Cloud platform performance test result analysis method and device, electronic equipment and medium

Abstract

The invention relates to the technical field of cloud computing, in particular to a cloud platform performance test result analysis method and device, electronic equipment and a medium. Obtaining a current test result obtained by performing a performance test on the cloud platform at the current moment; obtaining a test result dynamic threshold range corresponding to the current test result; detecting whether the current test result is in a test result dynamic threshold range or not; if the current test result is within the test result dynamic threshold range, determining that the current test result is effective confidence; and if the current test result is not in the test result dynamic threshold range, determining that the current test result is not confidence, and outputting prompt information. Therefore, whether the current test result is confidence or not can be quickly and accurately judged. And when the current test result is not belief, the prompt information is output, so that whether the interference of environmental factors and the like or the real code problem affects the final performance test data can be better distinguished.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to a method, device, electronic equipment and medium for analyzing cloud platform performance test results. Background Art

[0002] As a relatively popular field in recent years, cloud computing can support users to obtain application services at any location using various terminals. It also supports dynamic scaling to meet the needs of growing application and user scale. As the number of users increases, the application performance provided by cloud computing becomes more and more important. The performance of cloud computing will directly affect the user experience. If the performance is insufficient, the user interface may respond slowly or even fail to work properly during use, thereby causing certain losses to cloud computing providers and related companies. Therefore, performance testing is more important throughout the product life cycle.

[0003] In actual performance testing scenarios, due to considerations such as team costs, the test environment resource pool is basically shared by multiple people. Therefore, the performance testing process of the cloud platform will be interfered by many factors, which will affect the final analysis and judgment of the performance test results.

[0004] On the one hand, due to the influence of the deployment service surrounding components and resource pool environment, including factors such as traffic fluctuations in the environment and component version upgrades, it is generally necessary to conduct multiple performance tests on the test version to eliminate the interference of environmental factors on the performance test results; on the other hand, development code changes may affect the final performance indicators, which requires our attention and identification. This involves how we can better distinguish whether it is interference from environmental factors or real code problems that affect the final performance test data. Therefore, how to quickly and accurately determine whether the test results are credible has become a problem that we need to solve urgently. Summary of the invention

[0005] In view of this, the present invention provides a cloud platform performance test result analysis method, device, electronic device and medium to solve the problem of how to quickly and accurately determine whether the test results are credible.

[0006] In a first aspect, the present invention provides a method for analyzing cloud platform performance test results, the method comprising:

[0007] Get the current test results of the performance test on the cloud platform at the current moment;

[0008] Get the dynamic threshold range of the test result corresponding to the current test result;

[0009] Check whether the current test result is within the dynamic threshold range of the test result;

[0010] If the current test result is within the test result dynamic threshold range, the current test result is determined to be valid and confident;

[0011] If the current test result is not within the dynamic threshold range of the test result, the current test result is determined to be untrustworthy, and a prompt message is output, the prompt message being that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than a preset level.

[0012] The cloud platform performance test result analysis method provided in the embodiment of the present application obtains the current test result obtained by performing a performance test on the cloud platform at the current moment; obtains the dynamic threshold range of the test result corresponding to the current test result; detects whether the current test result is within the dynamic threshold range of the test result, thereby ensuring the accuracy of the obtained detection result. If the current test result is within the dynamic threshold range of the test result, it is determined that the current test result is valid and reliable; if the current test result is not within the dynamic threshold range of the test result, it is determined that the current test result is untrustworthy, and a prompt message is output, the prompt message being that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than the preset level. Thus, it is possible to quickly and accurately determine whether the current test result is reliable. And when the current test result is untrustworthy, a prompt message is output, so that it is possible to better distinguish whether it is interference from environmental factors or real code problems that affect the final performance test data.

[0013] In an optional implementation, obtaining a test result dynamic threshold range corresponding to a current test result includes:

[0014] Obtain a first historical test result obtained by performing a performance test on the cloud platform at a previous moment corresponding to the current moment;

[0015] Obtain the target weight corresponding to the first historical test result;

[0016] Based on the relationship between the target weight and the first historical test result, predict the current predicted test result corresponding to the cloud platform at the current moment;

[0017] Based on the current predicted test results, a dynamic threshold range for the test results is generated.

[0018] The cloud platform performance test result analysis method provided in the embodiment of the present application obtains the first historical test result obtained by performing a performance test on the cloud platform at the previous moment corresponding to the current moment, and obtains the target weight corresponding to the first historical test result; based on the relationship between the target weight and the first historical test result, the current predicted test result corresponding to the cloud platform at the current moment is predicted, thereby ensuring the accuracy of the predicted current predicted test result corresponding to the cloud platform at the current moment. Based on the current predicted test result, a dynamic threshold range of the test result is generated, thereby ensuring the accuracy of the generated dynamic threshold range of the test result. Furthermore, whether the current test result is reliable can be detected based on the dynamic threshold range of the test result.

[0019] In an optional implementation, obtaining a target weight corresponding to the first historical test result includes:

[0020] Obtain multiple second historical test results obtained by performing multiple performance tests on the cloud platform within a preset time period before the last moment;

[0021] Get multiple initial weights;

[0022] For each second historical test result, according to the relationship between each initial weight and the second historical test result, each second predicted test result at the historical moment corresponding to the second historical test result is predicted in sequence; wherein each second predicted test result corresponding to the second historical test result corresponds to each initial weight;

[0023] For each initial weight, obtaining each second predicted test result obtained by predicting each second historical test result based on the initial weight;

[0024] For each initial weight, according to the relationship between each second prediction test result corresponding to the initial weight and each second historical test result, determine the standard error corresponding to the initial weight;

[0025] The initial weight with the smallest standard error is determined from the initial weights as the target weight.

