CN113392018A - Traffic distribution method, traffic distribution device, storage medium, and electronic device - Google Patents
Traffic distribution method, traffic distribution device, storage medium, and electronic device Download PDFInfo
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Abstract
The present disclosure relates to a traffic distribution method, apparatus, storage medium, and electronic device, the method comprising: determining a test version corresponding to the application program; for each test version, according to the initial test flow corresponding to the test version, determining index data of the application program under the test version within a preset time length, and determining historical index data of the application program under the current release version; determining a target flow distribution model according to the type of the index data; and determining the test flow distributed to each test version and each release version in the next test process according to the index data, the historical index data and the target flow distribution model. By the method, intelligent flow distribution in the testing process can be realized, the testing efficiency of the application program is improved, the testing time of the application program can be reduced, and the invariance of environmental conditions in the continuous testing process is ensured.
Description
Technical Field
The present disclosure relates to the field of computing technologies, and in particular, to a traffic distribution method and apparatus, a storage medium, and an electronic device.
Background
With the continuous development of computer technology, various applications are layered endlessly, and the update iteration speed of the applications is very fast. In the update iteration process of the application program, the related art generally modifies the released application program according to the assumed situation to obtain a new version of the application program to be tested, and then tests the new version of the application program to be tested to determine a better version of the application program as the final online update version. In the process of testing a new version of application program each time, the number of testing users is determined manually, so that more manual participation is needed, and the testing efficiency is influenced.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present disclosure provides a traffic distribution method, including:
determining a test version corresponding to the application program;
for each test version, according to the initial test flow corresponding to the test version, determining index data of the application program in a preset time length under the test version, and determining historical index data of the application program under the current release version;
determining a target flow distribution model according to the type of the index data, wherein the target flow distribution model is used for carrying out flow distribution according to input index data;
and determining the test flow distributed to each test version and the release version in the next test process according to the index data, the historical index data and the target flow distribution model.
In a second aspect, the present disclosure provides a traffic distribution apparatus, the apparatus comprising:
the first determining module is used for determining a test version corresponding to the application program;
the second determining module is used for determining the index data of the application program in the test version within a preset time length according to the initial test flow corresponding to the test version and determining the historical index data of the application program in the current release version aiming at each test version;
a third determining module, configured to determine a target traffic distribution model according to the type of the index data, where the target traffic distribution model is configured to perform traffic distribution according to input index data;
and the fourth determining module is used for determining the test flow distributed to each test version and the release version in the next test process according to the index data, the historical index data and the target flow distribution model.
In a third aspect, the present disclosure provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processing apparatus, implements the steps of the method described in the first aspect.
In a fourth aspect, the present disclosure provides an electronic device comprising:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to carry out the steps of the method of the first aspect.
Through the technical scheme, the target flow distribution model can be determined according to the type of the index data. Then, the test flow distributed to each test version and each release version in the next test process can be determined according to the index data, the historical index data and the target flow distribution model, namely, the number of test users corresponding to the application programs of each version can be automatically determined through the model, the test flow does not need to be manually distributed, and therefore the test efficiency can be improved. In addition, through automatically distributing the test flow, a plurality of test versions can be tested simultaneously, and compared with a serial test mode, the test time is reduced, so that the test efficiency can be further improved. And because a plurality of test versions can be tested simultaneously, the invariance of environmental conditions in the test process can be ensured, thereby ensuring the test effect.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale. In the drawings:
fig. 1 is a flow chart illustrating a traffic distribution method according to an exemplary embodiment of the present disclosure;
FIG. 2 is a block diagram illustrating a traffic distribution apparatus according to an exemplary embodiment of the present disclosure;
fig. 3 is a block diagram illustrating an electronic device according to an exemplary embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units. It is further noted that references to "a", "an", and "the" modifications in the present disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
As mentioned in the background art, in the update iteration process of an application program, the related art usually modifies a released application program according to an assumed situation to obtain a new version of the application program to be tested, and then tests the new version of the application program to be tested to determine a better version of the application program as a final online update version. In the process of testing a new version of application program each time, the number of testing users is determined manually, so that more manual participation is needed, and the testing efficiency is influenced.
