CN117056239A

CN117056239A - Method, device, equipment and storage medium for determining test function using characteristics

Info

Publication number: CN117056239A
Application number: CN202311314146.9A
Authority: CN
Inventors: 张婧婧; 邓路
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2023-10-11
Filing date: 2023-10-11
Publication date: 2023-11-14
Anticipated expiration: 2043-10-11
Also published as: CN117056239B

Abstract

The application discloses a method, a device, equipment and a storage medium for determining the use characteristics of a test function, and belongs to the field of software testing. The method comprises the following steps: acquiring a first association weight and a second association weight of the node to be allocated, wherein the first association weight is used for indicating the social association degree of the node to be allocated and a first number of first central nodes belonging to a first group, and the second association weight is used for indicating the social association degree of the node to be allocated and a first number of second central nodes belonging to a second group; adding the node to be allocated to a first group to which a first number of first central nodes belong, and setting a test function based on a first parameter in the first group, the first parameter and a second parameter set for the test function being different in the second group, in the case that the first association weight exceeds the second association weight; and carrying out statistical calculation on the use information of the first number of first central nodes on the test function to obtain first use characteristics of the test function under the first parameters.

Description

Method, device, equipment and storage medium for determining test function using characteristics

Technical Field

The present application relates to the field of software testing, and in particular, to a method, an apparatus, a device, and a storage medium for determining a test function usage feature.

Background

The network effect is that the user's usage behavior may be affected by friends with social relationships.

In the related technology, when the test function is set by using different parameters for the first user and the second user in the comparison experiment, and the first user and the second user have social relationship; the use condition of the first user on the test function is affected by the setting parameters of the second user, and the use condition of the parameter setting of the first user cannot be accurately reflected.

How to reduce the influence caused by network effect in the comparative test is a problem to be solved.

Disclosure of Invention

The application provides a method, a device, equipment and a storage medium for determining the use characteristics of a test function, wherein the technical scheme is as follows:

according to an aspect of the present application, there is provided a method of determining a test function usage feature, the method comprising:

acquiring a first association weight and a second association weight of a node to be allocated, wherein the first association weight is used for indicating social association degrees of the node to be allocated and a first number of first central nodes belonging to a first group, the second association weight is used for indicating social association degrees of the node to be allocated and a first number of second central nodes belonging to a second group, the node to be allocated is an account number of an unassigned group, and the first central node and the second central node are account numbers of use information of the test function to be counted;

Adding the node to be allocated to the first group to which the first number of first center nodes belong, and setting the test function based on a first parameter in the first group, the first parameter and a second parameter set for the test function being different in the second group, in a case that the first association weight exceeds the second association weight;

and carrying out statistical calculation on the use information of the test function by the first number of first central nodes to obtain a first use characteristic of the test function under the first parameter.

In an alternative design of the application, the method further comprises:

setting a test function based on a second parameter in the second group, and carrying out statistical calculation on the use information of the test function by a second number of second center nodes to obtain a second use characteristic of the test function under the second parameter;

and obtaining the total usage characteristics of the first number of first central nodes and the second number of second central nodes by comparing the first usage characteristics with the second usage characteristics.

In an alternative design of the application, the full use feature comprises a full gain feature; the step of obtaining the full usage feature of pushing the first parameter to the first number of first central nodes and the second number of second central nodes by comparing the first usage feature and the second usage feature comprises the following steps:

Correcting the first use feature based on a first ratio between the first quantity and a first value to obtain a first full-quantity feature, wherein the first full-quantity feature is used for indicating the first central node and the second central node to set the use feature of the test function based on the first parameter, and the first value is the sum of the first quantity and the second quantity;

correcting the second use feature based on a second ratio between the second number and the first value to obtain a second full feature, wherein the second full feature is used for indicating the first central node and the second central node to set the use feature of the test function based on the second parameter;

and determining the full gain characteristic according to the difference value of the first full characteristic and the second full characteristic, wherein the full gain characteristic is used for indicating the use characteristic gain generated by the test function relative to the second parameter.

According to another aspect of the present application, there is provided a determination apparatus for testing a function usage characteristic, the apparatus comprising:

the system comprises an acquisition module, a judgment module and a test module, wherein the acquisition module is used for acquiring a first association weight and a second association weight of a node to be allocated, the first association weight is used for indicating the social association degree of the node to be allocated and a first number of first central nodes belonging to a first group, the second association weight is used for indicating the social association degree of the node to be allocated and a first number of second central nodes belonging to a second group, the node to be allocated is an account number of an unallocated group, and the first central node and the second central node are account numbers of use information of the test function to be counted;

A processing module, configured to add the node to be allocated to the first group to which the first number of first central nodes belong, if the first association weight exceeds the second association weight, and set the test function in the first group based on a first parameter, where the first parameter and a second parameter set by the second group for the test function are different;

the processing module is further configured to perform statistical calculation on usage information of the test function by the first number of first central nodes, so as to obtain a first usage feature of the test function under the first parameter.

In an alternative design of the application, the acquisition module is further configured to:

acquiring the first number of first sub-weights and the second number of second sub-weights of the nodes to be distributed; the first number of first center nodes and the first number of first sub-weights are in one-to-one correspondence, and the second number of second center nodes and the second number of second sub-weights are in one-to-one correspondence;

the first associated weight is determined according to the first number of first sub-weights, and the second associated weight is determined according to the second number of second sub-weights.

determining the ratio of a first affinity to a first set as the first sub-weights, and obtaining the first number of first sub-weights, wherein the first affinity is the social affinity between the node to be distributed and the first center node, and the first set is the sum of the social affinities between the first center node and each associated node with social relations;

and determining the ratio of a second affinity to a second set as the second sub-weight, and obtaining the second number of second sub-weights, wherein the second affinity is the social affinity between the node to be distributed and the second center node, and the second set is the sum of the social affinities between the second center node and each associated node with social relations.

