Network performance test analysis method and equipment

Abstract

The invention discloses a network performance test analysis method and equipment. The method comprises the following steps: acquiring a website file, and analyzing a plurality of websites from the website file; testing each website to obtain test values of one or more performance indexes of each website in each test; calculating an index score of each performance index of each website based on the test values of the performance indexes; and performing statistical analysis on the website based on the index score of the performance index. According to the method, the multiple websites can be automatically tested, the testing efficiency is improved, meanwhile, the corresponding index score can be given for each performance index of each website, statistical analysis is carried out, the performance indexes do not need to be manually analyzed through naked eyes, the analysis efficiency is improved, and the analysis result is more objective and accurate. And finally, by performing statistical analysis on the performance, the cost of manually integrating and analyzing the data can be greatly reduced, so that the human resource cost is reduced, and the overall efficiency of the daily test work and the maintenance and management of the test result are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of network-related technical fields, and in particular to a network performance testing and analysis method, electronic equipment, storage medium and computer program product. Background Art

[0002] Existing tests for the front end of a network (web) require accessing multiple URLs, testing the performance of each URL, and analyzing the performance of each URL.

[0003] There are many third-party tools for web front-end performance testing, such as Lighthouse, which runs through Chrome extensions, and Pingdom, which tests page loading speed by entering a URL address. There are many types of third-party tools available. However, existing web front-end performance tests require users to manually enter the test URL, and only the final performance test results of a single URL are obtained. It is impossible to analyze specific performance indicators that are of concern in daily testing, obtain analysis results, manage overall performance test results, and analyze some daily result trends.

[0004] Therefore, the existing network performance testing method requires manual testing, which is inefficient and cannot perform automatic statistical analysis, resulting in poor analysis results. Summary of the invention

[0005] Based on this, it is necessary to provide a network performance testing and analysis method, electronic device, storage medium and computer program product to address the technical problems that the network performance testing of the prior art uses manual testing, resulting in low efficiency and poor analysis results.

[0006] The present invention provides a network performance test and analysis method, comprising:

[0007] Obtain a URL file, and parse multiple URLs from the URL file;

[0008] Testing each URL to obtain test values ​​of one or more performance indicators of each URL in each test;

[0009] Calculate the indicator score of each performance indicator of each URL based on the test value of the performance indicator;

[0010] A statistical analysis is performed on the website based on the indicator score of the performance indicator.

[0011] Furthermore, the calculation of the index score of each performance index of each website based on the test value of the performance index includes:

[0012] For each performance indicator of each website, multiple scoring standard intervals corresponding to the performance indicator are obtained, the scoring standard interval where the test value of the performance indicator is located is determined, and the score corresponding to the scoring standard interval where the test value of the performance indicator is located is used as the indicator score of the performance indicator.

[0013] Furthermore, the performing statistical analysis on the website based on the indicator score of the performance indicator includes:

[0014] Calculating the average value of the indicator score of each performance indicator of each website in different time periods as a single indicator average value, and performing statistical analysis on the website based on the single indicator average value; and / or

[0015] The multi-index weighted values ​​are calculated for the index scores of all performance indexes of each website in the same test, the average values ​​of the multi-index weighted values ​​in different time periods are calculated as the multi-index average values, and the website is statistically analyzed based on the multi-index average values.

[0016] Furthermore, the performing statistical analysis on the website based on the single indicator average value includes:

[0017] Obtain the time period to be analyzed and the performance indicators to be analyzed;

[0018] Aggregate the single indicator average values ​​of the performance indicators to be analyzed of all the websites within the time period to be analyzed, obtain and display the aggregation result of the performance indicators to be analyzed in the time period to be analyzed.

