CN115952098A - Performance test tuning scheme recommendation method and system - Google Patents
Performance test tuning scheme recommendation method and system Download PDFInfo
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- CN115952098A CN115952098A CN202310011854.9A CN202310011854A CN115952098A CN 115952098 A CN115952098 A CN 115952098A CN 202310011854 A CN202310011854 A CN 202310011854A CN 115952098 A CN115952098 A CN 115952098A
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Abstract
The application relates to the technical field of information push, and discloses a method and a system for recommending a performance test tuning scheme, wherein the method comprises the following steps: a method for recommending a performance test tuning scheme comprises the following steps: acquiring historical performance test schemes and corresponding tuning schemes of all application programs in a test platform; constructing a tuning scheme database, wherein the tuning scheme database comprises a test scheme recommending module and a tuning scheme recommending module; acquiring a test scheme recommendation request of a current application program sent by a test end; controlling a test scheme recommending module to generate a recommended test scheme of the current application program, and then sending the recommended test scheme to a test end; or acquiring a tuning scheme recommendation request of the current application program sent by the test terminal; and the tuning scheme recommendation control module generates a recommended tuning scheme of the current application program and then sends the recommended tuning scheme to the test end. The method and the device have the effect of improving the performance test of the application program and the obtaining efficiency of the tuning scheme when a tester is unclear about the test method and the tuning means.
Description
Technical Field
The invention relates to the technical field of information pushing, in particular to a performance test tuning scheme recommendation method and system.
Background
The definition of the performance test is to simulate various normal, peak and abnormal load conditions through an automatic test tool to test various performance indexes of the system. Both load tests and pressure tests belong to the performance tests, and both can be performed in combination. The performance of the system under various working loads is determined through load tests, and the aim is to know the change of various performance indexes of the system when the load is gradually increased. Stress testing is a test that achieves the maximum level of service that a system can provide by determining a bottleneck or unacceptable performance point for the system.
At present, a database manager or a system operation and maintenance worker is generally relied on to monitor the operation data of the application program, perform performance test on the application program, and perform parameter optimization on the application program according to a test result, so that a system environment can be adapted to the operation characteristics of the application program to achieve better performance.
In view of the above-mentioned related technologies, the inventor believes that, due to a large number of parameters to be adjusted, part of testers are not aware of the testing method and tuning means, and generally choose to query data or ask people with a large experience, which may result in a decrease in the efficiency of performance testing of the application program and obtaining of the tuning scheme.
Disclosure of Invention
In order to improve the efficiency of performance testing of an application program and the efficiency of obtaining a tuning scheme when a testing person is unclear about a testing method or a tuning means, the application provides a method and a system for recommending the performance testing tuning scheme.
In a first aspect, the method for recommending a performance test tuning scheme provided by the application adopts the following technical scheme:
a method for recommending a performance test tuning scheme is applied to a server and comprises the following steps:
acquiring historical performance test schemes and corresponding tuning schemes of all application programs in a test platform;
constructing a tuning scheme database based on the historical performance test schemes of all the application programs and corresponding tuning schemes, wherein the tuning scheme database comprises a test scheme recommending module and a tuning scheme recommending module;
acquiring a test scheme recommendation request of a current application program sent by a test end;
based on the test scheme recommendation request, controlling the test scheme recommendation module to generate a recommended test scheme of the current application program, and then sending the recommended test scheme to a test end;
or acquiring a tuning scheme recommendation request of the current application program sent by the test terminal;
and controlling the tuning scheme recommending module to generate a recommended tuning scheme of the current application program based on the tuning scheme recommending request, and then sending the recommended tuning scheme to the testing end.
By adopting the technical scheme, the server collects the historical performance test schemes and the corresponding tuning schemes of all the application programs which are tested and tuned through the test platform to form a tuning scheme database, when the subsequent application programs encounter problems in the aspect of testing or tuning, the testing schemes or the tuning schemes can be automatically recommended through the tuning scheme database, so that testers at a testing end can directly refer to the tuning scheme database without turning over data or inquiring experienced people, the time can be greatly saved, and the efficiency of performance testing of the application programs and the efficiency of obtaining the tuning schemes are improved.
Optionally, the step of constructing a tuning scheme database based on the historical performance test schemes of all the application programs and the corresponding tuning schemes includes:
based on the historical performance test schemes and the corresponding tuning schemes of all the application programs, screening and classifying the historical performance test schemes and the corresponding tuning schemes, and then constructing a tuning scheme database;
the step of sending the recommended test scheme or the recommended tuning scheme to the test end comprises the following steps:
and acquiring a test result case or a tuning result case of the current application program fed back by the test end, and reconstructing the tuning scheme database.
