Noise reduction method for interface performance test index data and related device

Abstract

The invention provides an interface performance test index data noise reduction method and a related device, and relates to the technical field of performance testing, and the method comprises the steps: obtaining a first sample matrix of Web interface performance test index data through data enhancement; performing singular value decomposition on the first sample matrix to obtain a singular value matrix, and performing noise reduction on the singular value matrix; and reconstructing and recovering a second sample matrix of the Web interface performance test index data by using the singular value matrix after noise reduction. According to the method, the influence of noise in the performance test index data on the performance analysis process can be effectively reduced, by suppressing the noise, on one hand, performance defects in the performance test index data can be more obviously exposed, the accuracy of performance analysis is improved, on the other hand, the analysis direction can be accurately found, the performance defect checking speed is increased, and the performance analysis efficiency is improved. And the performance analysis efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of performance testing, and in particular to a method for reducing noise of interface performance testing index data and a related device. Background Art

[0002] Web interfaces often expose performance issues when they receive high concurrent requests or run for a long time. During the software development and testing process, for interfaces with high estimated visit volume, performance testing needs to be performed on the basis of implementing functions.

[0003] The general Web interface performance test process includes: demand analysis, test preparation, stress testing and monitoring, test analysis and summary, etc. Performance test analysis is generally carried out after the stress testing is completed. The performance indicator data (such as response time, TPS, server load, etc.) generated during the stress testing process and the performance pass criteria are used to determine whether the current system performance meets the requirements and confirm whether the system has performance defects. Whether it is manual analysis or statistical analysis, the impact of noise in the performance test indicator data on the analysis results is not considered.

[0004] Therefore, how to reduce the noise of performance test indicator data before performance analysis has become an urgent problem to be solved at this stage. Summary of the invention

[0005] In view of the above problems, the present application provides a method and a related device for reducing the noise of interface performance test index data to achieve the purpose of reducing the noise of performance test index data before performance analysis. The specific scheme is as follows:

[0006] A first aspect of the present application provides a method for reducing noise of interface performance test indicator data, the method for reducing noise of interface performance test indicator data comprising:

[0007] Obtain the first sample matrix of Web interface performance test indicator data through data enhancement;

[0008] Performing singular value decomposition on the first sample matrix to obtain a singular value matrix, and performing noise reduction on the singular value matrix;

[0009] The denoised singular value matrix is ​​used to reconstruct and restore a second sample matrix of the Web interface performance test index data.

[0010] In a possible implementation, the step of obtaining a first sample matrix of Web interface performance test indicator data through data enhancement includes:

[0011] Acquire the plurality of first original data samples in a repeated testing manner, and form the first sample matrix with the plurality of first original data samples; or,

[0012] A single second original data sample is acquired in a single test manner; and the second original data sample is locally sampled multiple times to obtain the first sample matrix.

[0013] In a possible implementation, performing singular value decomposition on the first sample matrix to obtain a singular value matrix includes:

[0014] Normalizing the first sample matrix;

[0015] Calculating a covariance matrix and the auxiliary representation parameter matrix according to the normalized first sample matrix;

[0016] extracting a first eigenvector matrix from the covariance matrix, and extracting a second eigenvector matrix from the auxiliary representation parameter matrix;

[0017] The singular value matrix is ​​calculated using the first eigenvector matrix, the second eigenvector matrix, and the normalized sample matrix.

[0018] In a possible implementation, the step of reconstructing and restoring the second sample matrix of the Web interface performance test index data using the denoised singular value matrix includes:

[0019] If the first sample matrix is ​​obtained by repeated testing, calculating a first intermediate sample matrix using the first eigenvector matrix, the second eigenvector matrix and the denoised singular value matrix;

[0020] The first intermediate sample matrix is ​​inversely normalized to reconstruct the second sample matrix.

[0021] In a possible implementation, the step of reconstructing and restoring the second sample matrix of the Web interface performance test index data using the denoised singular value matrix includes:

[0022] If the first sample matrix is ​​obtained through multiple local samplings, a second intermediate sample matrix is ​​calculated using the first eigenvector matrix, the second eigenvector matrix and the denoised singular value matrix;

[0023] performing inverse normalization on the second intermediate sample matrix;

[0024] The samples with the same subscript in the second intermediate sample matrix after inverse normalization are averaged to restore the second sample matrix.