[0026] The cloud platform performance test result analysis method provided in the embodiment of the present application obtains multiple second historical test results obtained by performing multiple performance tests on the cloud platform within a preset time before the last moment; obtains multiple initial weights; for each second historical test result, according to the relationship between each initial weight and the second historical test result, predicts each second predicted test result at the historical moment corresponding to the second historical test result in sequence, ensuring the accuracy of each second predicted test result obtained, and ensuring that each second predicted test result obtained corresponds to each initial weight. Then, for each initial weight, obtain each second predicted test result obtained based on the initial weight prediction of each second historical test result. For each initial weight, according to the relationship between each second predicted test result corresponding to the initial weight and each second historical test result, determine the standard error corresponding to the initial weight, ensuring the accuracy of determining the standard error corresponding to each initial weight. Determine the initial weight with the smallest standard error from each initial weight as the target weight, ensuring the accuracy of the determined target weight. And then, the accuracy of the current predicted test result predicted according to the target weight can be guaranteed.

[0027] In an optional implementation, for each initial weight, according to the relationship between each second prediction test result corresponding to the initial weight and each second historical test result, determining the standard error corresponding to the initial weight includes:

[0028] For each initial weight, calculating the difference between each second prediction test result and the corresponding second historical test result;

[0029] For each initial weight, a standard error corresponding to the initial weight is calculated according to the difference between each second prediction test result and the corresponding second historical test result.

[0030] The cloud platform performance test result analysis method provided in the embodiment of the present application calculates, for each initial weight, the difference between each second predicted test result and the corresponding second historical test result; for each initial weight, based on the difference between each second predicted test result and the corresponding second historical test result, the standard error corresponding to the initial weight is calculated, thereby ensuring the accuracy of the obtained standard error.

[0031] In an optional implementation, based on the relationship between the target weight and the first historical test result, predicting the current predicted test result corresponding to the cloud platform at the current moment includes:

[0032] Obtain the first prediction test result of the previous moment of the current moment corresponding to the target weight;

[0033] The difference between the first historical test result and the first predicted test result is multiplied by the target weight, and then the first predicted test result is added to obtain the current predicted test result.

[0034] The cloud platform performance test result analysis method provided in the embodiment of the present application obtains the first predicted test result of the previous moment of the current moment corresponding to the target weight; multiplies the difference between the first historical test result and the first predicted test result by the target weight, and then adds the first predicted test result to obtain the current predicted test result, thereby ensuring the accuracy of the current predicted test result.

[0035] In an optional implementation, generating a dynamic threshold range of a test result based on the current predicted test result includes:

[0036] Calculate the difference between each second prediction test result corresponding to the target weight and the corresponding second historical test result;

[0037] The average error is calculated based on the difference between each second prediction test result corresponding to the target weight and the corresponding second historical test result;

[0038] The current predicted test result minus the average error is used as the minimum value in the dynamic threshold range of the test result;

[0039] The current predicted test result plus the average error is used as the maximum value in the dynamic threshold range of the test result.

[0040] The cloud platform performance test result analysis method provided in the embodiment of the present application calculates the difference between each second predicted test result corresponding to the target weight and the corresponding second historical test result, and calculates the average error based on the difference between each second predicted test result corresponding to the target weight and the corresponding second historical test result, thereby ensuring the accuracy of the calculated average error. The current predicted test result minus the average error is used as the minimum value in the dynamic threshold range of the test result; the current predicted test result plus the average error is used as the maximum value in the dynamic threshold range of the test result, thereby ensuring the accuracy of the obtained dynamic threshold range of the test result.

[0041] In an optional implementation, after determining that the current test result is valid and confident, the method further includes:

[0042] Obtain a first historical test result obtained by performing a performance test on the cloud platform at a previous moment corresponding to the current moment;

[0043] comparing the first historical test result with the current test result;

[0044] If the first historical test result is better than the current test result, the problem to be optimized is output according to the comparison result.

[0045] The cloud platform performance test result analysis method provided in the embodiment of the present application obtains the first historical test result obtained by performing a performance test on the cloud platform at the previous moment corresponding to the current moment, and compares the first historical test result with the current test result; thereby, the performance corresponding to the cloud platform at the current moment can be judged. If the first historical test result is better than the current test result, the problem to be optimized is output according to the comparison result, which ensures the accuracy of the output problem to be optimized, so that the cloud platform can be optimized based on the problem to be optimized.

[0046] In a second aspect, the present invention provides a cloud platform performance test result analysis device, the device comprising:

[0047] The first acquisition module is used to obtain the current test result obtained by performing a performance test on the cloud platform at the current moment;

[0048] A second acquisition module is used to obtain a test result dynamic threshold range corresponding to a current test result;

[0049] A detection module is used to detect whether the current test result is within the dynamic threshold range of the test result;

[0050] A first determination module, configured to determine that the current test result is valid confidence if the current test result is within a test result dynamic threshold range;

[0051] The second determination module is used to determine that the current test result is untrustworthy if the current test result is not within the dynamic threshold range of the test result, and output a prompt message, wherein the prompt message is that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than a preset level.