[0019] Furthermore, the performing statistical analysis on the website based on the single indicator average value includes:

[0020] Obtain the time period to be analyzed, the performance indicators to be analyzed, and the URL to be analyzed;

[0021] Based on the single indicator average value of the performance indicator to be analyzed of the website to be analyzed within the time period to be analyzed, the single indicator fluctuation value of the performance indicator to be analyzed of the website to be analyzed within the time period to be analyzed is calculated, and the stability of the performance indicator to be analyzed of the website to be analyzed in the time period to be analyzed is evaluated according to the single indicator fluctuation value.

[0022] Furthermore, the performing statistical analysis on the website based on the average values ​​of the multiple indicators includes:

[0023] Get the time period to be analyzed;

[0024] Aggregate the multi-index average values ​​of all the URLs within the time period to be analyzed, obtain the aggregation result of the time period to be analyzed and display it.

[0025] Furthermore, the performing statistical analysis on the website based on the average values ​​of the multiple indicators includes:

[0026] Get the time period and URL to be analyzed;

[0027] Based on the multi-index average values ​​of the website to be analyzed in the time period to be analyzed, the overall fluctuation value of the website to be analyzed in the time period to be analyzed is calculated, and the stability of the website to be analyzed in the time period to be analyzed is evaluated according to the overall fluctuation value.

[0028] The present invention provides an electronic device, comprising:

[0029] at least one processor; and,

[0030] a memory communicatively connected to at least one of the processors; wherein,

[0031] The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the network performance test and analysis method as described above.

[0032] The present invention provides a storage medium, wherein the storage medium stores computer instructions, and when a computer executes the computer instructions, it is used to execute all steps of the network performance test and analysis method as described above.

[0033] The present invention provides a computer program product, including a computer program / instruction, which implements the network performance test and analysis method as described above when executed by a processor.

[0034] The present invention extracts multiple URLs that need to be tested from a URL file, then tests each URL, and performs statistical analysis based on the obtained performance indicators. The present invention can automatically test multiple URLs to improve test efficiency. At the same time, it can give corresponding index scores and perform statistical analysis for each performance indicator of each URL, without the need to manually analyze the performance indicators with the naked eye, thereby improving the analysis efficiency and making the analysis results more objective and accurate. Finally, by performing statistical analysis on performance, the cost of manpower to integrate data and analyze data can be greatly reduced, thereby reducing human resource costs and comprehensively improving the overall efficiency of daily testing work and the maintenance and management of test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of a network performance test and analysis method according to an embodiment of the present invention;

[0036] Figure 2 A flowchart of a network performance test and analysis method according to another embodiment of the present invention;

[0037] Figure 3 A flowchart of a network performance test and analysis method according to the best embodiment of the present invention;

[0038] Figure 4 The figure is a schematic diagram of the hardware structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0039] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. The same components are represented by the same reference numerals. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the accompanying drawings, and the words "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0040] The following is an explanation of some key terms / technical abbreviations used in the description of the present invention:

[0041] pyqt: Qt is a cross-platform C++ development framework for creating desktop and mobile applications. It provides a rich set of GUI components, tool libraries, and platform-related functions. PyQt is a cross-platform library for Python that is used to create desktop and mobile applications. It is based on the Qt framework and provides a rich set of GUI components and tool libraries.

[0042] lighthouse: is an open source automated tool for improving the quality of network applications.

[0043] Pingdom: is an online performance monitoring tool used to monitor the performance and availability of a website. It can help developers discover and solve performance problems and improve user experience.

[0044] sitespeed.io: is an open source web performance testing tool for measuring website performance and user experience.

[0045] Excel: is a spreadsheet software developed by Microsoft for creating, editing and storing spreadsheets.

[0046] openpyxl: is a Python library for reading and writing Excel 2010xlsx / xlsm / xltx / xltm files. It provides rich functions that can be used to create, modify and read Excel files.

[0047] Requests: is a Python library for sending HTTP requests. It provides a simple and easy-to-use API that can be used to send HTTP requests such as GET, POST, PUT, DELETE, and process response data.