[0025] A second aspect of the present application provides a device for reducing noise of interface performance test index data, the device for reducing noise of interface performance test index data comprising:

[0026] A data enhancement module, used for obtaining a first sample matrix of Web interface performance test indicator data through data enhancement;

[0027] A singular decomposition and noise reduction module, used for performing singular value decomposition on the first sample matrix to obtain a singular value matrix, and performing noise reduction on the singular value matrix;

[0028] The reconstruction and recovery module is used to reconstruct and restore the second sample matrix of the Web interface performance test index data using the denoised singular value matrix.

[0029] In a possible implementation, the data enhancement module is specifically used to:

[0030] Acquire the plurality of first original data samples in a repeated testing manner, and form the first sample matrix with the plurality of first original data samples; or,

[0031] A single second original data sample is acquired in a single test manner; and the second original data sample is locally sampled multiple times to obtain the first sample matrix.

[0032] The third aspect of the present application provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the method for reducing the noise of interface performance test index data of the first aspect or any implementation method of the first aspect.

[0033] A fourth aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0034] The memory is used to store computer programs;

[0035] The processor is used to execute the computer program so that the electronic device can implement the method for reducing noise of interface performance test index data of the above-mentioned first aspect or any implementation manner of the first aspect.

[0036] The fifth aspect of the present application provides a computer storage medium, characterized in that the storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the noise reduction method for interface performance test indicator data of the above-mentioned first aspect or any implementation method of the first aspect.

[0037] By means of the above technical scheme, the present application provides a method and related device for reducing the noise of interface performance test index data, obtains the first sample matrix of Web interface performance test index data through data enhancement; performs singular value decomposition on the first sample matrix to obtain a singular value matrix, and reduces the noise of the singular value matrix; uses the denoised singular value matrix to reconstruct and restore the second sample matrix of Web interface performance test index data. The present application can effectively reduce the impact of noise in performance test index data on the performance analysis process. By suppressing noise, on the one hand, it helps to more clearly expose the performance defects in the performance test index data and improve the accuracy of performance analysis. On the other hand, it helps to find the analysis direction, speed up the troubleshooting of performance defects, and improve the efficiency of performance analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0039] Figure 1 A flowchart of a method for reducing noise of interface performance test index data provided in an embodiment of the present application;

[0040] Figure 2 A partial flow chart of a method for reducing noise of interface performance test index data provided in an embodiment of the present application;

[0041] Figure 3 A schematic diagram of another part of the flow chart of a method for reducing noise of interface performance test index data provided in an embodiment of the present application;

[0042] Figure 4 A schematic diagram of another part of the flow chart of a method for reducing noise of interface performance test index data provided in an embodiment of the present application;

[0043] Figure 5 A schematic diagram of the structure of a device for reducing noise of interface performance test index data provided in an embodiment of the present application;

[0044] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0046] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0047] The terms "first", "second" etc. in the specification of the application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable in appropriate circumstances, and this is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0048] To facilitate understanding of this application, the following first describes the relevant concepts involved in this application:

[0049] Performance testing: On the basis of realizing functions, use tools to simulate a certain number of users sending a large number of requests to the Web interface within a certain period of time to test whether the system meets performance requirements and verify whether the system can run persistently and stably.

[0050] Request response time: The time interval from when the client sends a request to the server to when the server receives the request.

[0051] TPS (Transactions Per Second): The number of user requests that the system can handle per unit time.

[0052] Singular value decomposition: It is a matrix factorization method. Any non-zero m×n real matrix can be expressed as the product of three matrices, namely, an m-order standard orthogonal matrix, an m×n diagonal matrix composed of non-negative diagonal elements arranged in descending order, and an n-order standard orthogonal matrix.

[0053] Data augmentation: refers to the method of expanding data. There are two data augmentation methods in this application, one is repeated testing, and the other is multiple local sampling.