[0052] The cloud platform performance test result analysis device provided in the embodiment of the present application obtains the current test result obtained by performing a performance test on the cloud platform at the current moment; obtains the dynamic threshold range of the test result corresponding to the current test result; detects whether the current test result is within the dynamic threshold range of the test result, thereby ensuring the accuracy of the obtained detection result. If the current test result is within the dynamic threshold range of the test result, it is determined that the current test result is valid and reliable; if the current test result is not within the dynamic threshold range of the test result, it is determined that the current test result is untrustworthy, and a prompt message is output, the prompt message being that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than the preset level. Thus, it is possible to quickly and accurately determine whether the current test result is reliable. And when the current test result is untrustworthy, a prompt message is output, so that it is possible to better distinguish whether it is interference from environmental factors or real code problems that affect the final performance test data.

[0053] In a third aspect, the present invention provides an electronic device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the cloud platform performance test result analysis method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the cloud platform performance test result analysis method of the first aspect or any corresponding embodiment thereof.

[0055] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which are used to enable a computer to execute the cloud platform performance test result analysis method of the above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 is a flow chart of a method for analyzing cloud platform performance test results according to an embodiment of the present invention;

[0058] Figure 2 is a flow chart of another cloud platform performance test result analysis method according to an embodiment of the present invention;

[0059] Figure 3 It is a schematic diagram of the system architecture of a cloud platform performance test result analysis method according to an embodiment of the present invention;

[0060] Figure 4 A flowchart for analyzing cloud platform performance test results according to an embodiment of the present invention

[0061] Figure 5 is a structural block diagram of a cloud platform performance test result analysis device according to an embodiment of the present invention;

[0062] Figure 6 It is a schematic diagram of the hardware structure of the electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0064] As a relatively popular field in recent years, cloud computing can support users to obtain application services at any location using various terminals. It also supports dynamic scaling to meet the needs of growing application and user scale. As the number of users increases, the application performance provided by cloud computing becomes more and more important. The performance of cloud computing will directly affect the user experience. If the performance is insufficient, the user interface may respond slowly or even fail to work properly during use, thereby causing certain losses to cloud computing providers and related companies. Therefore, performance testing is more important throughout the product life cycle.

[0065] In actual performance testing scenarios, due to considerations such as team costs, the test environment resource pool is basically shared by multiple people. Therefore, the performance testing process of the cloud platform will be interfered by many factors, which will affect the final analysis and judgment of the performance test results.

[0066] On the one hand, due to the influence of the deployment service surrounding components and resource pool environment, including factors such as traffic fluctuations in the environment and component version upgrades, it is generally necessary to conduct multiple performance tests on the test version to eliminate the interference of environmental factors on the performance test results; on the other hand, development code changes may affect the final performance indicators, which requires our attention and identification. This involves how we can better distinguish whether it is interference from environmental factors or real code problems that affect the final performance test data. Therefore, how to quickly and accurately determine whether the test results are credible has become a problem that we need to solve urgently.

[0067] Based on this, an embodiment of the present application provides a method for analyzing the performance test results of a cloud platform, obtaining the current test result obtained by performing a performance test on the cloud platform at the current moment; obtaining the dynamic threshold range of the test result corresponding to the current test result; detecting whether the current test result is within the dynamic threshold range of the test result, thereby ensuring the accuracy of the obtained detection result. If the current test result is within the dynamic threshold range of the test result, it is determined that the current test result is valid and reliable; if the current test result is not within the dynamic threshold range of the test result, it is determined that the current test result is untrustworthy, and a prompt message is output, the prompt message being that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than the preset level. Thus, it is possible to quickly and accurately determine whether the current test result is trustworthy. And when the current test result is untrustworthy, a prompt message is output, so that it is possible to better distinguish whether it is interference from environmental factors or real code problems that affect the final performance test data.

[0068] It should be noted that the method for analyzing the cloud platform performance test results provided in the embodiment of the present application, its execution subject can be a device for analyzing the cloud platform performance test results, and the device for analyzing the cloud platform performance test results can be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware, wherein the electronic device can be a server or a terminal, wherein the server in the embodiment of the present application can be a single server or a server cluster composed of multiple servers, and the terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, a wearable device, and other intelligent hardware devices such as an intelligent robot. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0069] According to an embodiment of the present invention, an embodiment of a method for analyzing performance test results of a cloud platform is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0070] In this embodiment, a cloud platform performance test result analysis method is provided, which can be used for the above-mentioned electronic device. Figure 1 is a flow chart of a method for analyzing cloud platform performance test results according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0071] Step S101, obtaining the current test result obtained by performing performance testing on the cloud platform at the current moment.

[0072] Among them, the current test results include current hard disk read and write speed, current IOPS, current network bandwidth, current latency and other current test data.

[0073] Specifically, the electronic device can determine the preset cloud platform performance testing tool. Among them, the preset cloud platform performance testing tool can be Apache JMeter, LoadRunner, Gatling, New Relic, Datadog, etc. Then, the electronic device sets the test environment according to the preset cloud platform performance testing tool, including defining the test target (such as API endpoint, Web application, etc.), the number of test users, the test duration, the request type (such as GET, POST, etc.), etc. Next, the electronic device starts the performance test and lets the tool simulate a real user to initiate a request to the cloud platform. During the test run, the tool will collect various performance indicators. While the test is running, use the real-time monitoring function provided by the preset cloud platform performance testing tool to observe the test results. These preset cloud platform performance testing tools usually provide the following information: response time (average, minimum, maximum), transaction success rate, server load (CPU, memory, disk I / O), network traffic, error rate, etc. Finally, the electronic device obtains the current test results based on the information obtained from the monitoring.

[0074] Step S102, obtaining a test result dynamic threshold range corresponding to the current test result.