In an alternative design of the application, the processing module is further configured to:

adding the node to be allocated to the first group to which the first number of first center nodes belong, in case a relative error between the first and second associated weights exceeds a first threshold, and setting the test function based on the first parameter in the first group;

The first threshold is used for indicating that the social association degree of the node to be distributed and the first center node is obviously higher than that of the second center node.

In an alternative design of the application, the first group is an experimental group and the second group is a control group; the first parameter is a test parameter of the test function, and the second parameter is an initial parameter of the test function.

determining the first number of first central nodes belonging to the first group and the second number of second central nodes belonging to the second group in a random manner at n central nodes;

dividing n center nodes into at least two node types according to the number of associated nodes with social relations in each center node in the n center nodes;

in each of the at least two node types, a center node is added to the first group or the second group in a random manner.

Determining an ith center node as a first node type under the condition that the number of social friends of the ith center node in the n center nodes is greater than a number threshold;

and determining the ith center node as a second node type under the condition that the number of the social friends of the ith center node in the n center nodes is smaller than or equal to a number threshold.

and randomly determining the n central nodes according to a preset proportion in an undirected graph constructed by at least n+1 nodes.

In an alternative design of the present application, the social association degree is determined according to at least one of historical session information, historical interaction information, common group information, and friend labels between two nodes.

According to another aspect of the present application, there is provided a computer apparatus comprising a processor and a memory having stored therein at least one instruction, at least one program, a set of codes or a set of instructions, the at least one instruction, the at least one program, the set of codes or the set of instructions being loaded and executed by the processor to implement the method of determining a test function usage characteristic as described in the above aspect.

According to another aspect of the present application, there is provided a computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by a processor to implement a method of determining a test function usage characteristic as described in the above aspect.

According to another aspect of the present application, there is provided a computer program product comprising computer instructions stored in a computer readable storage medium, from which a processor reads and executes the computer instructions to implement the method of determining a test function usage characteristic as described in the above aspects.

The technical scheme provided by the application has the beneficial effects that at least:

the first association weight and the second association weight of the nodes to be distributed quantitatively describe the social association degree between the nodes, so that the network effect is described through the social association degree to cause interference to the test function; under the condition that the node to be distributed and the first central node with high social association degree are in different groups, the use of the first central node for the test function is affected by social communication and parameters of the test function; because the parameters set by the nodes to be distributed in different groups and the first central node in the test function are different, the first central node performs social communication to influence the parameters of the test function so as to generate interference; by adding the node to be distributed to the first group with high social connection degree, the same test function parameters can be set for the node to be distributed and the first center node with high social connection degree, so that the problem that the node to be distributed interferes with the service condition of the test function through interaction modes such as social communication and the like is avoided, and the influence of network effect on the service condition of the test function is reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a computer system provided in accordance with an exemplary embodiment of the present application;

FIG. 2 is a schematic diagram of a method for determining test function usage characteristics provided by an exemplary embodiment of the present application;

FIG. 3 is a flowchart of a method for determining test function usage characteristics provided by an exemplary embodiment of the present application;

FIG. 4 is another flow chart of a method for determining test function usage characteristics provided by an exemplary embodiment of the present application;

FIG. 5 is a further flowchart of a method for determining test function usage characteristics provided by an exemplary embodiment of the present application;

FIG. 6 is a further flowchart of a method for determining test function usage characteristics provided by an exemplary embodiment of the present application;

FIG. 7 is a further flowchart of a method for determining test function usage characteristics provided by an exemplary embodiment of the present application;

FIG. 8 is a block diagram of a test function usage feature determination device provided in an exemplary embodiment of the present application;

fig. 9 is a block diagram of a server according to an exemplary embodiment of the present application.

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail with reference to the accompanying drawings.

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.

The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region. For example, the information of social association degree, affinity and the like related to the application is obtained under the condition of full authorization.

It should be understood that, although the terms first, second, etc. may be used in this disclosure to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first parameter may also be referred to as a second parameter, and similarly, a second parameter may also be referred to as a first parameter, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.

FIG. 1 shows a schematic diagram of a computer system provided by one embodiment of the application. The computer system may implement a system architecture that becomes a method of determining features for use in testing functions. The computer system may include: a terminal 100 and a server 200.

The terminal 100 may be an electronic device such as a mobile phone, a tablet computer, a vehicle-mounted terminal (car), a wearable device, a PC (Personal Computer ), or the like. The terminal 100 may be provided with a client for running a target application program, which may be a test function usage feature determination application program, or may be another application program provided with a test function usage feature determination function, which is not limited in the present application. The present application is not limited to the form of the target Application program, and may be a web page, including but not limited to an App (Application), an applet, etc. installed in the terminal 100.

The server 200 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud computing services. The server 200 may be a background server of the target application program, and is configured to provide a background service for a client of the target application program.

According to the method for determining the test function usage characteristics, provided by the embodiment of the application, an execution main body of each step can be computer equipment, and the computer equipment refers to electronic equipment with data calculation, processing and storage capacity. Taking the implementation environment of the solution shown in fig. 1 as an example, the method for determining the usage characteristics of the test function may be performed by the terminal 100 (for example, the method for determining the usage characteristics of the test function may be performed by a client terminal installing a running target application program in the terminal 100), the method for determining the usage characteristics of the test function may be performed by the server 200, or the method for determining the usage characteristics of the test function may be performed by the terminal 100 and the server 200 in an interactive and coordinated manner, which is not limited in the present application.