By adopting the technical scheme, when the historical performance test scheme and the corresponding tuning scheme are obtained, the scheme with low correlation degree is screened and processed, then the classification processing is carried out according to the application programs of different types, when the test scheme or the tuning scheme needs to be recommended to the current application program, the related cases of the corresponding type are convenient to be quickly matched, and therefore the efficiency of the server for recommending the test scheme or the tuning scheme to the current application program is improved.
Optionally, after the step of controlling the tuning scheme recommendation module to generate the recommended tuning scheme of the current application program, the method further includes:
analyzing the matching degree of the recommended tuning scheme and the current application program;
if the matching degree is lower than a preset matching degree threshold value, acquiring historical performance tuning schemes of application programs of other standby platforms;
reconstructing a tuning scheme database by combining historical performance tuning schemes of the application programs of the other standby platforms;
and re-controlling the tuning scheme recommendation module to generate the recommended tuning scheme of the current application program.
By adopting the technical scheme, under the general condition, the server can recommend the tuning scheme according to the existing cases in the tuning scheme database, but the matching degree of the closest case in the historical cases and the current application program may not necessarily meet the requirement, the more relevant historical tuning scheme is obtained from the standby platform, the tuning scheme database is reconstructed, the tuning scheme database is continuously in deep learning, and therefore the performance tuning scheme more suitable for the current application program can be matched and recommended.
Optionally, the step of analyzing the matching degree between the recommended tuning scheme and the current application program includes:
analyzing reference data of the recommended tuning scheme and reference data of a current application program, wherein the reference data comprises workload flow, the number of load generators, script writing types and user thinking time;
and obtaining the matching degree of the recommended tuning scheme and the current application program according to the reference data of the recommended tuning scheme and the reference data of the current application program.
By adopting the technical scheme, the accuracy of the matching degree can be improved by matching the dimensions of the workload flow, the number of load generators, the script writing type and the user thinking time.
Optionally, the step of analyzing the reference data of the recommended tuning scheme and the reference data of the current application program includes:
analyzing the number of the load generators of the recommended tuning scheme and the number of the load generators of the current application program to obtain the number of virtual users of the recommended tuning scheme and the number of the load generators of the current application program;
acquiring the difference value of the number of the virtual users of the two users;
if the difference value reaches a preset difference value threshold value, judging whether the number of the virtual users of the recommended tuning scheme is larger than the number of the virtual users of the current application program;
if the number of the virtual users of the recommended tuning scheme is larger than that of the virtual users of the current application program;
sending a request for judging whether real users needing to link the difference value participate in the performance test and the tuning to a test end;
acquiring real users needing to link the difference value and sent by a test end to participate in performance test and tuning;
and linking the real users of the difference values to participate in performance testing and tuning.
By adopting the technical scheme, when reference data of the recommended tuning scheme and the current application program are obtained, the number of the load generators of the recommended tuning scheme and the current application program is analyzed besides the script type, the number of the virtual users participating in the performance test can be reflected through the number of the load generators, each virtual user corresponds to one piece of test data, if the number of the virtual users participating in the performance test in the recommended tuning scheme is far more than that of the virtual users of the current application program, whether the number of the virtual users needs to be supplemented by the current application program or not needs to be considered, and therefore the accuracy of the performance test of the current application program is improved. Meanwhile, if the virtual user is excessively utilized, risks exist all the time, and the test result may be unreliable, so that the real user can be selected to be linked to participate in the performance test in the process of adjusting the performance test, the accuracy of the test result is improved, and the server can recommend the tuning scheme more accurately in the later stage.
Optionally, after the step of determining whether the number of the virtual users of the recommended tuning scheme is greater than the number of the virtual users of the current application program, the method further includes:
if the number of the virtual users of the recommended tuning scheme is not larger than the number of the virtual users of the current application program;
the number of load generators of the current application is reduced.
By adopting the technical scheme, if the number of the virtual users of the current application program is too large, in order to improve the adaptation degree with the recommended tuning scheme, the number of the load generators of the current application program is reduced, so that the adaptation of the current application program and the recommended tuning scheme is improved.