[0075] Specifically, the electronic device may receive a test result dynamic threshold range corresponding to a current test result input by a user, and the electronic device may also set a test result dynamic threshold range corresponding to the current test result based on historical test results before the current moment.

[0076] This step will be described in detail below.

[0077] Step S103, detecting whether the current test result is within the test result dynamic threshold range.

[0078] Specifically, the electronic device compares the current test result with the maximum value and the minimum value in the dynamic threshold range of the test result respectively, and then detects whether the current test result is within the dynamic threshold range of the test result according to the comparison result.

[0079] Step S104: If the current test result is within the test result dynamic threshold range, then the current test result is determined to be valid and confident.

[0080] Specifically, if the current test result is within the dynamic threshold range of the test result, the electronic device determines that the current test result is valid and confident, and then the electronic device determines that the test environment of the cloud platform is normal and the code defect level in the cloud platform is less than or equal to the preset level.

[0081] Step S105: If the current test result is not within the test result dynamic threshold range, the current test result is determined to be untrustworthy, and a prompt message is output.

[0082] The prompt information is that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than a preset level. The preset level can be input by the user to the electronic device, or it can be set by the electronic device according to actual conditions. The embodiment of the present application does not specifically limit the preset level.

[0083] Specifically, if the current test result is not within the test result dynamic threshold range, it is determined that the current test result is untrustworthy, and a prompt message is output.

[0084] The cloud platform performance test result analysis method provided in the embodiment of the present application obtains the current test result obtained by performing a performance test on the cloud platform at the current moment; obtains the dynamic threshold range of the test result corresponding to the current test result; detects whether the current test result is within the dynamic threshold range of the test result, thereby ensuring the accuracy of the obtained detection result. If the current test result is within the dynamic threshold range of the test result, it is determined that the current test result is valid and reliable; if the current test result is not within the dynamic threshold range of the test result, it is determined that the current test result is untrustworthy, and a prompt message is output, the prompt message being that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than the preset level. Thus, it is possible to quickly and accurately determine whether the current test result is reliable. And when the current test result is untrustworthy, a prompt message is output, so that it is possible to better distinguish whether it is interference from environmental factors or real code problems that affect the final performance test data.

[0085] In this embodiment, a cloud platform performance test result analysis method is provided, which can be used for the above-mentioned electronic device. Figure 2 is a flow chart of a method for analyzing cloud platform performance test results according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0086] Step S201, obtaining the current test result obtained by performing performance testing on the cloud platform at the current moment.

[0087] For details about this step, please refer to the above description of step S101, which will not be elaborated here.

[0088] Step S202, obtaining a test result dynamic threshold range corresponding to the current test result.

[0089] Specifically, the above step S202 may include the following steps:

[0090] Step S2021, obtaining a first historical test result obtained by performing a performance test on the cloud platform at a previous moment corresponding to the current moment.

[0091] Specifically, the electronic device may search in the storage space for a first historical test result obtained by performing a performance test on the cloud platform at a previous moment corresponding to the current moment.

[0092] The first historical test result includes first historical test data such as historical hard disk read and write speed, historical IOPS, historical network bandwidth, and historical delay.

[0093] Step S2022, obtaining the target weight corresponding to the first historical test result.

[0094] Specifically, the above step S2022 may include the following steps:

[0095] Step a1, obtaining multiple second historical test results obtained by performing multiple performance tests on the cloud platform within a preset time period before the last moment.

[0096] Specifically, the electronic device can search in the storage space for multiple second historical test results obtained by performing multiple performance tests on the cloud platform within a preset time period before the last moment.

[0097] Exemplarily, the electronic device may obtain from the storage space a plurality of second historical test results corresponding to each final test version of the cloud platform within a preset time period before the last moment.

[0098] Among them, the number of the second historical test results can be 10, or 8, or other values. The embodiment of the present application does not specifically limit the number of the second historical test results.

[0099] Step a2, obtaining multiple initial weights.

[0100] Specifically, the electronic device may also receive multiple initial weights input by a user, or may receive multiple initial weights sent by other devices.

[0101] Exemplarily, the multiple initial weights may be obtained from 0 to 1, for example, the multiple initial weights may be 0.2, 0.5, and 0.8 respectively.

[0102] Step a3, for each second historical test result, according to the relationship between each initial weight and the second historical test result, each second predicted test result at the historical moment corresponding to the second historical test result is predicted in sequence.

[0103] Among them, each second prediction test result corresponding to the second historical test result corresponds to each initial weight.

[0104] Specifically, for each second historical test result, the electronic device predicts each second predicted test result at a historical moment corresponding to the second historical test result in sequence according to the relationship between each initial weight and the second historical test result and according to the exponential smoothing model.

[0105] Among them, the exponential smoothing model can be a simple exponential smoothing (SES) model, a Holt linear trend exponential smoothing (Holt) model, or a Holt-Winters exponential smoothing (Holt-Winters) model. The embodiment of the present application does not specifically limit the exponential smoothing model.

[0106] Exemplarily, for each second historical test result, the electronic device predicts the second predicted test result at the next moment corresponding to the second historical test result according to the exponential smoothing model based on the relationship between the first initial weight and the second historical test result, and the second predicted test result obtained by this prediction corresponds to the first initial weight. Then, the electronic device predicts the second predicted test result at the next moment corresponding to the second historical test result according to the exponential smoothing model based on the relationship between the second initial weight and the second historical test result, and the second predicted test result obtained by this prediction corresponds to the second initial weight, and so on.