[0054] Noise: The noise mentioned in this application specifically refers to Gaussian white noise, whose energy projection on any unit orthogonal matrix is ​​uniform, which is the theoretical premise for this application to achieve noise reduction.

[0055] When a web interface receives high concurrent requests or runs for a long time, it often exposes performance problems, such as slow response time, excessive server load, or even downtime. During the software development and testing process, for interfaces with high estimated visit volume, it is necessary to conduct performance testing on the basis of implementing functions, that is, using tools to simulate a certain number of users sending a large number of requests to the interface within a certain period of time, so as to promptly discover performance defects in the software and enable the system to run persistently and stably.

[0056] The general Web interface performance test process includes: demand analysis, test preparation, stress and monitoring, test analysis and summary. In the demand analysis stage, it is necessary to clarify the performance requirements of the project, determine the performance requirements from multiple dimensions such as the number of users, average daily transaction volume, peak transaction volume, response time, system resources, and set performance passing criteria. The criteria generally include three parts: TPS that the system should support, interface response time, and server resource conditions. In the test preparation stage, it is necessary to determine the stress tool and resource monitoring tool; after determining the stress tool, it is necessary to prepare test data and test scripts, and design test scenarios according to requirements. After the test preparation stage is completed, the stress and monitoring stage is carried out. In this stage, the stress tool simulates the user sending requests to the interface according to the test scenario, and records the number of requests processed per second by the interface and the response time of the request. At the same time, the resource monitoring tool records the server CPU, memory, network traffic and other resource conditions during the stress process. After the stress is completed, the test analysis and summary stage is carried out. Combined with the performance passing criteria and performance test indicator data, it is judged whether the performance of the current interface meets the performance requirements. If not, the cause of the defect needs to be investigated and repaired.

[0057] Performance test analysis is generally carried out after the stress test is completed. The performance indicator data (such as response time, TPS, server load, etc.) generated during the stress test is used together with the performance criteria to determine whether the performance of the current system meets the requirements and confirm whether the system has performance defects. Common analysis methods include: 1. Use tools to draw the indicator data into a performance curve. Testers manually observe the performance curve to determine whether the performance test results meet the requirements. For example, the stability of the interface can be determined based on whether the interface response time curve has a climbing trend, whether there are high peaks, and whether the fluctuation range is too large; 2. Perform statistical analysis on the indicator data. Testers determine whether the performance indicators meet the requirements based on the statistical indicators and preset criteria. Common statistical indicators include: the average, median, 90% quantile, maximum, minimum, standard deviation of the response time, the average and standard deviation of TPS, and the average and standard deviation of the server load monitoring items.

[0058] Whether it is manual analysis or statistical analysis, the impact of noise in performance test indicator data on the analysis results is not considered. On the one hand, noise may cover up the performance defects hidden in the indicator data, resulting in inaccurate performance test conclusions; on the other hand, noise may interfere with the performance analysis process, leading to the wrong analysis direction, prolonging the defect troubleshooting time, and thus affecting the progress of software development. In summary, noise reduction of performance test indicator data is a very necessary process before performance analysis.

[0059] In order to solve the above problems, the embodiment of the present application provides a method for reducing the noise of interface performance test index data. The method for reducing the noise of interface performance test index data of the embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0060] See also Figure 1 , Figure 1 A flowchart of a method for reducing the noise of interface performance test index data provided by an embodiment of the present application. Figure 1 As shown, a method for reducing noise of interface performance test index data provided in an embodiment of the present application may include steps S10 to S30, and these steps are described in detail below.

[0061] S10, obtaining a first sample matrix of Web interface performance test indicator data through data enhancement.

[0062] In an embodiment of the present application, Web interface performance test indicator data can be collected, such as transaction response time, TPS, server CPU utilization, server memory utilization and other indicator data during the performance test, and then the corresponding sample matrix, i.e., the first sample matrix, is obtained through data enhancement.