In addition, the related art generally performs serial testing, specifically, an application program is modified according to an assumed condition to obtain an application program of a test version, then a new assumption is performed again and the application program is modified according to a feedback of a manually-specified test user on the use of the application program of the test version to obtain an application program of another test version for testing, and thus the above-mentioned testing process is repeated until a better test version is determined, and update iteration of the application program is completed. According to the mode, the whole testing time is long, and the invariance of the environmental conditions in the continuous testing process cannot be ensured, so that the testing effect is influenced.
In view of this, the present disclosure provides a traffic distribution method to implement intelligent traffic distribution in a testing process, improve testing efficiency of an application program, reduce testing time of the application program, and ensure invariance of environmental conditions in a continuous testing process.
Fig. 1 is a flow chart illustrating a traffic distribution method according to an exemplary embodiment of the present disclosure. Referring to fig. 1, the traffic distribution method includes:
And 102, determining index data of the application program under the test version within a preset time length according to the initial test flow corresponding to the test version and determining historical index data of the application program under the current release version aiming at each test version.
And 103, determining a target flow distribution model according to the type of the index data, wherein the target flow distribution model is used for carrying out flow distribution according to the input index data.
And step 104, determining the test flow distributed to each test version and release version in the next test process according to the index data, the historical index data and the target flow distribution model.
For example, the test version corresponding to the application program may be obtained by modifying the current release version according to a modification operation of a tester, for example, for the application program of the current release version, modifying a display color of an operation control in the application program in response to a control color modification operation, or modifying a jump manner between pages in the application program in response to a page jump modification operation, and so on. It should be understood that different modifications to the application program may result in different test versions, and the different modifications may include multiple modifications to the same content of the application program, for example, three different test versions may result from respectively modifying the display color of the operation control to three different colors. In practical application, the application program of the current release version may be modified according to the update iteration requirement of the application program to obtain a corresponding test version, which is not limited in the embodiment of the present disclosure.
For example, the test flow refers to the number of test users, and the initial test flow may be specified by a tester in advance, for example, preset test users are evenly distributed according to the number of test versions, in which case, the initial test flow corresponding to each test version is equal. Alternatively, a plurality of test versions may be distributed to a corresponding number of test users according to a preset distribution ratio.
For example, the preset duration may be set according to an actual situation, and the index data may include different types of index data such as conversion data, click data, browsing duration, and the like, which is not limited in this disclosure. The conversion data can represent whether the test user corresponding to the test version performs the activation operation or the registration operation, that is, whether the user successfully converts to the activation user or the registration user after using the test version can be determined through the conversion data. The click data may represent whether the test user corresponding to the test version performs a click operation, and the browsing duration may be a browsing duration of the test user to the test version.
After the initial test flow corresponding to each test version is determined, that is, after the number of the test users corresponding to each test version is determined, the test versions can be distributed to the corresponding test users for use, so that index data of the application program in the test version within a preset time length can be determined. In addition, historical index data of the application program under the current release version can be determined, so that the test flow distributed to each version is determined by comparing the historical index data of the test version with the historical index data of the current release version, and a better application program version is determined through multiple tests.
In the embodiment of the present disclosure, first, a target traffic distribution model may be determined according to the type of the index data. Then, the test flow distributed to each test version and each release version in the next test process can be determined according to the index data, the historical index data and the target flow distribution model, namely, the number of test users corresponding to the application programs of each version can be automatically determined through the model, the test flow does not need to be manually distributed, and therefore the test efficiency can be improved. In addition, through automatically distributing the test flow, a plurality of test versions can be tested simultaneously, and compared with a serial test mode, the test time is reduced, so that the test efficiency can be further improved. And because a plurality of test versions can be tested simultaneously, the invariance of environmental conditions in the test process can be ensured, thereby ensuring the test effect.