Optionally, the step of analyzing the user thinking time of the recommended tuning scheme and the user thinking time of the current application program comprises the following steps:
obtaining the difference value of the user thinking time of the two;
judging whether the difference value of the user thinking time of the two is within a preset difference value range or not;
if the difference value is not within the preset difference value range, obtaining the average user thinking time of the real user using the current application program;
and adjusting the recommended tuning scheme according to the average user thinking time of the real user using the current application program, and recommending the recommended tuning scheme to the test terminal.
By adopting the technical scheme, if the difference value of the user thinking time of the two users is too large, whether the user thinking time of the virtual user is set by mistake between the two users needs to be considered, so that the recommended tuning scheme needs to be verified and optimized by combining the user thinking time of the real user, and the recommended tuning scheme has higher referential property.
In a second aspect, the present application provides a system for recommending a performance test tuning scheme, which adopts the following technical scheme:
a performance test tuning scheme recommendation system is a server and comprises:
the acquisition module is used for acquiring historical performance test schemes and corresponding tuning schemes of all application programs in the test platform;
the processing module is used for constructing a tuning scheme database based on the historical performance test schemes and the corresponding tuning schemes of all the application programs, and the tuning scheme database comprises a test scheme recommending module and a tuning scheme recommending module;
the acquisition module is also used for acquiring a test scheme recommendation request of the current application program sent by the test terminal;
the processing module is further configured to control the test scheme recommending module to generate a recommended test scheme of the current application program based on the test scheme recommending request;
the sending module is used for sending the recommended test scheme of the current application program to a test end;
the obtaining module is further used for obtaining a tuning scheme recommendation request of the current application program sent by the testing end;
the processing module is also used for controlling the tuning scheme recommending module to generate a recommended tuning scheme of the current application program based on the tuning scheme recommending request;
and the sending module is used for sending the recommended tuning scheme of the current application program to a testing end.
By adopting the technical scheme, the server collects the historical performance test schemes and the corresponding tuning schemes of all the application programs which are tested and tuned through the test platform to form a tuning scheme database, when the subsequent application programs encounter problems in the aspect of testing or tuning, the testing schemes or the tuning schemes can be automatically recommended through the tuning scheme database, so that testers at a testing end can directly refer to the tuning scheme database without turning over data or inquiring experienced people, the time can be greatly saved, and the efficiency of performance testing of the application programs and the efficiency of obtaining the tuning schemes are improved.
In a third aspect, the present application provides a computer device, which adopts the following technical solution:
a computer device comprising a processor, a memory for storing instructions, a user interface and a network interface for communicating with other devices, the processor being configured to execute the instructions stored in the memory to cause the computer device to perform the method as described above.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer readable storage medium having stored thereon instructions which, when executed, perform the method steps as described above.
To sum up, this application includes following beneficial technological effect:
1. the server collects the historical performance test schemes and the corresponding tuning schemes of all the application programs which are tested and tuned through the test platform to form a tuning scheme database, when the subsequent application programs encounter problems in the testing aspect or the tuning aspect, the performance test schemes or the tuning schemes can be automatically recommended through the tuning scheme database, and testers do not need to browse data or inquire experienced people, so that the time can be greatly saved, and the acquisition efficiency of the performance test and the tuning schemes of the application programs is improved;
2. and analyzing the recommended tuning scheme and the number of load generators of the current application program, reflecting the number of virtual users participating in the performance test through the number of load generators, and considering whether the number of virtual users needs to be supplemented to the current application program or not if the number of virtual users participating in the performance test in the recommended tuning scheme is far more than that of the virtual users of the current application program, so that the accuracy of the performance test of the current application program is improved. Meanwhile, in the process of adjusting the performance test, the real user can be selected to be linked to participate in the performance test, so that the accuracy of the test result is improved, and the later-stage more accurate adjustment is facilitated.
Drawings
Fig. 1 is an overall flowchart of a performance test tuning scheme recommendation method according to an embodiment of the present application;
FIG. 2 is an expanded flow chart of step S400 in FIG. 1;
FIG. 3 is an expanded flow chart of step S410 in FIG. 2;
fig. 4 is an expanded flowchart of step S411 in fig. 3;
fig. 5 is an expanded flowchart after step S411d in fig. 4;
FIG. 6 is a schematic structural diagram of a customized enterprise information batch pushing system according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Description of reference numerals:
1. an acquisition module; 2. a processing module; 3. a sending module; 400. an electronic device; 401. a processor; 402. a communication bus; 403. a user interface; 404. a network interface; 405. a memory.