[0107] In an optional implementation of the present application, the electronic device may determine the average of the first second historical test result and the second second historical test result arranged in chronological order as the second predicted test result of the historical time corresponding to the first second historical test result. Then, the electronic device predicts the second predicted test result corresponding to each initial weight of the historical time corresponding to the second second historical test result based on the relationship between the first second historical test result and each initial weight. The electronic device predicts the second predicted test result corresponding to each initial weight of the historical time corresponding to the third second historical test result based on the relationship between the second second historical test result and each initial weight, and so on.

[0108] For example, assume that the number of second historical test results is 8, and the corresponding historical times are t1-t8. The electronic device can calculate the average value of the second historical test results corresponding to the historical time t1 and the historical time t2, and use the calculated results as the second predicted test results corresponding to each initial weight at the historical time t1. Then, for the second historical test results corresponding to the historical time t1, according to the relationship between each initial weight and the second historical test results corresponding to the historical time t1, the second predicted test results corresponding to each initial weight at the historical time t2 are predicted in sequence. For the second historical test results corresponding to the historical time t2, according to the relationship between each initial weight and the second historical test results corresponding to the historical time t2, the second predicted test results corresponding to each initial weight at the historical time t3 are predicted in sequence, and so on.

[0109] Step a4: for each initial weight, obtain each second predicted test result obtained by predicting each second historical test result based on the initial weight.

[0110] Specifically, for each initial weight, the electronic device may obtain each second predicted test result obtained by predicting each second historical test result based on the initial weight.

[0111] Exemplarily, for the first initial weight, the electronic device obtains each second predicted test result based on the first initial weight prediction of each second historical test result; for the second initial weight, the electronic device obtains each second predicted test result based on the second initial weight prediction of each second historical test result, and so on.

[0112] Step a5, for each initial weight, according to the relationship between each second prediction test result corresponding to the initial weight and each second historical test result, determine the standard error corresponding to the initial weight.

[0113] Specifically, the above step a5 may include the following steps:

[0114] Step a51, for each initial weight, calculating the difference between each second prediction test result and the corresponding second historical test result.

[0115] Specifically, for each initial weight, the electronic device calculates the difference between each second prediction test result at the corresponding historical time and the corresponding second historical test result.

[0116] Exemplarily, the electronic device calculates the difference between the second predicted test result corresponding to historical time t1 and the second historical test result; calculates the difference between the second predicted test result corresponding to historical time t2 and the second historical test result; calculates the difference between the second predicted test result corresponding to historical time t3 and the second historical test result; and so on.

[0117] Step a52, for each initial weight, calculate the standard error corresponding to the initial weight according to the difference between each second prediction test result and the corresponding second historical test result.

[0118] Specifically, for each initial weight, the electronic device squares the difference between the second predicted test result at each historical time and the corresponding second historical test result, and then calculates the average, and then performs square root calculation on the calculated average to obtain the standard error corresponding to the initial weight.

[0119] Exemplarily, the electronic device calculates the standard error corresponding to the initial weight based on the following formula:

[0120]

[0121] Among them, the actual value of y1 represents the second historical test result corresponding to the historical time t1, the predicted value of y1 represents the second predicted test result corresponding to the historical time t1, and so on.

[0122] Step a6, determining the initial weight with the smallest standard error from among the initial weights as the target weight.

[0123] Specifically, the electronic device compares the standard errors corresponding to the initial weights, and then determines the initial weight with the smallest standard error from the initial weights as the target weight.

[0124] Step S2023, based on the target weight and the relationship between the first historical test result, predict the current predicted test result corresponding to the cloud platform at the current moment.

[0125] Specifically, the electronic device can predict the current predicted test result corresponding to the cloud platform at the current moment based on the exponential smoothing model based on the relationship between the target weight and the first historical test result.

[0126] Among them, the exponential smoothing model can be a simple exponential smoothing (SES) model, a Holt linear trend exponential smoothing (Holt) model, or a Holt-Winters exponential smoothing (Holt-Winters) model. The embodiment of the present application does not specifically limit the exponential smoothing model.

[0127] Specifically, the above step S2023 may include the following steps:

[0128] Step b1, obtaining the first prediction test result at the previous moment corresponding to the current moment of the target weight.

[0129] Specifically, the electronic device can predict the first predicted test result at the previous moment of the current moment corresponding to the target weight based on the relationship between the target weight and the second historical test result at the previous moment corresponding to the previous moment.

[0130] Step b2, multiplying the difference between the first historical test result and the first predicted test result by the target weight, and then adding the first predicted test result to obtain the current predicted test result.

[0131] Specifically, the electronic device multiplies the difference between the first historical test result and the first predicted test result by the target weight, and then adds the first predicted test result to obtain the current predicted test result.

[0132] Exemplarily, the electronic device may predict the current prediction test result based on the following formula:

[0133] y predicted value (t+1) = ay actual value (t) + (1-α)y predicted value (t)y predicted value (t) + α (y actual value (t)y predicted value (t))

[0134] Among them, y 预测值 (t+1) represents the current prediction test result, α represents the target weight, y 预测值 (t) represents the first prediction test result of the previous moment of the current moment; y 实际值 (t) represents the first historical test result at the previous moment of the current moment.

[0135] Step S2024, generating a dynamic threshold range of test results based on the current predicted test results.

[0136] Specifically, the above step S2024 may include the following steps:

[0137] Step c1, calculating the difference between each second prediction test result corresponding to the target weight and the corresponding second historical test result.

[0138] Specifically, according to the above implementation, the electronic device can calculate the difference between each second predicted test result and the corresponding second historical test result for each initial weight, and determine the difference between each second predicted test result corresponding to the target weight and the corresponding second historical test result.