In order to make the traffic distribution method provided by the present disclosure more understandable to those skilled in the art, the above steps are exemplified in detail below.
In a possible manner, according to the type of the index data, determining the target traffic distribution model may be: and determining a target flow distribution model according to the type of the index data in a preset first flow distribution model and a preset second flow distribution model. The first flow distribution model is used for carrying out probability calculation according to the index data and the historical index data to determine the test flow distributed to each test version and each release version, and the second flow distribution model is used for determining the flow distribution proportion according to the index data, the historical index data and the preset model parameters and determining the test flow distributed to each test version and each release version according to the flow distribution proportion.
For example, two different types of traffic distribution models, such as a first traffic distribution model and a second traffic distribution model, may be preset in the test system. Accordingly, in the subsequent process, the target traffic distribution model may be determined according to the type of the index data in the first traffic distribution model and the second traffic distribution model. Therefore, the model with better performance can be determined for different types of index data to perform flow distribution, and therefore the accuracy of flow distribution is improved.
Of course, in other possible manners, more types of traffic distribution models may be preset in the test system, so as to select a model with better performance for traffic distribution according to a finer index data type, and further improve accuracy of traffic distribution, which is not limited in the embodiment of the present disclosure.
For example, the first traffic distribution model may perform probability calculation according to the index data and the historical index data to determine the test traffic distributed to each test version and the release version, for example, the first traffic distribution model may be a probability estimation model such as a thompson sampling model. The second traffic distribution model can determine a traffic distribution ratio according to the index data, the historical index data and the preset model parameters, and determine test traffic distributed to each test version and each release version according to the traffic distribution ratio. For example, the second traffic distribution model may be an e-greedy algorithm model or the like that does not perform probability estimation. It should be appreciated that the e-greedy algorithm trades off exploration and utilization based on probability, with the e probability being explored at each attempt and the 1-e probability being utilized, and if the second traffic distribution model is the e-greedy algorithm model, the preset model parameters may be preset parameters e.
In a possible manner, in the preset first traffic distribution model and the preset second traffic distribution model, the determining the target traffic distribution model according to the type of the index data may be: in a case where the index data is target type data, the first traffic distribution model is determined as a target traffic distribution model, the target type data is index data that can be divided into a first category and a second category, and a result of characterization of the index data in the first category is opposite to a result of characterization of the index data in the second category. In a case where the index data is not the target type data, the second traffic distribution model is determined as the target traffic distribution model.
It should be appreciated that the first traffic distribution model is a traffic distribution model that is calculated by probability with a higher accuracy than a second traffic distribution model that is not calculated by probability. However, since the first traffic distribution model needs to perform probability calculation, the index data input to the first traffic distribution model should be able to distinguish two different data categories, so that probability estimation can be performed according to the data volumes of the two data categories according to the first traffic distribution model, and then the corresponding test traffic is output. For example, the conversion data can distinguish two different data categories, namely successful conversion and unsuccessful conversion, so that the conversion data can be input into the first traffic distribution model for probability calculation to realize traffic distribution. In addition, the click data can distinguish two different data categories, namely click and non-click, so that the click data can be input into the first flow distribution model for probability calculation, and flow distribution is realized. For the browsing duration, two data types with completely opposite results cannot be distinguished, so that the method is not suitable for inputting a first traffic distribution model for probability calculation to realize traffic distribution.
In the embodiment of the present disclosure, if the index data is target type data, the first traffic distribution model may be determined as a target traffic distribution model. The target type data is index data which can be divided into a first category and a second category, and the result of the index data representation in the first category is opposite to the result of the index data representation in the second category, such as the conversion data and the click data in the above example. On the contrary, if the index data is not the target type data, the second traffic distribution model may be determined as the target traffic distribution model. It should be understood that the second traffic distribution model determines a traffic distribution ratio according to the index data, the historical index data and the preset model parameters, and determines test traffic distributed to each test version and the release version according to the traffic distribution ratio without performing probability calculation, so that traffic distribution can be performed on index data of which the first category and the second category cannot be distinguished.