Detailed Description
The present application is described in further detail below with reference to figures 1-7.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
In the description of the embodiments of the present application, the words "exemplary," "for example," or "for instance" are used to indicate instances, or illustrations. Any embodiment or design described herein as "exemplary," "e.g.," or "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the words "exemplary," "such as," or "for example" are intended to present relevant concepts in a concrete fashion.
Furthermore, the terms "first," "second," and the like in the description of the present application are used for distinguishing between different objects and not necessarily for describing a particular order, and may explicitly or implicitly include one or more of the features.
The embodiment of the application discloses a customized enterprise information batch pushing method, which is applied to a server, and comprises the following steps of S100-S500 by referring to FIG. 1:
step S100: and acquiring historical performance test schemes and corresponding tuning schemes of all application programs in the test platform.
Specifically, the test platform may be an x86 or arm platform, and the mounted test tools may be a kylinTOP test and monitor platform, kylinPET, loadRunner, apache meter, openSTA, load impact, log, and the like. For example, in the historical data, on an x86 test platform, a LoadRunner test tool is used to perform performance test on 50 types of application programs, and a corresponding tuning scheme is set, so that a historical performance test scheme and a corresponding tuning scheme of the 50 types of application programs are obtained.
Step S200: and constructing a tuning scheme database based on the historical performance test schemes of all the application programs and the corresponding tuning schemes, wherein the tuning scheme database comprises a test scheme recommending module and a tuning scheme recommending module.
Before the tuning scheme database is constructed, the historical performance test scheme and the corresponding tuning scheme need to be screened and classified based on the historical performance test scheme and the corresponding tuning scheme of all the application programs. The method comprises the steps of screening the schemes with low correlation degree, then carrying out classification processing according to different types of application programs, and when a test scheme or an optimization scheme needs to be recommended to the current application program, conveniently and quickly matching the corresponding types of relevant cases of the application programs, such as life type, social type, office type, photographic type, audio type, news type and shopping type.
Step S300: and acquiring a test scheme recommendation request of the current application program sent by the test terminal.
Or obtaining a tuning scheme recommendation request of the current application program sent by the testing end.
Step S400: and controlling a test scheme recommending module to generate a recommended test scheme of the current application program based on the test scheme recommending request.
Or controlling the tuning scheme recommending module to generate the recommended tuning scheme of the current application program based on the tuning scheme recommending request.
Specifically, when a tester at a testing end is unfamiliar with a performance testing method, the architecture and the tuning means of a testing platform are unclear, a testing result does not meet requirements, and a testing means or a tuning scheme is questioned, the testing scheme recommending module and the tuning scheme recommending module can select according to a testing scheme or a tuning scheme of the previous application program of the same type, and then recommend the testing scheme or the tuning scheme most suitable for the current application program. It should be noted that the test scheme recommending module and the tuning scheme recommending module are existing algorithm programs and have a function of matching data.
Step S500: and sending the recommended test scheme or the recommended tuning scheme to the test end.
By obtaining the test scheme or the tuning scheme and referring to the relevant execution script and the performance parameters of the test scheme or the tuning scheme, the tester at the test end can more clearly understand the next performance test step or more understand how to tune the performance parameters.
The step of sending the recommended test scheme or the recommended tuning scheme to the test end comprises the following steps:
and acquiring a test result case or a tuning result case of the current application program fed back by the test end, and reconstructing the tuning scheme database.
Specifically, because the tuning scheme database is in the continuous optimization process, after the test of the current application program is finished or the tuning is performed, the test result case or the tuning result case is fed back to the tuning scheme database, so that the data of the tuning scheme database is richer, and the accuracy of the subsequent recommended performance test scheme or the tuning scheme is improved.
In another embodiment, referring to fig. 2, the step S400 of building a tuning plan database based on historical performance test plans and corresponding tuning plans of all applications includes steps S410 to S450:
step S410: and analyzing the matching degree of the recommended tuning scheme and the current application program.
Step S420: and judging whether the matching degree is lower than a preset matching degree threshold value.
Step S430: if the matching degree is lower than a preset matching degree threshold value, acquiring historical performance tuning schemes of application programs of other standby platforms; and if the matching degree is not lower than the preset matching degree threshold value, sending the recommended tuning scheme to the test end.
Step S440: and after the historical performance tuning schemes of the application programs of other standby platforms are obtained, reconstructing a tuning scheme database by combining the historical performance tuning schemes of the application programs of other standby platforms.