[0139] Step c2, calculating the average error according to the difference between each second prediction test result corresponding to the target weight and the corresponding second historical test result.

[0140] Specifically, the electronic device may add the differences between each second predicted test result corresponding to the target weight and the corresponding second historical test result, and then average them to calculate the average error.

[0141] Step c3, using the current predicted test result minus the average error as the minimum value in the dynamic threshold range of the test result.

[0142] Specifically, the electronic device subtracts the average error from the current predicted test result as the minimum value in the dynamic threshold range of the test result.

[0143] Step c4, using the current predicted test result plus the average error as the maximum value in the dynamic threshold range of the test result.

[0144] Specifically, the electronic device adds the average error to the current predicted test result as the maximum value in the dynamic threshold range of the test result.

[0145] Then, the electronic device obtains the test result dynamic threshold range according to the maximum value and the minimum value corresponding to the test result dynamic threshold range.

[0146] Step S203, detecting whether the current test result is within the test result dynamic threshold range.

[0147] Regarding this step, please refer to the above description of step S103 and will not be repeated here.

[0148] Step S204: If the current test result is within the test result dynamic threshold range, then the current test result is determined to be valid and confident.

[0149] Regarding this step, please refer to the above description of step S104 and will not be repeated here.

[0150] Step S205, obtaining a first historical test result obtained by performing a performance test on the cloud platform at a previous moment corresponding to the current moment.

[0151] Specifically, the electronic device may search the storage space for a first historical test result obtained by performing a performance test on the cloud platform at a previous moment corresponding to the current moment.

[0152] Step S206: compare the first historical test result with the current test result.

[0153] Specifically, the electronic device may compare the first historical test result with the current test result.

[0154] Exemplarily, the electronic device can compare the historical hard disk read and write speed, historical IOPS, historical network bandwidth, and historical delay in the first historical test result with the current hard disk read and write speed, current IOPS, current network bandwidth, and current delay in the current test result.

[0155] Step S207: if the first historical test result is better than the current test result, output the problem to be optimized according to the comparison result.

[0156] Specifically, if the first historical test result is better than the current test result, the electronic device outputs the problem to be optimized according to the comparison result.

[0157] Exemplarily, if the historical hard disk read / write speed in the first historical test result is better than the current hard disk read / write speed in the current test result, the electronic device outputs the problem to be optimized as the hard disk read / write speed problem.

[0158] Step S208, if the current test result is not within the dynamic threshold range of the test result, the current test result is determined to be untrustworthy, and a prompt message is output, the prompt message being that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than a preset level.

[0159] For details about this step, please refer to the above description of step S105, which will not be elaborated here.

[0160] The cloud platform performance test result analysis method provided in the embodiment of the present application obtains the first historical test result obtained by performing a performance test on the cloud platform at the last moment corresponding to the current moment. Obtain multiple second historical test results obtained by performing multiple performance tests on the cloud platform within a preset time before the last moment; obtain multiple initial weights; for each second historical test result, according to the relationship between each initial weight and the second historical test result, predict each second predicted test result at the historical moment corresponding to the second historical test result in sequence, thereby ensuring the accuracy of each second predicted test result obtained, and ensuring that each second predicted test result obtained corresponds to each initial weight. Then, for each initial weight, obtain each second predicted test result obtained based on the initial weight prediction of each second historical test result. For each initial weight, calculate the difference between each second predicted test result and the corresponding second historical test result; for each initial weight, calculate the standard error corresponding to the initial weight according to the difference between each second predicted test result and the corresponding second historical test result, thereby ensuring the accuracy of the standard error obtained. Determine the initial weight with the smallest standard error from each initial weight as the target weight, thereby ensuring the accuracy of the determined target weight. In addition, the accuracy of the current predicted test result predicted according to the target weight can be guaranteed.

[0161] Obtain the first prediction test result of the previous moment of the current moment corresponding to the target weight; multiply the difference between the first historical test result and the first prediction test result by the target weight, and then add the first prediction test result to obtain the current prediction test result. The accuracy of the current prediction test result corresponding to the cloud platform at the current moment is guaranteed. Calculate the difference between each second prediction test result corresponding to the target weight and the corresponding second historical test result, and calculate the average error based on the difference between each second prediction test result corresponding to the target weight and the corresponding second historical test result, thereby ensuring the accuracy of the calculated average error. Use the current prediction test result minus the average error as the minimum value in the dynamic threshold range of the test result; use the current prediction test result plus the average error as the maximum value in the dynamic threshold range of the test result, thereby ensuring the accuracy of the obtained dynamic threshold range of the test result. Then, it is possible to detect whether the current test result is reliable based on the dynamic threshold range of the test result.

[0162] In addition, after determining that the current test result is valid and reliable, the first historical test result obtained by performing a performance test on the cloud platform at the previous moment corresponding to the current moment is obtained, and the first historical test result is compared with the current test result; thereby, the performance corresponding to the cloud platform at the current moment can be judged. If the first historical test result is better than the current test result, the problem to be optimized is output according to the comparison result, ensuring the accuracy of the output problem to be optimized, so that the cloud platform can be optimized based on the problem to be optimized.

[0163] In order to better introduce the cloud platform performance test result analysis method provided in the embodiment of the present application, the present application provides a specific implementation method. Figure 3 As shown in Figure 1, it is a schematic diagram of the system architecture of the cloud platform performance test result analysis method. Figure 4 The figure shows the cloud platform performance test result analysis process.