In a possible manner, if the target traffic distribution model is the first traffic distribution model, determining the test traffic distributed to each test version and each release version in the next test process according to the index data, the historical index data and the target traffic distribution model, which may be: classifying the index data and the historical index data to determine the quantity of first class data and the quantity of second class data in the index data and the historical index data, then determining a first probability of each version corresponding to the first class data or a second probability of each version corresponding to the second class data according to a first traffic distribution model, the quantity of the first class data and the quantity of the second class data, and finally determining test traffic distributed to each test version and a release version in the next test process according to the first probability or the second probability corresponding to each version. Wherein each version comprises each test version and a current release version.
For example, conversion data of each test version in a preset time length and historical conversion data of a currently released version before the preset time length are acquired, the conversion data and the historical conversion data may be firstly classified, and the number of users who successfully convert (i.e., the number of the first category data) and the number of users who unsuccessfully convert (i.e., the number of the second category data) in the conversion data and the historical conversion data are determined. The categorized conversion data and historical conversion data may then be input into a first traffic distribution model. In the calculation process of the first traffic distribution model, the beta distribution can be called, and the number of successfully converted users and the number of unsuccessfully converted users are respectively used as parameters alpha and beta of the beta distribution to generate a random number. The random number can represent the probability of successfully converted users in each version, that is, the value of the random number is the first probability. Or, if a random number is generated by using the number of unsuccessfully transformed users and the distribution of the number of successfully transformed users as the parameters α and β of the beta distribution, the random number may represent the probability of unsuccessfully transformed users in each version, that is, the value of the random number is the second probability.
And then determining the test traffic distributed to each test version and each release version in the next test process according to the first probability or the second probability corresponding to each version. For example, the first traffic distribution model may sort the first probabilities and output the versions of the application with the highest first probabilities, and then may distribute more test traffic for the versions of the application and less test traffic for other versions of the application. Alternatively, the first traffic distribution model may rank the second probabilities and output the application versions with the highest second probabilities, and then may distribute less test traffic for the application versions and distribute more test traffic for other application versions. Therefore, the version with higher successful conversion probability or lower failed conversion probability has more test flow in the next test process, namely more test users use the version, so that the test effect of the version can be better verified, the version of the application program which is more in line with the use requirements of the users is determined, and the optimization of the application program is realized.
By the mode, probability calculation can be carried out through the first flow distribution model, so that flow distribution is carried out on index data which can obviously distinguish data types, accuracy of flow distribution can be improved, efficiency of flow distribution can be improved, and testing efficiency is improved.
In a possible manner, if the target traffic distribution model is the second traffic distribution model, determining, according to the index data, the historical index data, and the target traffic distribution model, that the test traffic distributed to each test version and each release version in the next test process may be: determining a first flow distribution proportion of each test version and each release version according to preset model parameters of a second flow distribution model, then determining a target version with better index performance in each test version and each release version according to index performance represented by index data and historical index data, determining a second distribution proportion of the target version according to the preset model parameters, and finally determining test flow distributed to each test version and each release version in the next test process according to the first distribution proportion, the second distribution proportion and preset total flow.