[0048] like Figure 1 The flowchart of a network performance test and analysis method according to an embodiment of the present invention includes:

[0049] Step S101, obtaining a website address file, and parsing a plurality of websites from the website address file;

[0050] Step S102, testing each website to obtain test values ​​of one or more performance indicators of each website in each test;

[0051] Step S103, calculating the indicator score of each performance indicator of each website based on the test value of the performance indicator;

[0052] Step S104: performing statistical analysis on the website based on the indicator score of the performance indicator.

[0053] This embodiment is a network performance test analysis method that can quickly perform front-end performance testing and automatically analyze performance results. By automatically analyzing web front-end performance and obtaining analysis results and managing and maintaining overall performance test results, human resource costs can be reduced and the overall efficiency of daily testing work and maintenance management of test results can be comprehensively improved.

[0054] Specifically, the present invention can be applied to electronic devices with processing capabilities, such as computers.

[0055] In the computer, a visualization tool is created, specifically:

[0056] The visualization device tool is based on pyqt to create an interface for managing web performance test results and analysis. The overall interface includes the management of benchmark performance indicators, management of web performance test results, and trend analysis functions.

[0057] For web performance testing, you can use sitespeed.io to perform web performance testing. First, you need to create overall performance indicators. Performance indicators include but are not limited to: total blocking time (TBT), white screen time (FP), first screen time (FCP), maximum content drawing time (LCP), page layout offset (CLS) and other specific web performance indicators. Use visualization tools to manage the thresholds of these indicators, such as setting the total blocking time (TBT) to 100ms and the white screen time (FP) to 300ms.

[0058] After setting the performance indicator threshold, you can use Excel to write the specific front-end address that needs to be tested. Excel mainly contains the website address of the web performance test, that is, the URL address. Then import Excel into the visualization device tool.

[0059] The Excel file is a URL file. By executing step S101, the URL file is obtained, and a plurality of URLs are parsed from the URL file.

[0060] Specifically, the visualization device tool parses the corresponding excel file through python's openpyxl to parse out multiple URLs.

[0061] Then, step S102 is executed to test each website to obtain test values ​​of one or more performance indicators of each website in each test.

[0062] Specifically, by calling sitespeed.io to test the performance of each URL, the test value of one or more performance indicators of each URL is finally obtained.

[0063] Then, step S103 is executed to calculate the indicator score of each performance indicator of each website based on the test value of the performance indicator.

[0064] Specifically, for the obtained performance test results, the page results are extracted by using Python's requests through the visualization device tool, the final performance indicator execution results are extracted, and then managed in the device tool. Each execution will generate corresponding performance test result data based on the timestamp and different URL addresses. Among them, the obtained performance test result data is only the specific value of the performance indicator. It is necessary to calculate the indicator score of the performance indicator based on the threshold of the indicator set by the user.

[0065] Finally, step S104 is executed to perform statistical analysis on the website based on the indicator score of the performance indicator.

[0066] Specifically, the statistical analysis results are displayed in the form of visual charts. The displayed data fields mainly include: execution timestamp, front-end URL address, corresponding result value of one or more performance indicator tests, and whether each value is within the set threshold range. It is specifically displayed in a table. Then, based on the indicator score of the performance indicator, the website is statistically analyzed, and the corresponding statistical analysis charts of different performance indicator results are generated through pyecharts.

[0067] The present invention can automatically test multiple websites, improve the test efficiency, and at the same time, can give a corresponding index score and perform statistical analysis for each performance index of each website, without the need to manually analyze the performance index by the naked eye, thereby improving the analysis efficiency and making the analysis results more objective and accurate. Finally, by performing statistical analysis on the performance, the cost of manpower to integrate and analyze data can be greatly reduced, thereby reducing the cost of human resources and comprehensively improving the overall efficiency of daily testing work and the maintenance and management of test results.