Step S450: and the tuning scheme recommendation module is controlled again to generate the recommended tuning scheme of the current application program.
In another embodiment, referring to FIG. 3, the step S410 of analyzing the matching degree between the recommended tuning scheme and the current application includes steps S411 to S412.
Step S411: and analyzing reference data of the recommended tuning scheme and reference data of the current application program, wherein the reference data comprises workload flow, the number of load generators, script writing types and user thinking time.
Step S412: and obtaining the matching degree of the recommended tuning scheme and the current application program according to the reference data of the recommended tuning scheme and the reference data of the current application program.
Specifically, when the matching degree of the recommended tuning scheme and the current application program is analyzed, matching is performed from these dimensions one by matching some typical reference data, such as workload flow, the number of load generators, the type of script written, and user thinking time, and when the integrated matching degree can reach a threshold value of the matching degree, the recommended tuning scheme is indicated to be referred by a tester at a testing end. For example, the matching degrees of the workload flow, the number of load generators, the script writing type and the user thinking time are all calculated according to 100%, the matching degrees of the four dimensions are respectively measured, and then the average of the four matching degrees is taken, namely the matching degree of the recommended tuning scheme and the current application program in the scheme; if the matching degree after the average number is 75% and the threshold value of the matching degree is reached (70%), the recommended tuning scheme is indicated to be referred by the tester at the testing end; meanwhile, if the matching degree threshold value (70%) is not reached, some tuning cases of other testing platforms (such as x86 and arm platforms) need to be referred to, then the cases are integrated into a tuning scheme database, and then the server controls the tuning scheme recommending module to recommend a more accurate tuning scheme to the testing end again, so that the tuning scheme database continuously supplements new cases and is in deep learning.
In another embodiment, referring to FIG. 4, the step S411 comprises steps S411a to S411g after analyzing the reference data of the recommended tuning scheme and the reference data of the current application program in step S411.
Step S411a: and analyzing the number of the virtual users based on the number of the load generators of the recommended tuning scheme and the number of the load generators of the current application program.
Step S411b: and acquiring the difference value of the number of the virtual users of the two.
Step S411c: and judging whether the difference value of the number of the virtual users of the two users reaches a preset difference value norm threshold value.
Step S411d: if the difference value reaches a preset difference value threshold value, judging whether the number of the virtual users recommending the tuning scheme is larger than the number of the virtual users of the current application program; and if the difference value does not reach the preset difference value threshold value, analyzing the user thinking time of the difference value and the user thinking time of the difference value.
Step S411e: if the number of the virtual users recommending the tuning scheme is larger than that of the virtual users of the current application program, sending a request for judging whether real users needing to link the difference value participate in the performance test and the tuning to a test end; and if the number of the virtual users recommending the tuning scheme is not larger than that of the virtual users of the current application program, reducing the number of load generators of the current application program.
Step S411f: and acquiring the real user participation performance test and the adjustment optimization of the link difference value required to be sent by the test end.
Step S411g: and the real users of the link difference values participate in performance test and tuning.
Specifically, in the present solution, an optimal matching degree analysis manner is to sequentially obtain matching degrees of the script writing type, the number of load generators, the user thinking time, and the workload traffic. The script type writing has an important decision function, and whether the types of the script type writing and the script type writing are suitable or not can be preliminarily matched, so that the matching degree of the script type writing is acquired preferentially by default. On the premise that the matching degree of the writing script type meets the requirement, the matching degree of the load generator is continuously obtained; on the premise that the matching degree of the load generator meets the requirement, the matching degree of the user thinking time is continuously obtained; and by analogy, finally obtaining the matching degree of the workload flow.
At the same time, it is ensured that the load generator, i.e. the computer running the virtual user test, is ready to bear the workload, and the virtual user can use scripts or application software, whose behavior is the same as the behavior of a real user when making a request to the application and system under test at the same time. There are a few things to consider here: during the performance test, any software that is not needed is suspended from the computer, it is confirmed that the computer is connected to the network and has enough network bandwidth, etc.; if many virtual users are to be run, many load generators are required; it is important to know all of this because if the load generator itself is over-utilized, there is always a risk that the test results may be unreliable. When the matching degree of the load generator is obtained, if many virtual users are to be run, many load generators are required.
The number of the virtual users participating in the performance test can be reflected through the number of the load generators, each virtual user corresponds to one piece of test data, if the number of the virtual users participating in the performance test in the recommended tuning scheme is far more than that of the virtual users of the current application program, whether the current application program needs to be supplemented with the number of the virtual users needs to be considered at the moment, and therefore the accuracy of the performance test of the current application program is improved.