Taking the second traffic distribution model as an example belonging to a greedy algorithm model, the preset total traffic is set to be 100, that is, the total number of the test users is 100, the preset model parameter belonging to 0.1, and the number of the versions (including the test version and the release version) of the application program is 2. Firstly, the preset model parameters can be averaged according to the number of versions, and the first traffic distribution ratio of each test version and each release version is determined, that is, the first traffic distribution ratio is 0.05. Then, a target version with better performance can be determined according to the index data and the historical index data. For example, if the conversion data and the click data of the test version are both higher than the release version, the test version can be determined to be the target version with better performance. A second distribution proportion of the target version may then be determined based on the first traffic distribution proportion. For example, on the basis of the first traffic distribution ratio, the remaining traffic distribution ratio of 0.9 is determined as the second distribution ratio of the target version. Thus, the traffic distribution ratio of the test version is 0.95, and the traffic distribution ratio of the release version is 0.05. And then, by combining the preset flow, it can be determined that the test flow of the test version in the next test process is 95, and the test flow of the release version is 5, that is, the number of test users of the test version in the next test process is 95, and the number of test users of the release version is 5.
Therefore, the flow distribution proportion can be determined according to the index data, the historical index data and the preset model parameters, the test flow distributed to each test version and each release version can be determined according to the flow distribution proportion, automatic flow distribution is achieved, and therefore the test efficiency is improved. Moreover, because probability estimation is not carried out, automatic traffic distribution can be carried out on more types of data, and the applicability of traffic distribution is improved.
In a possible manner, according to the index data, the historical index data, and the target traffic distribution model, determining the test traffic distributed to each test version and each release version in the next test process may further be: the method comprises the steps of carrying out weighted summation on a plurality of index data of the same type through a target flow distribution model to obtain target index data, carrying out weighted summation on a plurality of historical index data of the same type to obtain target historical index data, and then determining test flow distributed to each test version and release version in the next test process through the target flow distribution model based on the target index data and the target historical index data.
As described above, the target flow distribution model may be selected from a preset first flow distribution model and a preset second flow distribution model according to the type of the index data, the first flow distribution model may be a thompson sampling model, and the second flow distribution model may be an e-greedy algorithm model. However, the thompson sampling model and the e-greedy algorithm model generally only perform data prediction on single data, and in order to improve accuracy of flow distribution and enhance an application range of the flow distribution model in the embodiment of the present disclosure, a content for performing comprehensive calculation on a plurality of index data of the same type may be added to the model, that is, a formula for weighted summation is added, so that the model may perform flow distribution according to the index data after the comprehensive calculation. Compared with a mode of carrying out flow distribution according to single index data, the method can improve the accuracy of flow distribution and can also improve the application range of a target flow distribution model.
Based on the same inventive concept, the disclosed embodiments also provide a traffic distribution apparatus, which may become part or all of an electronic device through software, hardware, or a combination of both. Referring to fig. 2, the traffic distribution apparatus 200 may include:
a first determining module 201, configured to determine a test version corresponding to an application;
a second determining module 202, configured to determine, for each test version, index data of the application program in a preset duration in the test version according to an initial test flow corresponding to the test version, and determine historical index data of the application program in a currently released version;
a third determining module 203, configured to determine a target traffic distribution model according to the type of the index data, where the target traffic distribution model is used to perform traffic distribution according to input index data;
a fourth determining module 204, configured to determine, according to the index data, the historical index data, and the target traffic distribution model, a test traffic distributed to each test version and the release version in a next test process.
Optionally, the third determining module 203 is configured to:
determining a target flow distribution model in a preset first flow distribution model and a preset second flow distribution model according to the type of the index data;
the first flow distribution model is used for carrying out probability calculation according to the index data and the historical index data to determine test flows distributed to each test version and the release version, and the second flow distribution model is used for determining a flow distribution ratio according to the index data, the historical index data and preset model parameters and determining the test flows distributed to each test version and the release version according to the flow distribution ratio.
Optionally, the third determining module 203 is configured to:
determining the first traffic distribution model as a target traffic distribution model in case the indicator data is target type data, wherein the target type data is indicator data that can be divided into a first category and a second category, and a result of the indicator data characterization in the first category is opposite to a result of the indicator data characterization in the second category;
determining the second traffic distribution model as a target traffic distribution model if the metric data is not the target type data.