[0068] like Figure 2 The flowchart of a network performance test and analysis method in another embodiment of the present invention includes:

[0069] Step S201, obtaining a website address file, and parsing a plurality of websites from the website address file;

[0070] Step S202, testing each website to obtain test values ​​of one or more performance indicators of each website in each test;

[0071] Step S203, for each performance indicator of each website, obtain multiple scoring standard intervals corresponding to the performance indicator, determine the scoring standard interval where the test value of the performance indicator is located, and use the score corresponding to the scoring standard interval where the test value of the performance indicator is located as the indicator score of the performance indicator;

[0072] Step S204, calculating the average value of the indicator score of each performance indicator of each website in different time periods as a single indicator average value, and performing statistical analysis on the website based on the single indicator average value; and / or

[0073] The multi-index weighted values ​​are calculated for the index scores of all performance indexes of each website in the same test, the average values ​​of the multi-index weighted values ​​in different time periods are calculated as the multi-index average values, and the website is statistically analyzed based on the multi-index average values.

[0074] Specifically, in the computer, a visualization device tool is made, specifically:

[0075] The visualization device tool is based on pyqt to create an interface for managing web performance test results and analysis. The overall interface includes the management of benchmark performance indicators, management of web performance test results, and trend analysis functions.

[0076] For web performance testing, you can use sitespeed.io to perform web performance testing. First, you need to create overall performance indicators. Performance indicators include but are not limited to: total blocking time (TBT), white screen time (FP), first screen time (FCP), maximum content drawing time (LCP), page layout offset (CLS) and other specific web performance indicators. Use visualization tools to manage the thresholds of these indicators, such as setting the total blocking time (TBT) to 100ms and the white screen time (FP) to 300ms.

[0077] After setting the performance indicator threshold, you can use Excel to write the specific front-end address that needs to be tested. Excel mainly contains the website address of the web performance test, that is, the URL address. Then import Excel into the visualization device tool.

[0078] The Excel file is a URL file. By executing step S201, the URL file is obtained, and a plurality of URLs are parsed from the URL file.

[0079] Specifically, the visualization device tool parses the corresponding excel file through python's openpyxl to parse out multiple URLs.

[0080] Then, step S202 is executed to test each website to obtain test values ​​of one or more performance indicators of each website in each test.

[0081] Specifically, by calling sitespeed.io to test the performance of each URL, the test value of one or more performance indicators of each URL is finally obtained.

[0082] Then, execute step S203, for each performance indicator of each website, obtain multiple scoring standard intervals corresponding to the performance indicator, determine the scoring standard interval where the test value of the performance indicator is located, and use the score corresponding to the scoring standard interval where the test value of the performance indicator is located as the indicator score of the performance indicator.

[0083] Specifically, for the obtained performance test results, the page results are extracted by using Python's requests through the visualization device tool, the final performance indicator execution results are extracted, and then managed in the device tool. Each execution will generate corresponding performance test result data based on the timestamp and different URL addresses. Among them, the obtained performance test result data is only the specific value of the performance indicator. The test value of the performance indicator needs to be based on the threshold of the indicator set by the user to calculate the indicator score of the performance indicator.

[0084] Specifically, each test obtains test values ​​of multiple performance indicators. Each performance indicator is divided into multiple scoring standard intervals through a user-defined or system-defined threshold. Each scoring standard interval is pre-set for a score. The score corresponding to the scoring standard interval where the test value is located is used as the indicator score of the website for the performance indicator in this test.

[0085] For example, taking the total blocking time (TBT) as an example, TBT is divided into the following threshold ranges: The threshold ranges can be customized by the user according to the visualization interface.

[0086]

[0087]

[0088] Finally, step S204 is executed to calculate the average value of the indicator score of each performance indicator of each website in different time periods as the single indicator average value, and perform statistical analysis on the website based on the single indicator average value; and / or

[0089] The multi-index weighted values ​​are calculated for the index scores of all performance indexes of each website in the same test, the average values ​​of the multi-index weighted values ​​in different time periods are calculated as the multi-index average values, and the website is statistically analyzed based on the multi-index average values.