In addition, the technical scheme of the application can be combined with a block chain technology. For example, the disclosed test function uses a feature determination method in which some of the data involved (data such as direction information of the first road and the second road) may be saved on the blockchain. Communication between the terminal 100 and the server 200 may be performed through a network, such as a wired or wireless network.

Fig. 2 provides a schematic diagram of a method for determining a test function usage characteristic according to an exemplary embodiment of the present application.

In this embodiment, the first group 302 includes five first center nodes: a first node 302a, a second node 302b, a third node 302c, a fourth node 302d, a fifth node 302e; the second group 304 includes five second center nodes, which are: a sixth node 304a, a seventh node 304b, an eighth node 304c, a ninth node 304d, a tenth node 304e.

It will be appreciated that in different embodiments, the first and second groups 302, 304 may include more or fewer central nodes; the number of first center nodes in the first group 302 may be the same as or different from the number of second center nodes in the second group 304.

Illustratively, there is a social relationship between the node to be assigned 306 and the first node 302a, the fourth node 302d, the fifth node 302e in the first group 302; there is a social relationship between the node to be allocated 306 and the ninth node 304d in the second set 304. Illustratively, the node to be assigned 306 is indicated in a wired manner as having a social relationship with the first and second central nodes having a social relationship.

Illustratively, the first association weight 312 is used to indicate the degree of social association of the node to be allocated 306 and three first central nodes in the first group 302 that have social relationships; the second association weight 314 is used to indicate the degree of social association of the node to be assigned 306 with a second central node of the second group 304 that has a social relationship. Specifically, the social association degree is determined according to at least one of a historical frequency of conversation between two nodes, the number of shared groups, the historical number of interactions performed in the social streaming media, and a friend relationship grouping tag between two nodes.

Subtracting the second association weight 314 from the first association weight 312 to obtain a first difference value, and determining the ratio of the first difference value to the second association weight 314 as a relative error 316a; in case the relative error 316a exceeds the first threshold 316b, adding the node to be allocated 306 to the first group 302 to which the first central node belongs; illustratively, the first group 302 includes five first center nodes in the first group 302, and the nodes to be allocated 306 added to the first group 302 are non-center nodes; it will be appreciated that a greater number of non-central nodes may be included in the first group; illustratively, adding the node to be allocated 306 to the first group 302 is determined based on the degree of social association between the node to be allocated 306 and the first central node in the first group 302; illustratively, the nodes added in the first group 302 are added according to a degree of closeness around the first central node in the dimension of the degree of social association.

Illustratively, the test functions are set in the first group 302 based on the first parameters 322, and the usage information of the test functions by the five first central nodes in the first group 302 is statistically calculated to obtain the first usage characteristics 332; the test functions are set in the second group 304 based on the second parameters 324, and the usage information of the test functions by the five second center nodes in the second group 304 is statistically calculated, so as to obtain second usage characteristics 334.

Illustratively, determining the impact of the parameter setting on the use of the test function based on the first use feature 332 and the second use feature 334, such as by comparing the first use feature 332 and the second use feature 334, results in a gain effect for setting the first parameter for the test function as compared to setting the second parameter for the test function.

Fig. 3 is a flowchart illustrating a method for determining a test function usage characteristic according to an exemplary embodiment of the present application. The method may be performed by a computer device. The method comprises the following steps:

step 510: acquiring a first association weight and a second association weight of a node to be allocated;

for example, the first association weight is used for indicating the social association degree of the node to be allocated and the first number of first central nodes belonging to the first group, and the second association weight is used for indicating the social association degree of the node to be allocated and the first number of second central nodes belonging to the second group; the first number and the second number are each a positive integer, and the first number and the second number may be the same or different, and the present application is not limited.

Illustratively, the social association degree is used for indicating the frequency of social interaction between two nodes, and illustratively, the social interaction comprises at least one of chat session, giving virtual gift and transferring virtual resource, and the application does not limit the social interaction mode.

The node to be distributed is an account number of an unassigned group, and the first central node and the second central node are account numbers of use information of a function to be tested in a statistics mode; the method comprises the steps that the use information of a first central node in a first group on a test function is counted, and the use information of each node in the first group on the test function is represented; and representing the usage information of each node in the second group on the test function by counting the usage information of the second central node in the second group on the test function.

It should be noted that the first group may further include a non-central node, where the first central node is an account number for counting usage information of the test function, and the usage information of the test function based on the usage information of the first central node represents usage information of each node in the first group for the test function; illustratively, there is no need to count usage information of test functions by non-central nodes in the first group. Similarly, non-central nodes may also be included in the second group.

Step 520: adding the node to be allocated to a first group to which a first number of first central nodes belong, in case the first association weight exceeds the second association weight, and setting a test function based on a first parameter in the first group;

for example, in the case that the first association weight exceeds the second association weight, the social association degree of the node to be allocated and the first center node is higher than that of the second center node. The node to be allocated is added to the first group. The node to be allocated added to the first group is a non-central node, so that the association degree of any non-central node in the first group and the first central node is higher than the association degree of the non-central node and the second central node.

Illustratively, the first parameter and the second set of second parameters set for the test function are different; by setting the test function for each node in the first group based on the first parameter, it is achieved that at least two nodes with a high degree of social relevance are set with the same parameter. The problem that two nodes with social association degree interfere with the use information of the test function due to the fact that parameters of different test functions are set is avoided, and the influence of network effect on the use condition of the test function is reduced.

Step 530: carrying out statistical calculation on the use information of the test function by the first number of first central nodes to obtain first use characteristics of the test function under the first parameters;

illustratively, the usage information is a usage of the test function by the first central node. And carrying out statistical calculation on the use information through at least one of summing, variance calculation, averaging and the like to obtain first use characteristics of the test function for a first number of first central nodes under a first parameter. Illustratively, the usage information of the test function by the first central node represents the usage information of the test function by each node in the first group.