[0063] The web interface performance test index data is generated by the stress tester and the test server during the stress test. The index data generated on the stress tester easily reflect performance problems, such as request response time and TPS. Among them, the request response time refers to the time from the stress tester sending a request to the server to receiving the response to the request. It is generally measured in time units, usually seconds (s) or milliseconds (ms); TPS refers to the number of user requests processed by the system per unit time, and its unit is generally (per second). Data collection on the stress tester side can generally be completed using stress test tools. The index data generated on the test server include CPU usage, memory usage, etc. Its data collection method can be obtained through system built-in commands (such as top command), monitoring software, etc.

[0064] It should be noted that the Web interface performance test index data in the embodiment of the present application refers to the recorded value of a certain index within a certain period of time, which is mathematically expressed using a one-dimensional vector.

[0065] Specifically, the first sample matrix can be obtained by repeated testing or multiple local sampling. In this regard, the embodiment of the present application provides a method for reducing the noise of interface performance test index data, wherein step S10 "obtaining the first sample matrix of Web interface performance test index data through data enhancement" can adopt the following steps:

[0066] Acquire multiple first original data samples in a repeated testing manner, and form a first sample matrix with the multiple first original data samples; or,

[0067] A single second original data sample is obtained in a single test manner; and a first sample matrix is ​​obtained by performing multiple local sampling on the second original data sample.

[0068] In the embodiment of the present application, the data enhancement method for expanding data includes repeated testing and multiple local sampling. Specifically:

[0069] 1) Obtain multiple original data samples (i.e., first original data samples) by repeated testing, and use the multiple first original data samples to form a first sample matrix X = [x1, x2, ..., x I ], where x i It represents the sample obtained in the i-th test, with a length of N, and I represents the total number of repeated tests. This method requires sufficient test time and test resources, and the test object has the possibility of repeated testing.

[0070] 2) Obtain a single original data sample (i.e., the second original data sample) in a single test, and perform multiple local sampling on the second original data sample to obtain the first sample matrix. When the test time and test resources are insufficient, or the test object cannot be tested repeatedly, this method can be used to perform data enhancement on the single test result. For example, if the second original data sample is x=[x1,x2,...x, M ], a local sampling method is x i =[x i ,x i+1 ,...,x i-1+N ], where x i represents the sample after the i-th local sampling, and N represents the sample x i The length of N is generally required to be N≤M / 2 to ensure that the total number of samples I≥N. After sampling is completed, the first sample matrix X=[x1,x2,...,x I ].

[0071] S20, performing singular value decomposition on the first sample matrix to obtain a singular value matrix, and performing noise reduction on the singular value matrix.

[0072] In the embodiment of the present application, singular value decomposition is performed on the first sample matrix, larger singular values ​​are retained, and the remaining singular values ​​are set to 0, thereby achieving noise reduction.

[0073] See also Figure 2 , Figure 2 A partial flow chart of a method for reducing noise of interface performance test index data provided by an embodiment of the present application. Figure 2 As shown, an embodiment of the present application provides a method for denoising interface performance test index data, wherein "performing singular value decomposition on the first sample matrix to obtain a singular value matrix" in step S20 may include steps S201 to S204, and these steps are described in detail below.

[0074] S201, normalize the first sample matrix.

[0075] In the embodiment of the present application, after obtaining the first sample matrix X=[x1, x2, ..., x I ], it can be normalized. Specifically, x i =[x i ,x i+1 ,...,x i-1+N ] is the i-th sample in the first sample matrix, which is a one-dimensional vector with a length of N. i The normalization process is shown in the following formula (1):

[0076]

[0077] Among them, x i ' -1+n For x i The normalized value of the nth value, x i-1+n For x i The nth value of , μ is x i The mean of i The calculation method of μ is shown in the following formula (2), and the calculation method of σ is shown in the following formula (3):

[0078]

[0079] The normalized first sample matrix is ​​represented as x i ′, accordingly, the normalized first sample matrix can be expressed as X′=[x′1,x′2,...,x′ I ].

[0080] S202, calculating a covariance matrix and an auxiliary representation parameter matrix according to the normalized first sample matrix.

[0081] In the embodiment of the present application, after the first sample matrix is ​​normalized, its covariance matrix and auxiliary representation parameter matrix can be calculated. The calculation method of the covariance matrix C is shown in the following formula (4), and the calculation method of the auxiliary representation parameter matrix D is shown in the following formula (5):

[0082] C=X′X′ T (4)

[0083] D=X′ T X′ (5)

[0084] S203, extracting a first eigenvector matrix from the covariance matrix, and extracting a second eigenvector matrix from the auxiliary representation parameter matrix.