Optionally, the fourth determining module 204 is configured to:
classifying the index data and the historical index data to determine the number of first category data and the number of second category data in the index data and the historical index data;
determining a first probability of each version corresponding to the first category data or a second probability of each version corresponding to the second category data according to the first traffic distribution model, the number of the first category data and the number of the second category data;
and determining the test flow distributed to each test version and the release version in the next test process according to the first probability or the second probability corresponding to each version.
Optionally, the fourth determining module 204 is configured to:
determining values of preset model parameters in the second traffic distribution model according to the index data and the historical index data, and determining a first distribution proportion and a second distribution proportion according to the values of the preset model parameters, wherein the first distribution proportion is used for representing a total traffic distribution proportion corresponding to each test version, and the second distribution proportion is used for representing a traffic distribution proportion corresponding to the release version;
determining target flow according to the first distribution proportion and a preset total flow, and equally distributing the target flow according to the number of the test versions to determine the test flow distributed to each test version in the next test process;
and determining the test flow distributed to the release version in the next test process according to the second distribution proportion and the preset total flow.
Optionally, the fourth determining module 204 is configured to:
weighting and summing a plurality of index data of the same type through the target flow distribution model to obtain target index data, and weighting and summing a plurality of historical index data of the same type to obtain target historical index data;
and determining the test flow distributed to each test version and release version in the next test process based on the target index data and the target historical index data through the target flow distribution model.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Based on the same inventive concept, the disclosed embodiments also provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processing device, implements the steps of any of the traffic distribution methods described above.
Based on the same inventive concept, an embodiment of the present disclosure further provides an electronic device, including:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to implement the steps of any of the traffic distribution methods described above.
Referring now to FIG. 3, a block diagram of an electronic device 300 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device 300 to communicate wirelessly or by wire with other devices to exchange data. While fig. 3 illustrates an electronic device 300 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 309, or installed from the storage means 308, or installed from the ROM 302. The computer program, when executed by the processing device 301, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the communication may be performed using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: determining a test version corresponding to the application program;
for each test version, according to the initial test flow corresponding to the test version, determining index data of the application program in a preset time length under the test version, and determining historical index data of the application program under the current release version; determining a target flow distribution model according to the type of the index data, wherein the target flow distribution model is used for carrying out flow distribution according to input index data; and determining the test flow distributed to each test version and the release version in the next test process according to the index data, the historical index data and the target flow distribution model.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the name of a module in some cases does not constitute a limitation on the module itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Example 1 provides a traffic distribution method according to one or more embodiments of the present disclosure, including:
determining a test version corresponding to the application program;
for each test version, according to the initial test flow corresponding to the test version, determining index data of the application program in a preset time length under the test version, and determining historical index data of the application program under the current release version;
determining a target flow distribution model according to the type of the index data, wherein the target flow distribution model is used for carrying out flow distribution according to input index data;
and determining the test flow distributed to each test version and the release version in the next test process according to the index data, the historical index data and the target flow distribution model.
Example 2 provides the method of example 1, the determining a target traffic distribution model according to the type of the metric data, including:
determining a target flow distribution model in a preset first flow distribution model and a preset second flow distribution model according to the type of the index data;
the first flow distribution model is used for carrying out probability calculation according to the index data and the historical index data to determine test flows distributed to each test version and the release version, and the second flow distribution model is used for determining a flow distribution ratio according to the index data, the historical index data and preset model parameters and determining the test flows distributed to each test version and the release version according to the flow distribution ratio.
Example 3 provides the method of example 2, in which, in the preset first and second traffic distribution models, determining a target traffic distribution model according to the type of the index data includes:
determining the first traffic distribution model as a target traffic distribution model in case the indicator data is target type data, wherein the target type data is indicator data that can be divided into a first category and a second category, and a result of the indicator data characterization in the first category is opposite to a result of the indicator data characterization in the second category;
determining the second traffic distribution model as a target traffic distribution model if the metric data is not the target type data.