Based on the above consideration, when the difference (e.g. 100) between the numbers of the virtual users reaches the preset difference threshold (e.g. 50), the test end needs to be queried whether the real user needs to participate in the performance test or performance tuning, because the accuracy of the test result can be improved under the participation of the real user; specifically, if the tester at the testing end has a question or is unclear about the tuning scheme at this time, and the difference between the number of virtual users is large, the performance testing process at the previous step needs to be returned, so that the real users or the newly added load generators are re-linked to supplement the testing data, so as to reduce the contingency of the performance testing result; if the number of the virtual users of the current application program is too large, in order to adapt to the recommended tuning scheme, the number of the load generators of the current application program is reduced, so that the number of the virtual users of the current application program is consistent with the number of the virtual users of the current application program, the adaptability of the virtual users of the current application program and the number of the virtual users of the current application program are improved, and the later tuning is facilitated. Therefore, the scheme can recommend the tuning scheme and correct improper parameter configuration in the performance test or tuning process.
In another embodiment, referring to FIG. 5, steps S411A to S411D are included after analyzing the user thinking time of the two in step S411D.
Step S411A: and acquiring the difference value of the user thinking time of the two.
Step S411B: and judging whether the difference value of the user thinking time of the two is within a preset difference value range.
Step S411C: if the difference value is not within the preset difference value range, acquiring the average user thinking time of the real user using the current application program; and if the difference value is within the preset difference value range, analyzing the workload flow of the recommended tuning scheme and the workload flow of the current application program.
Specifically, according to the above explanation, if the difference (e.g. 5 s) between the user thinking times of the two users is within the preset difference range (2 to 4 s), which indicates that the matching degree of the user thinking times of the two users meets the requirement, the matching degree of the workload flow is continuously analyzed backwards; if the difference value is not within the preset difference value range, it indicates that a problem may occur, and it is necessary to prove the average thinking time of the real user using the current application program.
Step S411D: and after the average user thinking time of the real user using the current application program is obtained, the recommended tuning scheme is adjusted and recommended to the test terminal according to the average user thinking time of the real user using the current application program.
User think time is an important component of script logic, and all tools should have logic to increase think time by specifying how many seconds a virtual user is expected to wait. User think time is useful for mimicking correct workloads according to the virtual user's actual behavior; failure to properly utilize thinking time is another common performance testing error. If the difference value of the user thinking time of the two users is too large, whether the user thinking time of the virtual user is set by mistake exists between the two users needs to be considered, so that the recommended tuning scheme needs to be verified and optimized by combining the user thinking time of the real user, and the recommended tuning scheme has higher referential property.
Referring to fig. 6, an embodiment of the present application discloses a batch pushing system for customized enterprise information, where the system is a server and includes an obtaining module, a processing module and a sending module.
The acquisition module is used for acquiring historical performance test schemes and corresponding tuning schemes of all application programs in the test platform;
the processing module is used for constructing a tuning scheme database based on historical performance test schemes of all application programs and corresponding tuning schemes, and the tuning scheme database comprises a test scheme recommending module and a tuning scheme recommending module;
the acquisition module is also used for acquiring a test scheme recommendation request of the current application program sent by the test terminal;
the processing module is also used for controlling the test scheme recommending module to generate a recommended test scheme of the current application program based on the test scheme recommending request;
the sending module is used for sending the recommended test scheme of the current application program to the test end;
the obtaining module is also used for obtaining a tuning scheme recommendation request of the current application program sent by the testing end;
the processing module is also used for controlling the tuning scheme recommending module to generate a recommended tuning scheme of the current application program based on the tuning scheme recommending request;
and the sending module is used for sending the recommended tuning scheme of the current application program to the testing end.
In another embodiment, the processing module is further configured to analyze reference data of the recommended tuning scheme and reference data of the current application, the reference data including workload flow, number of load generators, type of script written, and user thinking time.
The processing module is further used for obtaining the matching degree of the recommended tuning scheme and the current application program according to the reference data of the recommended tuning scheme and the reference data of the current application program.
In another embodiment, the processing module is further configured to analyze the number of virtual users of the recommended tuning scheme and the number of load generators of the current application program to obtain the number of the load generators of the recommended tuning scheme and the number of the load generators of the current application program.