[0085] In the embodiment of the present application, the eigenvector matrix (i.e., the first eigenvector matrix) U is extracted from the covariance matrix C, and the eigenvector matrix (i.e., the second eigenvector matrix) V is extracted from the auxiliary representation parameter matrix D. The first eigenvector matrix U and the second eigenvector matrix V are both unit orthogonal matrices.

[0086] S204, calculating a singular value matrix using the first eigenvector matrix, the second eigenvector matrix and the normalized sample matrix.

[0087] In the embodiment of the present application, the singular value matrix can be calculated using the following formula (6):

[0088] X′=UΔV T (6)

[0089] Among them, Δ represents the singular value matrix, And λ1≥λ2≥...≥λ N ,λ n It is the eigenvalue of the covariance matrix C or the auxiliary representation parameter matrix D, which is called the singular value of the sample in this application.

[0090] Therefore, after obtaining the singular value matrix Δ, the singular values ​​λ1,λ2,...,λ N , the denoising process can be carried out. First, the singular values ​​are analyzed to find the minimum K that meets the conditions shown in the following formula (7):

[0091]

[0092] Among them, λ1,λ2,...,λ K Contains most of the effective energy in the sample, λ K+1 ,λ K+2 ,...,λ N Contains most of the noise energy. K+1 ,λ K+2 ,...,λN Set to 0, the denoised singular value matrix is This achieves a noise reduction effect.

[0093] S30, reconstructing and restoring a second sample matrix of the Web interface performance test index data using the denoised singular value matrix.

[0094] In the embodiment of the present application, the denoised singular value matrix is ​​used to reconstruct and restore the Web interface performance test index data, thereby obtaining a second sample matrix, which is the denoised first sample matrix.

[0095] If the data enhancement method is repeated testing, the second sample matrix can be obtained after reconstruction. If the data enhancement method is multiple local sampling, it is necessary to use inverse sampling after reconstruction to restore the second sample matrix.

[0096] See also Figure 3 , Figure 3 Another partial flow chart of a method for reducing noise of interface performance test index data provided by an embodiment of the present application. Figure 3 As shown, an embodiment of the present application provides a method for denoising interface performance test index data, wherein "using the denoised singular value matrix to reconstruct and restore the second sample matrix of Web interface performance test index data" in step S30 may include steps S3011 to S3012, and these steps are described in detail below.

[0097] S3011: If the first sample matrix is ​​obtained through repeated testing, a first intermediate sample matrix is ​​calculated using the first eigenvector matrix, the second eigenvector matrix, and the denoised singular value matrix.

[0098] In the embodiment of the present application, if the data enhancement method is repeated testing, the following formula (8) is used to calculate the corresponding intermediate sample matrix, that is, the first intermediate sample matrix

[0099]

[0100] S3012: Perform inverse normalization on the first intermediate sample matrix to reconstruct a second sample matrix.

[0101] In the embodiment of the present application, the first intermediate sample matrix Perform inverse normalization to For example, the inverse normalization process is shown in the following formula (9):

[0102]

[0103] For the first intermediate sample matrix After inverse normalization, the second sample matrix can be reconstructed

[0104] See also Figure 4 , Figure 4 Another partial flow chart of a method for reducing noise of interface performance test index data provided by an embodiment of the present application. Figure 4 As shown, an embodiment of the present application provides a method for denoising interface performance test index data, wherein "using the denoised singular value matrix to reconstruct and restore the second sample matrix of Web interface performance test index data" in step S30 may include steps S3021 to S3023, and these steps are described in detail below.

[0105] S3021: If the first sample matrix is ​​obtained through multiple local samplings, calculate a second intermediate sample matrix using the first eigenvector matrix, the second eigenvector matrix, and the denoised singular value matrix.

[0106] In the embodiment of the present application, if the data enhancement method is multiple local sampling, the corresponding intermediate sample matrix, that is, the second intermediate sample matrix, can be calculated according to the above formula (8), which will not be repeated here.