[0090] Specifically, the statistical analysis results are displayed in the form of visual charts. The displayed data fields mainly include: execution timestamp, front-end URL address, corresponding result value of one or more performance indicator tests, and whether each value is within the set threshold range. It is specifically displayed in a table. Then the URL is statistically analyzed based on the indicator score of the performance indicator. Specifically, it can be grouped according to the time of result acquisition and the specified time aggregation method, such as by day, according to the time unit of the day. Then the average value of each indicator value within a day is calculated, and the score of multiple indicators also generates the corresponding average score. Finally, pyecharts is used to generate the corresponding statistical analysis chart of different performance indicator results within the selected time range. The statistical analysis chart can be a result trend chart. For example, a result trend change chart for each indicator in units of days, such as a few days, or a trend change chart of the overall score according to the dimension of days can be generated.

[0091] Specifically, a visual interface is used to manage different web performance test results and performance indicator thresholds, and then the results of the web front-end performance test are automatically analyzed and managed. It can also further aggregate the results of multiple performance indicators in multiple different time periods, perform automatic trend analysis, and visualize the results.

[0092] Statistical analysis may be performed on a single performance indicator. Specifically, the average value of the indicator score of each performance indicator of each website in different time periods is calculated as the single indicator average value, and the website is statistically analyzed based on the single indicator average value.

[0093] The overall trend analysis is divided into days, weeks, and months. For each calculated test value of each performance indicator, the indicator score of this performance indicator is calculated according to the threshold of each performance indicator. Then all performance test results are split according to time dimensions such as days, weeks, and months. Then, according to different time periods, the average value of the indicator score of each performance indicator tested multiple times in each time period is calculated as the single indicator average value corresponding to each performance indicator. Then, the website is statistically analyzed based on the single indicator average value.

[0094] Statistical analysis can also be performed on the overall performance. Specifically, for the analysis of multiple indicators, after the threshold range is specified for each evaluation indicator, the weight standard occupied by each evaluation indicator is set. For example, set the TBT weight to 30%, the FP weight to 10%, the FCP weight to 10%, the LCP weight to 25%, and the CLS weight to 25%. Then, according to the scoring criteria and combined with sitespeed.io, the indicator score for each evaluation indicator is obtained once, and then the weighted average of the indicator scores of all evaluation indicators is calculated according to the weight as the multi-indicator weighted value to obtain the scoring result of a performance test. Then, all performance test results are split according to time dimensions such as days, weeks, and months. After that, according to different time periods, the average value of the multi-indicator weighted values ​​of all tests in each time period is calculated as the multi-indicator average value of the website in that time period. Then, the website is statistically analyzed based on the multi-indicator average value.

[0095] This embodiment can analyze the performance test results after the web front-end performance test. It can reduce the need for manpower to analyze whether the performance indicators meet the limit value by naked eyes. At the same time, this embodiment can manage the performance test analysis results and perform overall statistical analysis, such as an overall pass rate that meets the limited performance indicator requirements, and some trends of performance indicators to further manage and automatically analyze the overall performance test results, which is clearer and more intuitive, and through rational management, it greatly reduces the cost of manpower to integrate and analyze data.

[0096] In one embodiment, the performing statistical analysis on the website based on the single indicator average value includes:

[0097] Obtain the time period to be analyzed and the performance indicators to be analyzed;

[0098] Aggregate the single indicator average values ​​of the performance indicators to be analyzed of all the websites within the time period to be analyzed, obtain and display the aggregation result of the performance indicators to be analyzed in the time period to be analyzed.

[0099] Specifically, the statistical analysis includes automatically aggregating the results according to different time periods. First, the time period to be analyzed and the performance indicators to be analyzed are obtained.

[0100] The time period to be analyzed and the performance index to be analyzed are specified by the user or preset by the system. The time period to be analyzed can be all time periods or part of time periods, and the performance index to be analyzed can be all performance indexes or part of performance indexes.