Example 4 provides the method of example 2 or 3, wherein determining, according to the indicator data, the historical indicator data, and the target traffic distribution model, the test traffic distributed to each test version and the release version in the next test procedure comprises:
classifying the index data and the historical index data to determine the number of first category data and the number of second category data in the index data and the historical index data;
determining a first probability of each version corresponding to the first category data or a second probability of each version corresponding to the second category data according to the first traffic distribution model, the number of the first category data and the number of the second category data;
and determining the test flow distributed to each test version and the release version in the next test process according to the first probability or the second probability corresponding to each version.
Example 5 provides the method of example 2 or 3, wherein determining, according to the indicator data, the historical indicator data, and the target traffic distribution model, the test traffic distributed to each test version and the release version in the next test procedure comprises:
determining values of preset model parameters in the second traffic distribution model according to the index data and the historical index data, and determining a first distribution proportion and a second distribution proportion according to the values of the preset model parameters, wherein the first distribution proportion is used for representing a total traffic distribution proportion corresponding to each test version, and the second distribution proportion is used for representing a traffic distribution proportion corresponding to the release version;
determining target flow according to the first distribution proportion and a preset total flow, and equally distributing the target flow according to the number of the test versions to determine the test flow distributed to each test version in the next test process;
and determining the test flow distributed to the release version in the next test process according to the second distribution proportion and the preset total flow.
Example 6 provides the method of any one of examples 1 to 3, wherein determining, according to the indicator data, the historical indicator data, and the target traffic distribution model, the test traffic distributed to each test version and the release version in the next test procedure includes:
weighting and summing a plurality of index data of the same type through the target flow distribution model to obtain target index data, and weighting and summing a plurality of historical index data of the same type to obtain target historical index data;
and determining the test flow distributed to each test version and release version in the next test process based on the target index data and the target historical index data through the target flow distribution model.
Example 7 provides, in accordance with one or more embodiments of the present disclosure, a traffic distribution apparatus, the apparatus comprising:
the first determining module is used for determining a test version corresponding to the application program;
the second determining module is used for determining the index data of the application program in the test version within a preset time length according to the initial test flow corresponding to the test version and determining the historical index data of the application program in the current release version aiming at each test version;
a third determining module, configured to determine a target traffic distribution model according to the type of the index data, where the target traffic distribution model is configured to perform traffic distribution according to input index data;
and the fourth determining module is used for determining the test flow distributed to each test version and the release version in the next test process according to the index data, the historical index data and the target flow distribution model.
Example 8 provides the apparatus of example 7, the third determination module to:
determining a target flow distribution model in a preset first flow distribution model and a preset second flow distribution model according to the type of the index data;
the first flow distribution model is used for carrying out probability calculation according to the index data and the historical index data to determine test flows distributed to each test version and the release version, and the second flow distribution model is used for determining a flow distribution ratio according to the index data, the historical index data and preset model parameters and determining the test flows distributed to each test version and the release version according to the flow distribution ratio.
Example 9 provides a non-transitory computer-readable storage medium having stored thereon, a computer program that, when executed by a processing device, implements the steps of the method of any of examples 1-6, in accordance with one or more embodiments of the present disclosure.
Example 10 provides, in accordance with one or more embodiments of the present disclosure, an electronic device comprising:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to carry out the steps of the method of any of examples 1-6.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Claims (10)
1. A method of traffic distribution, the method comprising:
determining a test version corresponding to the application program;
for each test version, according to the initial test flow corresponding to the test version, determining index data of the application program in a preset time length under the test version, and determining historical index data of the application program under the current release version;
determining a target flow distribution model according to the type of the index data, wherein the target flow distribution model is used for carrying out flow distribution according to input index data;
and determining the test flow distributed to each test version and the release version in the next test process according to the index data, the historical index data and the target flow distribution model.