In summary, according to the method provided by the embodiment, the social association degree between the nodes is quantitatively described by the first association weight and the second association weight of the nodes to be allocated, so that the test function is interfered by the network effect described by the social association degree; under the condition that the node to be distributed and the first central node with high social association degree are in different groups, the use of the first central node for the test function is affected by social communication and parameters of the test function; because the parameters set by the nodes to be distributed in different groups and the first central node in the test function are different, the first central node performs social communication to influence the parameters of the test function so as to generate interference; by adding the node to be distributed to the first group with high social connection degree, the same test function parameters can be set for the node to be distributed and the first center node with high social connection degree, so that the problem that the node to be distributed interferes with the service condition of the test function through interaction modes such as social communication and the like is avoided, and the influence of network effect on the service condition of the test function is reduced.

Fig. 4 is another flow chart of a method for determining a test function usage characteristic according to an exemplary embodiment of the present application. The method may be performed by a computer device. That is, in the embodiment shown in fig. 3, step 510 may be implemented as steps 512, 514:

step 512: acquiring a first number of first sub-weights and a second number of second sub-weights of nodes to be distributed;

the first number of first center nodes and the first number of first sub-weights are in one-to-one correspondence, and the second number of second center nodes and the second number of second sub-weights are in one-to-one correspondence; it can be appreciated that the first sub-weight is used to indicate a degree of social association between the node to be allocated and a first central node; the second sub-weight is used for indicating the social association degree between the node to be allocated and a second center node.

In an optional implementation manner of the present application, the first sub-weight is used to indicate a relative affinity of each associated node of the node to be allocated having a social relationship with respect to the first central node; the second sub-weight and the first sub-weight are similar. By way of example, step 512 may be implemented as two sub-steps:

Substep 12a: and determining the ratio of the first affinity to the first set as a first sub-weight, and obtaining a first number of first sub-weights.

Substep 12b: and determining the ratio of the second affinity to the second set as a second sub-weight, and obtaining a second number of second sub-weights.

Illustratively, the first affinity is a social affinity between the node to be assigned and the first central node, and the first set is a sum of social affinities between the first central node and respective associated nodes where social relationships exist. Similarly, the second affinity is the social affinity between the node to be assigned and the second central node, and the second set is the sum of the social affinities between the second central node and the respective associated nodes where the social relationship exists.

In an alternative implementation of the present application, the social association degree between the nodes is determined according to at least one of historical session information, historical interaction information, common group information and friend labels between the two nodes. Further, the social affinity includes at least one of historical session information, historical interaction information, common group information, and friend tags. Further description will follow.

Historical session information: the method is used for indicating at least one of the times, the frequency and the duration of chat sessions between account numbers corresponding to two nodes in a text, voice, video and other multimedia mode, and indicating the social association degree between the nodes on a social platform based on the initiation session through the information of the social session between the account numbers corresponding to the two nodes.

Historical interaction information: the method is used for indicating at least one of praying, commenting, forwarding, participating in voting, giving a virtual gift and transferring virtual resources on the social streaming media between the accounts corresponding to the two nodes, and the social association degree based on interaction on the social platform between the nodes is indicated through the history information of interaction between the accounts corresponding to the two nodes.

Common group information: the method is used for indicating groups which are commonly added or commonly managed on a social platform between account numbers corresponding to two nodes, and the social association degree existing in the virtual groups between the nodes is indicated through common group information between the account numbers corresponding to the two nodes.

Friend tag: the method is used for indicating the friend labels customized on the social platform between the account numbers corresponding to the two nodes, and the friend labels can be customized and set or can be virtual medal labels acquired according to chat sessions or interaction. And indicating the social association degree between the nodes by a user-defined mode through friend labels between account numbers corresponding to the two nodes.

Step 514: determining a first association weight according to the first number of first sub-weights, and determining a second association weight according to the second number of second sub-weights;

illustratively, the first associated weight is determined by summing or averaging the first number of first sub-weights; similarly, a second number of second sub-weights is summed or averaged to determine a second associated weight. In an alternative implementation, the first associated weight is determined from an average of the first number of first sub-weights and the second associated weight is determined from an average of the second number of second sub-weights, in case the first number and the second number are different. In case the first number and the second number are the same, a first associated weight is determined from the sum of the first number of first sub-weights and a second associated weight is determined from the sum of the second number of second sub-weights.

In summary, according to the method provided by the embodiment, the social association degree between the nodes is quantitatively described by the first association weight and the second association weight of the node to be allocated, and the social association degree between the first center node and the node to be allocated is determined one by one through the first sub-weight; the relative intimacy degree of the intimacy degree between the account corresponding to the first center node and the account corresponding to the node to be distributed relative to each friend is fully considered through the ratio of the first intimacy degree to the first set; the network effects are described to cause interference to the test function through social association degrees, the network effects corresponding to friends are quantitatively described according to the intimacy degree between account numbers, different social association degrees are distinguished, nodes to be allocated are added to a first group with high social association degrees, the same test function parameters are set for the nodes to be allocated and the first center nodes with high social association degrees, the problem that the nodes to be allocated influence the use condition of the test function through social communication and other interaction modes, and the influence of the network effects on the use condition of the test function is reduced.

In one example of the present application, the node to be allocated is denoted as the kth node, and the center node is denoted as the ith node.