[0101] Then, the single indicator average values ​​of the performance indicators to be analyzed of all the websites in the time period to be analyzed are aggregated to obtain and display the aggregated results of the performance indicators to be analyzed in the time period to be analyzed.

[0102] Aggregation can use existing clustering algorithms to aggregate all URLs within the time period to be analyzed, and for each performance indicator to be analyzed, use clustering algorithms based on the average value of each single indicator of each performance indicator to be analyzed to obtain multiple aggregation results, which are displayed in images and other ways. Based on the aggregation results, URLs with relatively close average values ​​of each single indicator of each performance indicator to be analyzed are grouped into a cluster and displayed.

[0103] Through aggregation, this embodiment enables users to see the trend changes of each indicator in different time periods according to different time dimensions, as well as the final trend changes after the scores of multiple indicators are calculated by weighted average. At the same time, users can observe the stability of system performance from multiple aspects through different divisions such as days, weeks, and months, and distinguish the overall performance trend effects of different granularities through different time dimensions.

[0104] In one embodiment, the performing statistical analysis on the website based on the single indicator average value includes:

[0105] Obtain the time period to be analyzed, the performance indicators to be analyzed, and the URL to be analyzed;

[0106] Based on the single indicator average value of the performance indicator to be analyzed of the website to be analyzed within the time period to be analyzed, the single indicator fluctuation value of the performance indicator to be analyzed of the website to be analyzed within the time period to be analyzed is calculated, and the stability of the performance indicator to be analyzed of the website to be analyzed in the time period to be analyzed is evaluated according to the single indicator fluctuation value.

[0107] Specifically, the statistical analysis includes evaluating the stability of the website during the time period to be analyzed.

[0108] The time period to be analyzed, the performance index to be analyzed, and the URL to be analyzed are specified by the user or preset by the system. The time period to be analyzed can be all time periods or part of time periods, the performance index to be analyzed can be all performance indexes or part of performance indexes, and the URL to be analyzed can be part of URLs or all URLs.

[0109] When calculating the average value of a single indicator, the average value of the indicator score of the performance indicator tested multiple times in each time period is calculated. Therefore, based on the average value, the fluctuation value of the indicator score of the performance indicator to be analyzed in the time period to be analyzed can be calculated as the single indicator fluctuation value. The fluctuation value can be a variance, etc.

[0110] If the fluctuation value of a single indicator is within a certain threshold range, it means that the corresponding performance indicator is stable. If not, it means that the corresponding performance indicator is not stable.

[0111] In some embodiments, it also includes: using existing time series analysis methods to fit data and predict future trends, using mean square error to ensure the accuracy of the predicted data after calculation, and then automatically analyzing the predicted data.

[0112] In one embodiment, the performing statistical analysis on the website based on the multi-indicator average value includes:

[0113] Get the time period to be analyzed;

[0114] Aggregate the multi-index average values ​​of all the URLs within the time period to be analyzed, obtain the aggregation result of the time period to be analyzed and display it.

[0115] Specifically, the statistical analysis includes automatically aggregating the results according to time. First, the time period to be analyzed is obtained.

[0116] Then, the average values ​​of multiple indicators of all URLs in the time period to be analyzed are aggregated to obtain and display the aggregated results of the time period to be analyzed.

[0117] Aggregation can use existing clustering algorithms to aggregate all URLs within the time period to be analyzed based on the average values ​​of multiple indicators using clustering algorithms to obtain the overall aggregation results, which are then displayed in images and other ways. Based on the aggregation results, URLs with similar average values ​​of multiple indicators are grouped into a cluster and displayed.

[0118] In one embodiment, the performing statistical analysis on the website based on the multi-indicator average value includes:

[0119] Get the time period and URL to be analyzed;

[0120] Based on the multi-index average values ​​of the website to be analyzed in the time period to be analyzed, the overall fluctuation value of the website to be analyzed in the time period to be analyzed is calculated, and the stability of the website to be analyzed in the time period to be analyzed is evaluated according to the overall fluctuation value.

