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
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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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