Illustratively, the affinity between the kth node and the ith node is noted asThe method comprises the steps of carrying out a first treatment on the surface of the The sum of social affinities between the ith node and each associated node in which a social relationship exists is noted asThe method comprises the steps of carrying out a first treatment on the surface of the Exemplary, since there is a social relationship between the ith node and the associated node, if there is a social relationship between the jth node and the ith node。

Illustratively, the sub-weights between the kth node and the ith node are:

；

it will be appreciated that the central node is denoted as section iThe center node may be a first center node or a second center node. The node to be distributed and the central node are provided with node attribute identifiers, and the central node is taken as an ith node as an example, and the node attribute identifier of the ith nodeThe method comprises the steps of carrying out a first treatment on the surface of the The node to be distributed is a non-central node, and the node attribute identifier of the kth node。

Illustratively, the central node also has a group identification; for the first central node, the first central node is assigned to a first group, and the group identification of the first central nodeThe method comprises the steps of carrying out a first treatment on the surface of the For the second central node, the second central node is assigned to a second group, and the group identification of the second central node 。

The first associated weight is determined by summing a first number of first sub-weights, and the second associated weight is determined from a second number of second sub-weights.

Further, the first association weight is:

；

illustratively, by calculating the sub-weights between the kth node and the ith nodeNode attribute identification with the i-th nodeThe product of the sub-weights between the node to be allocated and the central node is guaranteed to be calculated; by passing throughThe central nodes are screened and only the sum of the sub-weights between the first central node belonging to the first group and the node to be assigned is calculated.

The second association weight is:

；

illustratively, by calculating the sub-weights between the kth node and the ith nodeNode attribute identification with the i-th nodeThe product of the sub-weights between the node to be allocated and the central node is guaranteed to be calculated; by passing throughThe central nodes are screened and only the sum of the sub-weights between the second central node belonging to the second group and the node to be assigned is calculated.

Fig. 5 is a flowchart illustrating a method for determining a test function usage characteristic according to an exemplary embodiment of the present application. The method may be performed by a computer device. That is, in the embodiment shown in fig. 3, step 520 may be implemented as step 522:

Step 522: adding the node to be allocated to a first group to which a first number of first central nodes belong, in case the relative error between the first associated weight and the second associated weight exceeds a first threshold, and setting a test function based on a first parameter in the first group;

illustratively, the relative error is used to indicate a ratio of a weight difference value, which is the difference of the first associated weight minus the second associated weight, to the second associated weight. In one example, the first associated weight is noted asThe second associated weight is noted asThe relative error between the first associated weight and the second associated weight is:the method comprises the steps of carrying out a first treatment on the surface of the The first threshold is marked asThe method comprises the steps of carrying out a first treatment on the surface of the In case the relative error exceeds a first threshold, adding the node to be allocated to a first group to which a first number of first central nodes belong, i.e. whenAdding the node to be allocated to a first group to which a first number of first central nodes belong; group identification of nodes to be allocatedThe method comprises the steps of carrying out a first treatment on the surface of the Optionally, a first thresholdThe value of (2) is greater than or equal to 0. Alternatively, in satisfyingAdding the node to be allocated to a second group to which a second number of second central nodes belong; group identification of nodes to be allocated 。

Illustratively, the relative error in the present embodiment is a ratio of the weight difference value and the second associated weight, but a case where the relative error is determined as a ratio of the weight difference value and the first associated weight is not excluded.

The first threshold is used for indicating that the social association degree of the node to be allocated and the first central node is significantly higher than the threshold of the second central node. Illustratively, the first threshold is greater than 0, and the first threshold may be a preset empirical value, such as a value of 0.5.

The social association degree of the node to be allocated and the first center node is further limited by a first threshold; and adding the node to be allocated to the first group under the condition that the social association degree of the node to be allocated and the first central node is obviously higher than the threshold of the second central node. In an alternative implementation, the first group is an experimental group and the second group is a control group; the first parameter is a test parameter of the test function, and the second parameter is an initial parameter of the test function. The number of non-central nodes added to the first group is constrained by the first threshold, and the range of nodes for setting the test parameters is constrained on the basis of reducing the influence of network effects on the use condition of the test function. In the case of setting test parameters for test functions in an experimental group, for example, the test parameters and initial parameters need to be supported simultaneously for the test functions, the node range of the test parameters is restricted, and the consumption of computing resources caused by the first parameters of the test functions is reduced.

In summary, according to the method provided by the embodiment, the social association degree between the nodes is quantitatively described by the first association weight and the second association weight of the nodes to be allocated, the network effect is described to cause interference to the test function by the social association degree, the nodes to be allocated are added to the first group obviously with high social association degree based on the relative error between the first association weight and the second association weight, the number of non-central nodes added to the first group is restricted, the test function of the nodes to be allocated and the first central node is set based on the first parameter, and the influence of the network effect on the use condition of the test function is reduced.

Fig. 6 is a flowchart illustrating a method for determining a test function usage characteristic according to an exemplary embodiment of the present application. The method may be performed by a computer device. I.e. on the basis of the embodiment shown in fig. 3, further comprising a step 505, a step 506, a step 507:

the present embodiment describes a manner of determining the group of the center nodes; the manner in which the group of central nodes is determined includes at least two implementations. Wherein, implementation one corresponds to step 505, implementation two corresponds to step 506, step 507.

Step 505: determining a first number of first central nodes belonging to a first group and a second number of second central nodes belonging to a second group in a random manner at n central nodes;

by means of randomness of the first center node and the second center node in the center nodes, interference to use information due to differences in the characteristics of the number of friends, the account registration time, the social information release amount of the account and the like of the account corresponding to the center nodes when different parameter setting test functions are adopted is avoided.