[0164] Here we take the cloud platform disk performance test as an example to list the actual usage process:

[0165] Step 1: Receive performance test script from user input.

[0166] Step 2: Use a performance testing tool or platform to run the above performance testing script to perform a performance test on the disk of the cloud host in the cloud platform.

[0167] Step 3: Get the disk performance test data, including read and write speed, IOPS, network bandwidth, and latency.

[0168] Step 4: Get historical data, about 8 sets of data.

[0169] Step 5: Determine the initial value. The average value of the first and second sets of data is used as the initial data, that is, the predicted data corresponding to each initial weight corresponding to the first set of data, as shown in Table 1 below. The initial value can be set to (98+99) / 2=98.5.

[0170] Step 6: Using different initial weights α = 0.2 / 0.5 / 0.8, the predicted values ​​of the eight sets of data under different weights are calculated using the exponential smoothing method, as shown in the following table:

[0171] Table 1 Delayed actual values ​​and exponential smoothing predicted values

[0172] Serial number Actual delay value (ms) Weight = 0.2 Weight = 0.5 Weight = 0.8 1 99 98.5 98.5 98.5 2 98 98.6 98.75 98.9 3 100 98.48 98.38 98.18 4 101 98.78 99.19 99.64 5 92 99.23 100.1 100.73 6 107 97.78 96.05 93.75 7 96 99.63 101.52 104.35 8 105 98.9 98.76 97.67

[0173] The standard errors between different predicted values ​​and true values ​​under each initial weight are calculated as shown in Table 2. The initial weight with the smallest standard error is selected as the target weight value used in the end, so the final target weight is 0.2.

[0174] Table 2 Standard error of predicted values

[0175] Weight Weight = 0.2 Weight = 0.5 Weight = 0.8 Standard error 4.9436 5.7188 6.9052

[0176] Step 7: Use the above selected target weight 0.2 combined with the exponential smoothing method to calculate the predicted value y of the performance test data.

[0177] Step 8: Store the above predicted values ​​into the data collection module.

[0178] Step 9: Based on the selected target weight value, the average error between the predicted value and the true value is 3.875.

[0179] Step 10: Based on the predicted value in step 7 and the average error in step 8, the dynamic threshold range of the predicted value is [y-3.875, y+3.875].

[0180] Step 11: Analyze and process the actual value of the performance test result of step 3 and the dynamic threshold range of the predicted value of step 10.

[0181] If the actual value of the performance test result in step 3 is within the dynamic threshold range of the predicted value in step 10, it means that the performance test result is credible and the result can be analyzed in the next step. If the performance test result in step 3 is worse than the previous performance test result in various indicators, it means that the performance of this version needs to be optimized. This needs to be analyzed in combination with the actual situation, including but not limited to optimizing code logic, optimizing database query time, increasing cache, etc.

[0182] If the actual value of the performance test result in step 3 is not within the dynamic threshold range of the predicted value in step 10, it means that the performance test result is unreliable. There may be problems with the environment or serious defects in the code. You need to troubleshoot first, then repeat steps 2 and 3, and then perform comparative analysis in step 11.

[0183] Step 12: After the analysis and processing is completed, the final actual performance test results need to be stored in the data collection module.

[0184] In this embodiment, a cloud platform performance test result analysis device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0185] This embodiment provides a cloud platform performance test result analysis device, such as Figure 5 As shown, including:

[0186] The first acquisition module 301 is used to obtain the current test result obtained by performing a performance test on the cloud platform at the current moment;

[0187] The second acquisition module 302 is used to obtain the test result dynamic threshold range corresponding to the current test result;

[0188] A detection module 303 is used to detect whether the current test result is within the test result dynamic threshold range;

[0189] A first determination module 304 is used to determine that the current test result is valid confidence if the current test result is within the test result dynamic threshold range;

[0190] The second determination module 305 is used to determine that the current test result is untrustworthy if the current test result is not within the dynamic threshold range of the test result, and output a prompt message, wherein the prompt message is that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than a preset level.

[0191] In some optional embodiments, the second acquisition module 302 is specifically used to obtain a first historical test result obtained by performing a performance test on the cloud platform at the previous moment corresponding to the current moment; obtain a target weight corresponding to the first historical test result; based on the relationship between the target weight and the first historical test result, predict the current predicted test result corresponding to the cloud platform at the current moment; based on the current predicted test result, generate a dynamic threshold range for the test result.

[0192] In some optional embodiments, the second acquisition module 302 is specifically used to obtain multiple second historical test results obtained by performing multiple performance tests on the cloud platform within a preset time period before the previous moment; obtain multiple initial weights; for each second historical test result, based on the relationship between each initial weight and the second historical test result, predict the second predicted test results of the historical moment corresponding to the second historical test result in sequence; wherein, each second predicted test result corresponding to the second historical test result corresponds to each initial weight; for each initial weight, obtain each second predicted test result obtained by predicting each second historical test result based on the initial weight; for each initial weight, determine the standard error corresponding to the initial weight based on the relationship between each second predicted test result corresponding to the initial weight and each second historical test result; and determine the initial weight with the smallest standard error from each initial weight as the target weight.

[0193] In some optional embodiments, the second acquisition module 302 is specifically used to calculate, for each initial weight, the difference between each second predicted test result and the corresponding second historical test result; for each initial weight, based on the difference between each second predicted test result and the corresponding second historical test result, calculate the standard error corresponding to the initial weight.

[0194] In some optional embodiments, the second acquisition module 302 is specifically used to obtain the first prediction test result at the previous moment of the current moment corresponding to the target weight; subtract the first prediction test result from the first historical test result and multiply it by the target weight, and then add the first prediction test result to obtain the current prediction test result.