The obtaining module is further configured to obtain a difference value between the two virtual user numbers.
The processing module is further used for judging whether the difference value of the number of the virtual users of the two reaches a preset difference value norm threshold value.
The processing module is further used for judging whether the number of the virtual users recommending the tuning scheme is larger than the number of the virtual users of the current application program or not when the difference value reaches a preset difference value threshold value; and when the difference value does not reach the preset difference value threshold value, analyzing the user thinking time of the two.
If the number of the virtual users recommending the tuning scheme is larger than that of the virtual users of the current application program, the sending module is also used for sending a request for whether the real users needing the link difference value participate in the performance test and the tuning to the test end; and if the number of the virtual users recommending the tuning scheme is not more than the number of the virtual users of the current application program, the processing module is also used for reducing the number of load generators of the current application program.
The obtaining module is further used for obtaining the real users needing to link the difference value and sent by the testing end to participate in the performance testing and the adjusting and optimizing.
The processing module is also used for linking real users of the difference values to participate in performance testing and tuning.
In another embodiment, the obtaining module is further configured to obtain a difference value between the user thinking times of the two.
The processing module is also used for judging whether the difference value of the user thinking time of the two is within a preset difference value range; if the difference value is not within the preset difference value range, the obtaining module is further used for obtaining the average user thinking time of the real user using the current application program; and if the difference value is within the preset difference value range, analyzing the workload flow of the recommended tuning scheme and the workload flow of the current application program.
The obtaining module is further used for adjusting the recommended tuning scheme according to the average user thinking time of the real user using the current application program after obtaining the average user thinking time of the real user using the current application program, and recommending the adjusted optimized scheme to the testing terminal.
It should be noted that: in the system provided in the above embodiment, when the functions of the system are implemented, only the division of the functional modules is illustrated, and in practical application, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to implement all or part of the functions described above. In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure. As shown in fig. 7, the computer device 400 may include: at least one processor 401, at least one network interface 404, a user interface 403, memory 405, at least one communication bus 402.
Wherein a communication bus 402 is used to enable connective communication between these components.
The user interface 403 may include a Display screen (Display) and a Camera (Camera), and the user interface 403 may also include a standard wired interface and a wireless interface.
The network interface 404 may include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 405 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable medium. The memory 405 may be used to store instructions, programs, code sets, or instruction sets. The memory 405 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store the data and the like referred to above in the respective method embodiments. The memory 405 may alternatively be at least one storage device located remotely from the aforementioned processor 401.
As shown in fig. 7, the present application provides a computer-readable storage medium storing instructions that, when executed, perform the method steps of any one of the embodiments.
The memory 405, which is a type of computer-readable storage medium, may include therein an operating system, a network communication module, a user interface module, and an application program of the performance test tuning scheme recommendation method. In the computer device 400 shown in fig. 7, the user interface 403 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and processor 401 may be configured to invoke an application program stored in memory 405 that stores the performance test tuning scheme recommendations, which when executed by one or more processors, causes computer device 400 to perform the methods as described in one or more of the above embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required for this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some service interfaces, devices or units, and may be an electrical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned memory comprises: various media that can store program codes, such as a U disk, a removable hard disk, a magnetic disk, or an optical disk.
The above are merely exemplary embodiments of the present disclosure, and the scope of the present disclosure should not be limited thereby. It is intended that all equivalent variations and modifications made in accordance with the teachings of the present disclosure be covered thereby. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (10)
1. A performance test tuning scheme recommendation method is applied to a server and is characterized by comprising the following steps:
acquiring historical performance test schemes and corresponding tuning schemes of all application programs in a test platform;
constructing a tuning scheme database based on the historical performance test schemes of all the application programs and corresponding tuning schemes, wherein the tuning scheme database comprises a test scheme recommending module and a tuning scheme recommending module;
acquiring a test scheme recommendation request of a current application program sent by a test end;
based on the test scheme recommendation request, controlling the test scheme recommendation module to generate a recommended test scheme of the current application program, and then sending the recommended test scheme to a test end;
or acquiring a tuning scheme recommendation request of the current application program sent by the test terminal;
and controlling the tuning scheme recommending module to generate a recommended tuning scheme of the current application program based on the tuning scheme recommending request, and then sending the recommended tuning scheme to the testing end.