[0107] S3022: Perform inverse normalization on the second intermediate sample matrix.

[0108] In the embodiment of the present application, the process of normalizing the second intermediate sample matrix may be as shown in the above formula (9), which will not be described in detail here.

[0109] S3023, performing mean calculation on samples with the same subscript in the inverse normalized second intermediate sample matrix to restore and obtain the second sample matrix.

[0110] In the embodiment of the present application, for the data enhancement method of multiple local sampling, the original data recovery process is required. The second intermediate sample matrix after inverse normalization can be expressed as in, The recovery process is as follows: The samples with the same subscripts are used to calculate the mean. Specifically, we can find The samples with subscript m (1≤m≤M) in the middle and calculate their average value Then we get the second sample matrix

[0111] A method for reducing noise of interface performance test index data provided in an embodiment of the present application can effectively reduce the impact of noise in performance test index data on the performance analysis process. By suppressing noise, on the one hand, it helps to more clearly expose performance defects in performance test index data and improve the accuracy of performance analysis. On the other hand, it helps to find the right analysis direction, speed up the troubleshooting of performance defects, and improve the efficiency of performance analysis.

[0112] A method for reducing the noise of interface performance test index data provided in an embodiment of the present application is introduced above. A device for executing the above-mentioned method for reducing the noise of interface performance test index data will be introduced below.

[0113] See also Figure 5 , Figure 5 A schematic diagram of a device for reducing noise of interface performance test index data provided by an embodiment of the present application. Figure 5 As shown, an interface performance test index data noise reduction device provided in an embodiment of the present application includes:

[0114] A data enhancement module 10, configured to obtain a first sample matrix of Web interface performance test index data through data enhancement;

[0115] A singular decomposition and noise reduction module 20, configured to perform singular value decomposition on the first sample matrix to obtain a singular value matrix, and perform noise reduction on the singular value matrix;

[0116] The reconstruction and recovery module 30 is used to reconstruct and recover the second sample matrix of the Web interface performance test index data using the denoised singular value matrix.

[0117] In a possible implementation, the data enhancement module 10 is specifically configured to:

[0118] Acquire multiple first original data samples in a repeated testing manner, and form a first sample matrix with the multiple first original data samples; or,

[0119] A single second original data sample is obtained in a single test manner; and a first sample matrix is ​​obtained by performing multiple local sampling on the second original data sample.

[0120] In a possible implementation, the singular decomposition and denoising module 20 for performing singular value decomposition on the first sample matrix to obtain a singular value matrix is ​​specifically used to:

[0121] Normalize the first sample matrix; calculate the covariance matrix and the auxiliary representation parameter matrix based on the normalized first sample matrix; extract the first eigenvector matrix from the covariance matrix, and extract the second eigenvector matrix from the auxiliary representation parameter matrix; calculate the singular value matrix using the first eigenvector matrix, the second eigenvector matrix and the normalized sample matrix.

[0122] In a possible implementation, the reconstruction and recovery module 30 is specifically configured to:

[0123] If the first sample matrix is ​​obtained through repeated testing, the first intermediate sample matrix is ​​calculated using the first eigenvector matrix, the second eigenvector matrix and the denoised singular value matrix; the first intermediate sample matrix is ​​inversely normalized to reconstruct the second sample matrix.

[0124] In a possible implementation, the reconstruction and recovery module 30 is specifically configured to:

[0125] If the first sample matrix is ​​obtained through multiple local samplings, the second intermediate sample matrix is ​​calculated using the first eigenvector matrix, the second eigenvector matrix and the denoised singular value matrix; the second intermediate sample matrix is ​​inverse normalized; and the mean of the samples with the same subscript in the inverse normalized second intermediate sample matrix is ​​calculated to restore the second sample matrix.

[0126] It should be noted that the detailed functions of each module in the embodiment of the present application can be found in the corresponding public part of the above-mentioned interface performance test index data denoising method embodiment, which will not be repeated here.