[0121] Statistical analysis involves evaluating the stability of a website over the period of time being analyzed.

[0122] When calculating the multi-index average, the average of the multi-index weighted values ​​of the index scores of all performance indicators tested multiple times in each time period is calculated. Therefore, based on the average, the fluctuation value of the multi-index weighted values ​​of the index scores in the time period to be analyzed can be calculated as the overall fluctuation value. The fluctuation value can be variance, etc.

[0123] If the overall fluctuation value is within a certain threshold range, it means that the overall performance is stable. If not, it means that the overall stability of the performance test is not good. Then analyze the performance problem in detail based on the score of each specific value.

[0124] In some embodiments, it also includes: using existing time series analysis methods to fit data and predict future trends, using mean square error to ensure the accuracy of the predicted data after calculation, and then automatically analyzing the predicted data. It can also directly calculate the weighted average based on custom indicator rules and weights, and then calculate whether it meets the defined average value. If not, further analyze the corresponding performance issues.

[0125] like Figure 3 The flowchart of a network performance test and analysis method according to the best embodiment of the present invention is shown, comprising:

[0126] Step S301, opening the device tool;

[0127] Step S302, establishing a user-defined benchmark threshold value, weight ratio, and a reasonable threshold value of the multi-indicator weighted average value or single indicator average value finally obtained for the performance indicator;

[0128] Step S303, write the URL address of the front-end web to be tested in Excel;

[0129] Step S304, import the excel file into the device tool, parse it through openpyxl, perform web front-end address performance analysis by calling sitespeed.io, extract performance test results through requests, and display the chart through pyecharts;

[0130] Step S305, automatically analyzing the web front-end performance test results, and performing test management and overall trend analysis on the overall results. Specifically, managing all obtained performance test results and all trend graphs obtained based on the results.

[0131] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0132] like Figure 4 The figure shows a hardware structure diagram of an electronic device of the present invention, including:

[0133] at least one processor 401; and,

[0134] A memory 402 is communicatively connected to at least one of the processors 401; wherein,

[0135] The memory 402 stores instructions that can be executed by at least one of the processors. The instructions are executed by at least one of the processors so that the at least one of the processors can execute the network performance test and analysis method as described above.

[0136] Figure 4 A processor 401 is taken as an example.

[0137] The electronic device may further include: an input device 403 and a display device 404 .

[0138] The processor 401, the memory 402, the input device 403 and the display device 404 may be connected via a bus or other means, and the figure takes the connection via a bus as an example.

[0139] The memory 402 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the network performance test analysis method in the embodiment of the present application, for example, Figure 1 , Figure 2 The processor 401 executes various functional applications and data processing by running the non-volatile software programs, instructions and modules stored in the memory 402, that is, the network performance test and analysis method in the above embodiment is implemented.

[0140] The memory 402 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the network performance test analysis method, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 402 may optionally include a memory remotely arranged relative to the processor 401, and these remote memories may be connected to a device for executing the network performance test analysis method 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.

[0141] The input device 403 can receive user clicks and generate signal inputs related to user settings and function control of the network performance test analysis method. The display device 404 can include display devices such as a display screen.

[0142] The one or more modules are stored in the memory 402, and when executed by the one or more processors 401, the network performance test and analysis method in any of the above method embodiments is executed.

[0143] The present invention can automatically test multiple websites, improve the test efficiency, and at the same time, can give a corresponding index score and perform statistical analysis for each performance index of each website, without the need to manually analyze the performance index by the naked eye, thereby improving the analysis efficiency and making the analysis results more objective and accurate. Finally, by performing statistical analysis on the performance, the cost of manpower to integrate and analyze data can be greatly reduced, thereby reducing the cost of human resources and comprehensively improving the overall efficiency of daily testing work and the maintenance and management of test results.