For example, nodes with different account characteristics have different usage tendencies for the test function, taking the function of sharing social posts with friends as an example, a first parameter indicates that a sharing portal is displayed in the upper left corner of the social post, and a second parameter indicates that sharing portals are displayed in parallel beside forwarding portals of the social post. For the function of sharing social posts to friends, the coverage degree of the number of friends on friends in the real world is limited, and the use tendency of the account with large number of friends on the function is stronger than that of the account with small number of friends; if the account numbers with large numbers of friends are grouped into the first group in a centralized manner, the difference of the usage information between the first group and the second group is affected by the account number characteristics, and whether the difference of the usage information caused by the forwarding entry position or the difference of the usage information caused by the different numbers of friends cannot be judged. The group of n central nodes is determined in a random mode, and the interference of differences in account characteristics corresponding to the central nodes on the use information is avoided.

It will be appreciated that step 505 may be implemented separately from steps 510 through 530 of the figures, combined into a new embodiment. Similarly, step 506 and step 507 may be combined with steps 510 to 530 in the figures to form a new embodiment, which is not limited in this embodiment.

Step 506: dividing the n central nodes into at least two node types according to the number of associated nodes with social relations in each of the n central nodes;

the number of the association nodes with social relations is used for indicating the number of friends added by the account corresponding to the central node and capable of sending session messages in a bidirectional mode, the number of interest accounts with single attention, and the number of fans (or attention persons) of the account corresponding to the single attention central node.

Illustratively, the n central nodes are divided into at least two node types, with the n central nodes being divided into at least two node types by the dimension of the number of associated nodes.

In an alternative implementation, the present ratio can only be implemented as:

and determining the ith center node as the first node type under the condition that the number of the social friends of the ith center node in the n center nodes is larger than a number threshold.

The number threshold is illustratively a demarcation threshold that determines a node type for the center node, determines a node type based on the number of social friends, and classifies nodes having a number similarity of social friends to the same node type.

Step 507: adding a center node to the first group or the second group in a random manner in each of the at least two node types;

illustratively, adding the center node to the first group or the second group in a random manner in each node type achieves a uniform distribution of the center node to the first group or the second group in a random manner across the number of social friends' dimensions. The problem that the group effect is poor when the number of the central nodes is small is solved by directly distributing the groups in a random mode.

In an alternative implementation manner of this embodiment, the n central nodes are randomly determined according to a preset proportion in an undirected graph constructed by at least n+1 nodes. I.e. further comprising, prior to step 505 or step 506:

Randomly determining n center nodes according to a preset proportion in an undirected graph constructed by at least n+1 nodes;

by selecting the central node in a random manner, the central node can represent each node in the undirected graph, and the problem that the account corresponding to the central node has similar account characteristics and cannot represent each node in the undirected graph is avoided.

In summary, the method provided in this embodiment determines the group of the central nodes in a random manner, so as to avoid the problem that the account corresponding to the central node in the same group is concentrated on the similar account feature and cannot represent each node in the undirected graph; the network effect is described to cause interference to the test function through the social association degree, and the node to be allocated is added to the first group with the high social association degree, so that the test functions of the node to be allocated and the first center node are set based on the first parameter, and the influence of the network effect on the use condition of the test function is reduced.

In an alternative implementation, the undirected graph above is further described.

Illustratively, an undirected graph constructed of at least n+1 nodes may be represented asTaking the undirected graph as an example, M nodes are included, M is greater than or equal to n+1.Is a set of M nodes that are configured to communicate,is a collection of edges between two nodes, inUnder the condition of (1), social relation exists between the ith node and the jth node; that is, there is an edge between the i-th node and the j-th node. Exemplary collection of edges in undirected graphAnd is further configured to indicate that a network effect propagation relationship exists between any two of the M nodes.

In particular, toDimensional symmetry matrixRepresenting network effect propagation relationships.

Exemplary, if there is no edge between the ith node and the jth node, then. If there is an edge between the ith node and the jth node, thenAnd (2) andthe value of (a) is the social affinity between the i-th node and the j-th node, and the description of the social affinity is referred to above in fig. 4 and is not repeated here.

Fig. 7 is a flowchart illustrating a method for determining a test function usage characteristic according to an exemplary embodiment of the present application. The method may be performed by a computer device. I.e. on the basis of the embodiment shown in fig. 3, further comprising step 540, step 550:

step 540: setting a test function based on a second parameter in a second group, and carrying out statistical calculation on the use information of the test function by a second number of second center nodes to obtain a second use characteristic of the test function under the second parameter;

Illustratively, the first parameter and the second set of second parameters set for the test function are different; the second central node usage information of the test function is a second central node usage of the test function. And carrying out statistical calculation on the use information through at least one of summing, variance calculation, averaging and the like to obtain second use characteristics of the test function for a second number of second central nodes under a second parameter.

Step 550: obtaining a total usage feature of pushing the first parameter to the first number of first center nodes and the second number of second center nodes by comparing the first usage feature with the second usage feature;

the method includes the steps of determining a first parameter, determining a first central node of the first plurality of central nodes, determining a second central node of the second plurality of central nodes, determining a third central node of the second plurality of central nodes, determining a fourth central node of the second plurality of central nodes, and determining a fourth central node of the second plurality of central nodes.

In an alternative implementation of the application, step 550 can be implemented as the following sub-steps:

substep 50a: correcting the first use feature based on a first ratio between the first quantity and the first value to obtain a first full-quantity feature;

Substep 50b: correcting the second use feature based on a second ratio between the second number and the first value to obtain a second full-scale feature;

the first full feature is used to indicate that the first central node and the second central node each set a usage feature of the test function based on the first parameter; the second full-scale feature is used to indicate that the first central node and the second central node both set usage features of the test function based on the second parameter. Wherein the first value is the sum of the first number and the second number.