[0127] The present application also provides an electronic device in an embodiment. Figure 6 , Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device in the embodiment of the present application may include but is not limited to fixed terminals such as mobile phones, notebook computers, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 6 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0128] like Figure 6 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. When the electronic device is powered on, various programs and data required for the operation of the electronic device are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0129] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a memory card, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0130] An embodiment of the present application also provides a computer program product including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any method for reducing the noise of interface performance test indicator data provided in the embodiment of the present application.

[0131] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any method for reducing the noise of interface performance test indicator data provided in an embodiment of the present application.

[0132] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0133] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0134] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0135] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. A method for reducing noise of interface performance test index data, characterized in that: The method for reducing noise of interface performance test index data includes: Obtain the first sample matrix of Web interface performance test indicator data through data enhancement; Performing singular value decomposition on the first sample matrix to obtain a singular value matrix, and performing noise reduction on the singular value matrix; The denoised singular value matrix is ​​used to reconstruct and restore a second sample matrix of the Web interface performance test index data.

2. The method for reducing noise of interface performance test index data according to claim 1, characterized in that: The method of obtaining the first sample matrix of Web interface performance test index data through data enhancement includes: Acquire the plurality of first original data samples in a repeated testing manner, and form the first sample matrix with the plurality of first original data samples; or, A single second original data sample is acquired in a single test manner; and the second original data sample is locally sampled multiple times to obtain the first sample matrix.

3. The method for reducing noise of interface performance test index data according to claim 2, characterized in that: The step of performing singular value decomposition on the first sample matrix to obtain a singular value matrix includes: Normalizing the first sample matrix; Calculating a covariance matrix and the auxiliary representation parameter matrix according to the normalized first sample matrix; extracting a first eigenvector matrix from the covariance matrix, and extracting a second eigenvector matrix from the auxiliary representation parameter matrix; The singular value matrix is ​​calculated using the first eigenvector matrix, the second eigenvector matrix, and the normalized sample matrix.

4. The method for reducing noise of interface performance test index data according to claim 3, characterized in that: The method of reconstructing and restoring the second sample matrix of the Web interface performance test index data using the denoised singular value matrix includes: If the first sample matrix is ​​obtained by repeated testing, calculating a first intermediate sample matrix using the first eigenvector matrix, the second eigenvector matrix and the denoised singular value matrix; The first intermediate sample matrix is ​​inversely normalized to reconstruct the second sample matrix.

5. The method for reducing noise of interface performance test index data according to claim 3, characterized in that: The method of reconstructing and restoring the second sample matrix of the Web interface performance test index data using the denoised singular value matrix includes: If the first sample matrix is ​​obtained through multiple local samplings, a second intermediate sample matrix is ​​calculated using the first eigenvector matrix, the second eigenvector matrix and the denoised singular value matrix; performing inverse normalization on the second intermediate sample matrix; The samples with the same subscript in the second intermediate sample matrix after inverse normalization are averaged to restore the second sample matrix.

6. A device for reducing noise of interface performance test index data, characterized in that: The interface performance test index data noise reduction device comprises: A data enhancement module, used for obtaining a first sample matrix of Web interface performance test indicator data through data enhancement; A singular decomposition and noise reduction module, used for performing singular value decomposition on the first sample matrix to obtain a singular value matrix, and performing noise reduction on the singular value matrix; The reconstruction and recovery module is used to reconstruct and restore the second sample matrix of the Web interface performance test index data using the denoised singular value matrix.

7. The device for reducing noise of interface performance test index data according to claim 6, characterized in that: The data enhancement module is specifically used for: Acquire the plurality of first original data samples in a repeated testing manner, and form the first sample matrix with the plurality of first original data samples; or, Acquiring a single second raw data sample in a single test manner; The second original data samples are locally sampled multiple times to obtain the first sample matrix.

8. A computer program product, characterized in that It includes computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the method for reducing noise of interface performance test index data as described in any one of claims 1 to 5.

9. An electronic device, characterized in that: The method comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement the method for reducing noise of interface performance test indicator data as described in any one of claims 1 to 5.

10. A computer storage medium, characterized in that: The storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the method for reducing noise of interface performance test indicator data as described in any one of claims 1 to 5.

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