[0144] An embodiment of the present invention provides a storage medium, wherein the storage medium stores computer instructions. When a computer executes the computer instructions, the storage medium is used to execute all steps of the network performance test and analysis method as described above.

[0145] In the context of the present disclosure, a storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. The storage medium may be a machine-readable signal medium or a machine-readable storage medium. Alternatively, the storage medium may be a non-temporary computer-readable storage medium, for example, a non-temporary computer-readable storage medium may be a ROM, a random access memory (Random Access Memory, RAM), a compact disc read-only memory (Compact Disc ROM, CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0146] An embodiment of the present invention provides a computer program product, including a computer program / instruction, which implements the network performance test and analysis method as described above when executed by a processor.

[0147] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A network performance test and analysis method, characterized in that: include: Obtain a URL file, and parse multiple URLs from the URL file; Testing each URL to obtain test values ​​of one or more performance indicators of each URL in each test; Calculate the indicator score of each performance indicator of each URL based on the test value of the performance indicator; A statistical analysis is performed on the website based on the indicator score of the performance indicator.

2. The network performance test and analysis method according to claim 1, characterized in that: The calculation of the index score of each performance index of each URL based on the test value of the performance index includes: For each performance indicator of each website, multiple scoring standard intervals corresponding to the performance indicator are obtained, the scoring standard interval where the test value of the performance indicator is located is determined, and the score corresponding to the scoring standard interval where the test value of the performance indicator is located is used as the indicator score of the performance indicator.

3. The network performance test and analysis method according to claim 1, characterized in that: The statistical analysis of the website based on the indicator score of the performance indicator includes: Calculating the average value of the indicator score of each performance indicator of each website in different time periods as a single indicator average value, and performing statistical analysis on the website based on the single indicator average value; and / or The multi-index weighted values ​​are calculated for the index scores of all performance indexes of each website in the same test, the average values ​​of the multi-index weighted values ​​in different time periods are calculated as the multi-index average values, and the website is statistically analyzed based on the multi-index average values.

4. The network performance test and analysis method according to claim 3, characterized in that: The performing statistical analysis on the website based on the single indicator average value includes: Obtain the time period to be analyzed and the performance indicators to be analyzed; Aggregate the single indicator average values ​​of the performance indicators to be analyzed of all the websites within the time period to be analyzed, obtain and display the aggregation result of the performance indicators to be analyzed in the time period to be analyzed.

5. The network performance test and analysis method according to claim 3, characterized in that: The performing statistical analysis on the website based on the single indicator average value includes: Obtain the time period to be analyzed, the performance indicators to be analyzed, and the URL to be analyzed; Based on the single indicator average value of the performance indicator to be analyzed of the website to be analyzed within the time period to be analyzed, the single indicator fluctuation value of the performance indicator to be analyzed of the website to be analyzed within the time period to be analyzed is calculated, and the stability of the performance indicator to be analyzed of the website to be analyzed in the time period to be analyzed is evaluated according to the single indicator fluctuation value.

6. The network performance test and analysis method according to claim 3, characterized in that: The performing statistical analysis on the website based on the average values ​​of the multiple indicators includes: Get the time period to be analyzed; Aggregate the multi-index average values ​​of all the URLs within the time period to be analyzed, obtain the aggregation result of the time period to be analyzed and display it.

7. The network performance test and analysis method according to claim 3, characterized in that: The performing statistical analysis on the website based on the average values ​​of the multiple indicators includes: Get the time period and URL to be analyzed; Based on the multi-index average values ​​of the website to be analyzed in the time period to be analyzed, the overall fluctuation value of the website to be analyzed in the time period to be analyzed is calculated, and the stability of the website to be analyzed in the time period to be analyzed is evaluated according to the overall fluctuation value.

8. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the network performance testing and analysis method as described in any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium stores computer instructions, and when a computer executes the computer instructions, it is used to execute all steps of the network performance testing and analysis method as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the network performance testing and analysis method as described in any one of claims 1 to 7 is implemented.

Images