[0195] In some optional embodiments, the second acquisition module 302 is specifically used to calculate the difference between each second predicted test result corresponding to the target weight and the corresponding second historical test result; calculate the average error based on the difference between each second predicted test result corresponding to the target weight and the corresponding second historical test result; use the current predicted test result minus the average error as the minimum value in the dynamic threshold range of the test result; use the current predicted test result plus the average error as the maximum value in the dynamic threshold range of the test result.

[0196] In some optional implementations, the first determination module 304 is also used to obtain a first historical test result obtained by performing a performance test on the cloud platform at the previous moment corresponding to the current moment; compare the first historical test result with the current test result; if the first historical test result is better than the current test result, output the problem to be optimized according to the comparison result.

[0197] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0198] The cloud platform performance test result analysis device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0199] The embodiment of the present invention also provides an electronic device having the above Figure 5 The cloud platform performance test result analysis device shown.

[0200] See also Figure 6 , Figure 6 is a schematic diagram of the structure of an electronic device provided by an optional embodiment of the present invention, such as Figure 6As shown, the electronic device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0201] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0202] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0203] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0204] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0205] The electronic device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0206] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0207] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0208] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0209] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A cloud platform performance test result analysis method, characterized in that: The method comprises: Get the current test results of the performance test on the cloud platform at the current moment; Obtaining a test result dynamic threshold range corresponding to the current test result; Detecting whether the current test result is within the test result dynamic threshold range; If the current test result is within the test result dynamic threshold range, determining that the current test result is valid and confident; If the current test result is not within the dynamic threshold range of the test result, the current test result is determined to be untrustworthy, and a prompt message is output, wherein the prompt message is that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than a preset level.

2. The method according to claim 1, characterized in that The obtaining the dynamic threshold range of the test result corresponding to the current test result includes: Obtaining a first historical test result obtained by performing a performance test on the cloud platform at a previous moment corresponding to the current moment; Obtaining a target weight corresponding to the first historical test result; Based on the relationship between the target weight and the first historical test result, predicting a current predicted test result of the cloud platform corresponding to the current moment; The test result dynamic threshold range is generated based on the current predicted test result.

3. The method according to claim 2, characterized in that The obtaining the target weight corresponding to the first historical test result includes: Acquire multiple second historical test results obtained by performing multiple performance tests on the cloud platform within a preset time period before the last moment; Get multiple initial weights; For each of the second historical test results, according to the relationship between each of the initial weights and the second historical test results, each of the second predicted test results at the historical moment corresponding to the second historical test result is predicted in sequence; wherein each of the second predicted test results corresponding to the second historical test result corresponds to each of the initial weights; For each of the initial weights, obtaining each of the second predicted test results obtained by predicting each of the second historical test results based on the initial weights; For each of the initial weights, determining a standard error corresponding to the initial weight according to a relationship between each of the second prediction test results corresponding to the initial weight and each of the second historical test results; The initial weight with the smallest standard error is determined from among the initial weights as the target weight.

4. The method according to claim 3, characterized in that The determining, for each of the initial weights, a standard error corresponding to the initial weight according to a relationship between each of the second prediction test results corresponding to the initial weight and each of the second historical test results, includes: For each of the initial weights, calculating the difference between each of the second prediction test results and the corresponding second historical test result; For each of the initial weights, a standard error corresponding to the initial weight is calculated according to a difference between each of the second prediction test results and the corresponding second historical test results.

5. The method according to claim 2, characterized in that: The predicting, based on the relationship between the target weight and the first historical test result, the current predicted test result corresponding to the cloud platform at the current moment, includes: Obtaining a first prediction test result at a previous moment of the current moment corresponding to the target weight; The current prediction test result is obtained by subtracting the first prediction test result from the first historical test result and multiplying the result by the target weight. Then, the target weight is added to the first prediction test result.

6. The method according to claim 4, characterized in that The generating the test result dynamic threshold range based on the current predicted test result includes: Calculating the difference between each of the second prediction test results corresponding to the target weight and the corresponding second historical test result; Calculate an average error according to the difference between each second prediction test result corresponding to the target weight and the corresponding second historical test result; Subtracting the average error from the current predicted test result to obtain the minimum value in the dynamic threshold range of the test result; The current predicted test result plus the average error is used as the maximum value in the dynamic threshold range of the test result.

7. The method according to claim 1, characterized in that After determining that the current test result is valid and confident, the method further includes: Obtaining a first historical test result obtained by performing a performance test on the cloud platform at a previous moment corresponding to the current moment; comparing the first historical test result with the current test result; If the first historical test result is better than the current test result, the problem to be optimized is output according to the comparison result.

8. A cloud platform performance test result analysis device, characterized in that: The device comprises: The first acquisition module is used to obtain the current test result obtained by performing a performance test on the cloud platform at the current moment; A second acquisition module is used to obtain a dynamic threshold range of a test result corresponding to the current test result; A detection module, used to detect whether the current test result is within the test result dynamic threshold range; A first determination module, configured to determine that the current test result is valid confidence if the current test result is within the test result dynamic threshold range; The second determination module is used to determine that the current test result is untrustworthy if the current test result is not within the dynamic threshold range of the test result, and output a prompt message, wherein the prompt message is that there is a problem with the test environment of the cloud platform and / or the code defect level in the cloud platform is greater than a preset level.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the cloud platform performance test result analysis method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the cloud platform performance test result analysis method according to any one of claims 1 to 7.

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