In one example, the first usage feature is modified by calculating a quotient of the first usage feature divided by the first ratio to obtain a first full feature. Similarly, the second usage feature is modified by calculating a quotient of the second usage feature divided by the second ratio to obtain a second full-scale feature. In one example, the usage characteristic is at least one of a total length of usage of the test function, a total amount of computing resource usage of the test function, and a total amount of data transmission using the test function.

Substep 50c: determining a full gain feature according to the difference between the first full feature and the second full feature;

illustratively, the difference between the first full feature and the second full feature is used to indicate a difference in usage of the test function between the first parameter and the second parameter. The full gain feature is used to indicate the usage feature gain produced by the test function relative to the second parameter. Optionally, the full gain feature is a ratio of a difference between the first full feature and the second full feature to the second full feature.

In summary, according to the method provided by the embodiment, the social association degree between the nodes is quantitatively described by the first association weight and the second association weight of the nodes to be allocated, the network effect is described to cause interference to the test function by the social association degree, and the nodes to be allocated are added to the first group with high social association degree, so that the test functions of the nodes to be allocated and the first center node are set based on the first parameter, and the influence of the network effect on the use condition of the test function is reduced.

In one example, the test function is a function entry of the application, the first parameter is used to indicate that the function entry of the application is set to green, and the second parameter is used to indicate that the function entry of the application is set to red. It will be appreciated that in different examples, the first parameter/second parameter may also be used to set other options of the test function, such as at least one of a position of the function entry, a display size of the function entry, a cutscene of the function entry, a pop-up mode of triggering the function entry, a push-information mode, an initial display scale of the interface, and the like.

In one implementation, the method for determining the test function usage characteristics in the application is adopted to determine the test function usage characteristics on an undirected graph consisting of tens of millions of nodes.

In another implementation, the network random experiment mode is adopted to count the use information; according to the network random experiment mode, the ten-million nodes are divided into a plurality of sub-networks according to the friend relation between the accounts according to the indication, the social relation of the accounts between different sub-networks is distant, and the social relation between the accounts in the sub-networks is close. Grouping half of sub-networks into experimental groups, wherein the use information index of all accounts in the sub-networks is considered as an index value of the parameters set by the experimental groups to all nodes; and grouping half of the sub-networks into a control group, wherein the usage information index of all the accounts in the sub-networks is considered as an index value of the parameters set by the control group pushed to all the nodes. And comparing the use information difference of the experimental group with the use information difference of the control group to obtain the use information difference caused by the parameters set by the experimental group and the parameters set by the control group.

In another implementation mode, the central node network experimental mode of the loss rate upper limit constraint is adopted to count the use information; the upper limit of the loss rate is a selection condition for restraining the central node, the central node and the non-central node with the friend relation of the central node are divided into the same group, when conflict occurs in the divided groups, the upper limit of the loss rate is used for restraining, the central node which cannot meet the upper limit of the loss rate is deleted, and the central node is reselected; the loss rate is the proportion of associated nodes that have social relationships that are not grouped into the same group.

Illustratively, the results of grouping in the three implementations are shown in Table one.

List one

Index (I)	Network random experiment mode	Central node network experimental mode of loss rate upper limit constraint	Method for determining use characteristics of test function
				Loss rate of control and experimental group	0.32，0.32	0.38，0.38	0.34，0.17
Sample size	1155 ten thousand account numbers, 25 ten thousand sub-networks	113 ten thousand central nodes	114 ten thousand central nodes
				Standard deviation of statistics	0.126593	0.000830	0.000829
Whether or not to represent a large disc	Can be used for	Cannot be used	Can be used for

In the network random experiment mode, the use condition of the test function of all nodes needs to be counted, and the measurement of the fluctuation degree of the statistic standard deviation indication under the condition of different sample extraction is larger than that of other two realization modes, so that the stability of the test function is lower when different parameter comparison tests are carried out.

In the central node network experimental mode of the loss rate upper limit constraint, because the loss rate upper limit is a selection condition for constraining central nodes, the central nodes are subjected to conditional screening, and cannot represent a large disk, namely cannot represent all the nodes in tens of millions.

In the method for determining the test function using characteristics, the loss rate of the experimental group is obviously reduced, so that the center node and the non-center nodes with friend relations of the center node can be divided into the same group more, and the influence caused by network effect in the experimental group is greatly reduced; since in the example of the present application the central node is determined in a random manner, the central node can represent a large disk, i.e. can represent all nodes on the order of tens of millions.

It will be appreciated by those skilled in the art that the above embodiments may be implemented independently, or the above embodiments may be combined freely to form a new embodiment to implement the method for determining the test function usage characteristics of the present application.

Fig. 8 is a block diagram showing a configuration of a test function usage characteristic determining apparatus according to an exemplary embodiment of the present application. The device comprises:

the obtaining module 810 is configured to obtain a first association weight and a second association weight of a node to be allocated, where the first association weight is used to indicate a social association degree of the node to be allocated and a first number of first central nodes belonging to a first group, and the second association weight is used to indicate a social association degree of the node to be allocated and a first number of second central nodes belonging to a second group, where the node to be allocated is an account number of unassigned groups, and the first central node and the second central node are account numbers for counting usage information of the test function;

a processing module 820, configured to add the node to be allocated to the first group to which the first number of first central nodes belong, if the first association weight exceeds the second association weight, and set the test function in the first group based on a first parameter, where the first parameter and the second parameter set by the second group for the test function are different;

The processing module 820 is further configured to perform statistical calculation on usage information of the test function by the first number of first central nodes, so as to obtain a first usage characteristic of the test function under the first parameter.