2. The method of claim 1, wherein determining a target traffic distribution model based on the type of metric data comprises:
determining a target flow distribution model in a preset first flow distribution model and a preset second flow distribution model according to the type of the index data;
the first flow distribution model is used for carrying out probability calculation according to the index data and the historical index data to determine test flows distributed to each test version and the release version, and the second flow distribution model is used for determining a flow distribution ratio according to the index data, the historical index data and preset model parameters and determining the test flows distributed to each test version and the release version according to the flow distribution ratio.
3. The method according to claim 2, wherein the determining a target traffic distribution model according to the type of the index data in the preset first traffic distribution model and the preset second traffic distribution model includes:
determining the first traffic distribution model as a target traffic distribution model in case the indicator data is target type data, wherein the target type data is indicator data that can be divided into a first category and a second category, and a result of the indicator data characterization in the first category is opposite to a result of the indicator data characterization in the second category;
determining the second traffic distribution model as a target traffic distribution model if the metric data is not the target type data.
4. The method according to claim 2 or 3, wherein the determining, according to the index data, the historical index data and the target traffic distribution model, the test traffic distributed to each test version and the release version in the next test process comprises:
classifying the index data and the historical index data to determine the number of first category data and the number of second category data in the index data and the historical index data;
determining the probability of each version corresponding to the first category data or the second probability of each version corresponding to the second category data according to the first traffic distribution model, the number of the first category data and the number of the second category data;
and determining the test flow distributed to each test version and the release version in the next test process according to the first probability or the second probability corresponding to each version.
5. The method according to claim 2 or 3, wherein the determining, according to the index data, the historical index data and the target traffic distribution model, the test traffic distributed to each test version and the release version in the next test process comprises:
determining a first traffic distribution ratio of each test version and the release version according to preset model parameters of the second traffic distribution model;
according to the index data and the historical index data, determining a target version with better index performance in a test version and the release version, and determining the remaining flow distribution ratio as a second distribution ratio of the target version on the basis of the first flow distribution ratio of each test version and the release version;
and determining the test flow distributed to each test version and the release version in the next test process according to the first distribution proportion, the second distribution proportion and the preset total flow.
6. The method according to any one of claims 1 to 3, wherein the determining, according to the index data, the historical index data and the target traffic distribution model, the test traffic distributed to each test version and the release version in the next test process comprises:
weighting and summing a plurality of index data of the same type through the target flow distribution model to obtain target index data, and weighting and summing a plurality of historical index data of the same type to obtain target historical index data;
and determining the test flow distributed to each test version and the release version in the next test process based on the target index data and the target historical index data through the target flow distribution model.
7. A flow distribution apparatus, comprising:
the first determining module is used for determining a test version corresponding to the application program;
the second determining module is used for determining the index data of the application program in the test version within a preset time length according to the initial test flow corresponding to the test version and determining the historical index data of the application program in the current release version aiming at each test version;
a third determining module, configured to determine a target traffic distribution model according to the type of the index data, where the target traffic distribution model is configured to perform traffic distribution according to input index data;
and the fourth determining module is used for determining the test flow distributed to each test version and the release version in the next test process according to the index data, the historical index data and the target flow distribution model.
8. The apparatus of claim 7, wherein the third determining module is configured to:
determining a target flow distribution model in a preset first flow distribution model and a preset second flow distribution model according to the type of the index data;
the first flow distribution model is used for carrying out probability calculation according to the index data and the historical index data to determine test flows distributed to each test version and the release version, and the second flow distribution model is used for determining a flow distribution ratio according to the index data, the historical index data and preset model parameters and determining the test flows distributed to each test version and the release version according to the flow distribution ratio.
9. A non-transitory computer readable storage medium, having stored thereon a computer program, characterized in that the program, when executed by a processing device, implements the steps of the method of any one of claims 1-6.
10. An electronic device, comprising:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to carry out the steps of the method according to any one of claims 1 to 6.
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