2. The method of claim 1, wherein the performance test tuning scheme is applied to the mobile device,
the step of constructing a tuning scheme database based on the historical performance test scheme and the corresponding tuning scheme of all the application programs comprises the following steps:
based on the historical performance test schemes and the corresponding tuning schemes of all the application programs, screening and classifying the historical performance test schemes and the corresponding tuning schemes, and then constructing a tuning scheme database;
the step of sending the recommended test scheme or the recommended tuning scheme to the test end comprises the following steps:
and acquiring a test result case or a tuning result case of the current application program fed back by the test end, and reconstructing the tuning scheme database.
3. The method of claim 1, wherein the step of controlling the tuning scheme recommendation module to generate the recommended tuning scheme of the current application is further followed by:
analyzing the matching degree of the recommended tuning scheme and the current application program;
if the matching degree is lower than a preset matching degree threshold value, acquiring historical performance tuning schemes of application programs of other standby platforms;
reconstructing a tuning scheme database by combining historical performance tuning schemes of the application programs of the other standby platforms;
and re-controlling the tuning scheme recommendation module to generate the recommended tuning scheme of the current application program.
4. The method of claim 3, wherein the step of analyzing the matching degree of the recommended tuning scheme and the current application program comprises:
analyzing reference data of the recommended tuning scheme and reference data of a current application program, wherein the reference data comprises workload flow, the number of load generators, script writing types and user thinking time;
and obtaining the matching degree of the recommended tuning scheme and the current application program according to the reference data of the recommended tuning scheme and the reference data of the current application program.
5. The method of claim 4, wherein the step of analyzing the reference data of the recommended tuning scheme and the reference data of the current application is followed by:
analyzing the number of the load generators of the recommended tuning scheme and the number of the load generators of the current application program to obtain the number of the virtual users of the recommended tuning scheme and the number of the load generators of the current application program;
acquiring the difference value of the number of the virtual users of the two users;
if the difference value reaches a preset difference value threshold value, judging whether the number of the virtual users of the recommended tuning scheme is larger than the number of the virtual users of the current application program;
if the number of the virtual users of the recommended tuning scheme is larger than that of the virtual users of the current application program, sending a request for judging whether the real users needing to link the difference value participate in the performance test and the tuning to a test end;
acquiring real users needing to link the difference value and sent by a test end to participate in performance test and tuning;
and linking the real users of the difference values to participate in performance testing and tuning.
6. The method of claim 5, wherein the step of determining whether the number of virtual users of the recommended tuning scheme is greater than the number of virtual users of the current application program further comprises:
if the number of the virtual users of the recommended tuning scheme is not larger than that of the virtual users of the current application program;
the number of load generators of the current application is reduced.
7. The method of claim 4, wherein the step of analyzing the user's thinking time of the recommended tuning scheme and the user's thinking time of the current application program is followed by the steps of:
acquiring the difference value of the user thinking time of the two;
judging whether the difference value of the user thinking time of the two is within a preset difference value range;
if the difference value is not within the preset difference value range, acquiring the average user thinking time of the real user using the current application program;
and adjusting the recommended tuning scheme according to the average user thinking time of the real user using the current application program, and recommending the adjusted scheme to the test end.
8. A performance test tuning scheme recommendation system is characterized in that the system is a server and comprises:
the acquisition module (1) is used for acquiring historical performance test schemes and corresponding tuning schemes of all application programs in the test platform;
the processing module (2) is used for constructing a tuning scheme database based on the historical performance test schemes and the corresponding tuning schemes of all the application programs, and the tuning scheme database comprises a test scheme recommending module and a tuning scheme recommending module;
the acquisition module (1) is further configured to acquire a test scheme recommendation request of a current application program sent by a test end;
the processing module (2) is further configured to control the test scheme recommending module to generate a recommended test scheme of the current application program based on the test scheme recommending request;
the sending module (3) is used for sending the recommended test scheme of the current application program to a test end;
the obtaining module (1) is further configured to obtain a tuning scheme recommendation request of a current application program sent by a testing end;
the processing module (2) is also used for controlling the tuning scheme recommending module to generate a recommended tuning scheme of the current application program based on the tuning scheme recommending request;
and the sending module (3) is used for sending the recommended tuning scheme of the current application program to a testing end.
9. A computer device comprising a processor (401), a memory (405), a user interface (403), and a network interface (404), the memory (405) being configured to store instructions, the user interface (403) and the network interface (404) being configured to communicate with other devices, the processor (401) being configured to execute the instructions stored in the memory (405) to cause the electronic device to perform the method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that it stores instructions that, when executed, perform the method steps of any of claims 1-7.
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