to be tested. Specifically, the object under test may be a functional module, a calculation operator that realizes a set calculation function, or a calculation ...

Embodiment 2

[0046] Figure 2a It is an implementation flowchart of a method for predicting test results provided by Embodiment 2 of the present invention. In this embodiment, on the basis of the above-mentioned embodiments, the first number of seed sequences are input into the measured object for calculation, and the operations before obtaining the test result sequence are further refined. Correspondingly, the method in the embodiment of the present invention specifically includes the following steps:

[0047] S210. Construct a first number of seed sequences according to multiple input parameters matching the measured object, where the seed sequences correspond to a group of selectable values ​​of each input parameter.

[0048]In this embodiment, the measured object includes multiple input parameters, and each input parameter corresponds to a value range, wherein the value range can be a discrete interval or a continuous interval. When the value range is a continuous space, it is necessa...

Embodiment 3

[0085] Figure 3a It is an implementation flowchart of a method for predicting test results provided by Embodiment 3 of the present invention. In this embodiment, before inputting the second number of extended sequences into the classification model and respectively inputting the second number of extended sequences into the The process after the classification model is further refined.

[0086] Correspondingly, the method in the embodiment of the present invention specifically includes the following steps:

[0087] S310. Input the first quantity of seed sequences into the object under test for calculation, and obtain a test result sequence.

[0088] S320. Construct training samples that match various subsequences according to the numerical relationship between the test result sequences.

[0089] S330. Using training samples, train to obtain a classification model.

[0090] S340. Construct a complete sequence set according to selectable value sets respectively corresponding ...

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Abstract

The embodiment of the invention discloses a test result prediction method and device, computer equipment and a storage medium. The method comprises the following steps: inputting a first number of seed sequences into a tested object for calculation to obtain test result sequences; constructing a training sample matching each seed sequence according to a numerical relationship among the test resultsequences; training by using the training sample to obtain a classification model; and inputting the second number of extended sequences into the classification model to obtain a prediction test result. According to the technical scheme provided by the embodiment of the invention, a large amount of computing resources are saved while the parameter space of the tested object is approximately tested completely.

Description

technical field [0001] Embodiments of the present invention relate to automated testing and artificial intelligence technologies, and in particular to a test result prediction method, device, computer equipment, and storage medium. Background technique [0002] In automated software testing, in order to detect specific exceptions caused by certain parameter combinations, the common design method is to traverse the parameters of the object under test, but limited by computing resources, when the input parameters contained in the tested function module When there are many and each parameter has a variety of values, it is difficult to realize in the actual automated testing work if the method of permutation and combination is used to traverse all possible input combinations. [0003] To solve the above problems, the existing test scheme can only test the parameter boundaries and typical parameter combinations as exhaustively as possible. This method cannot test all parameter co...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36G06K9/62
CPCG06F11/3692G06F18/24G06F18/214
Inventor 马欣金罗军吴臻志
Owner LYNXI TECH CO LTD