In an optional implementation manner of this embodiment, the obtaining module 810 is further configured to:

In an alternative implementation of this embodiment, the processing module 820 is further configured to:

In an alternative implementation manner of this embodiment, the first group is an experimental group, and the second group is a control group; the first parameter is a test parameter of the test function, and the second parameter is an initial parameter of the test function.

In an optional implementation manner of this embodiment, the social association degree is determined according to at least one of historical session information, historical interaction information, common group information and friend labels between two nodes.

It should be noted that, when the apparatus provided in the foregoing embodiment performs the functions thereof, only the division of the respective functional modules is used as an example, in practical application, the foregoing functional allocation may be performed by different functional modules according to actual needs, that is, the content structure of the device is divided into different functional modules, so as to perform all or part of the functions described above.

With respect to the apparatus in the above embodiments, the specific manner in which the respective modules perform the operations has been described in detail in the embodiments regarding the method; the technical effects achieved by the execution of the operations by the respective modules are the same as those in the embodiments related to the method, and will not be described in detail herein.

The embodiment of the application also provides a computer device, which comprises: a processor and a memory, the memory storing a computer program; the processor is configured to execute the computer program in the memory to implement the method for determining the test function usage characteristics provided by the method embodiments.

Optionally, the computer device is a server. Fig. 9 is a block diagram illustrating a structure of a server according to an exemplary embodiment of the present application.

In general, the server 2300 includes: a processor 2301 and a memory 2302.

The processor 2301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, or the like. The processor 2301 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 2301 may also include a main processor, which is a processor for processing data in an awake state, also referred to as a central processor (Central Processing Unit, CPU), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 2301 may be integrated with an image processor (Graphics Processing Unit, GPU) for use in connection with rendering and rendering of content to be displayed by the display screen. In some embodiments, the processor 2301 may also include an artificial intelligence (Artificial Intelligence, AI) processor for processing computing operations related to machine learning.

Memory 2302 may include one or more computer-readable storage media, which may be non-transitory. Memory 2302 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 2302 is used to store at least one instruction for execution by processor 2301 to implement the method of determining test function usage characteristics provided by a method embodiment of the present application.

In some embodiments, server 2300 may further optionally include: an input interface 2303 and an output interface 2304. The processor 2301 and the memory 2302 may be connected to the input interface 2303 and the output interface 2304 through buses or signal lines. The respective peripheral devices may be connected to the input interface 2303 and the output interface 2304 through buses, signal lines, or a circuit board. Input interface 2303, output interface 2304 may be used to connect at least one Input/Output (I/O) related peripheral device to processor 2301 and memory 2302. In some embodiments, the processor 2301, memory 2302, and input interface 2303, output interface 2304 are integrated on the same chip or circuit board; in some other embodiments, the processor 2301, the memory 2302, and either or both of the input interface 2303 and the output interface 2304 may be implemented on separate chips or circuit boards, as embodiments of the application are not limited in this respect.

Those skilled in the art will appreciate that the structures shown above are not limiting of server 2300 and may include more or fewer components than shown, or may combine certain components, or may employ a different arrangement of components.

In an exemplary embodiment, a chip is also provided, the chip comprising programmable logic circuits and/or program instructions for implementing the method of determining the test function usage characteristics of the above aspects when the chip is run on a computer device.

In an exemplary embodiment, a computer program product is also provided, the computer program product comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor reads and executes the computer instructions from the computer readable storage medium to implement the method for determining the test function usage characteristics provided by the above-mentioned method embodiments.

In an exemplary embodiment, there is also provided a computer-readable storage medium having stored therein a computer program loaded and executed by a processor to implement the method of determining a test function usage characteristic provided by the above-described method embodiments.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the embodiments of the present application may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, these functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

The foregoing description of the preferred embodiments of the present application is not intended to limit the application, but rather, the application is to be construed as limited to the appended claims.

Claims

1. A method of determining a test function usage characteristic, the method comprising:

2. The method of claim 1, wherein the obtaining the first association weight and the second association weight of the node to be assigned comprises:

3. The method of claim 2, wherein the obtaining the first number of first sub-weights and the second number of second sub-weights for the node to be allocated comprises:

4. A method according to any one of claims 1 to 3, characterized in that said adding the node to be allocated to the first group to which the first number of first central nodes belongs, in case the first association weight exceeds the second association weight, and setting the test function based on a first parameter in the first group, comprises:

5. The method of claim 4, wherein the first group is an experimental group and the second group is a control group; the first parameter is a test parameter of the test function, and the second parameter is an initial parameter of the test function.

6. A method according to any one of claims 1 to 3, wherein the method further comprises:

the first number of first central nodes belonging to the first group and the second number of second central nodes belonging to the second group are determined at n central nodes in a random manner.

7. A method according to any one of claims 1 to 3, wherein the method further comprises:

8. The method of claim 7, wherein the dividing the n center nodes into at least two node types according to the number of associated nodes in which each of the n center nodes has a social relationship comprises:

9. The method of claim 7, wherein the method further comprises:

10. A method according to any one of claims 1 to 3, wherein the degree of social association is determined from at least one of historical session information, historical interaction information, common group information, friend tags between two nodes.

11. A method according to any one of claims 1 to 3, wherein the method further comprises:

12. A device for determining a characteristic of use of a test function, the device comprising:

13. A computer device, the computer device comprising: a processor and a memory, wherein at least one section of program is stored in the memory; the processor is configured to execute the at least one program in the memory to implement the method for determining the test function usage characteristics according to any one of claims 1 to 11.

14. A computer readable storage medium having stored therein executable instructions that are loaded and executed by a processor to implement a method of determining a test function usage characteristic according to any of the preceding claims 1 to 11.