A machine learning based server test metric management method and system

Abstract

The application relates to the technical field of servers, in particular to a server test measurement management method and system based on machine learning. A server test management platform is built based on a Spring, a Spring Boot and a MyBatis framework, an independent measurement algorithm and a calculation formula are used to accurately calculate a server test progress, and the idea of machine learning is relied on to constantly improve parameters in the formula and the algorithm and improve measurement accuracy. The application mainly relates to three component modules of a test case management module, a test progress measurement module and an algorithm parameter iterative optimization module, can realize accurate measurement on the server test progress, provides data support for test personnel such as TSE and VM, more reasonably perfects and adjusts subsequent plans and task arrangements, guarantees that test work can be efficiently completed within a required time, and improves test efficiency and test quality.

Description

Technical Field

[0001] This invention relates to the field of server technology, and in particular to a server test metric management method and system based on machine learning. Background Technology

[0002] With the rapid development of the internet, the number and performance of servers are constantly being challenged. Therefore, conducting fast and high-quality server testing is of paramount importance. In traditional server testing, the Test System Engineer (TSE) typically develops the test plan, and the Virtual Machine (VM) assigns test tasks according to the plan to complete the server tests. Both the TSE and VM rely heavily on past project experience and personal expertise to estimate the planning and task timelines. Due to differences in personal experience and priorities, plans and tasks vary widely. Some plans prioritize testing key and difficult points, while others complete basic and relatively simple tests first.

[0003] Aside from those who develop the plan, other project members often struggle to accurately assess the overall testing progress. Therefore, a method for quickly and accurately measuring testing progress is essential. This method not only clarifies the overall project schedule but also allows for continuous adjustments and optimizations to the plan based on the current progress, ensuring that testing is completed on time and to a high standard.

[0004] In the current server testing process, TSE and VM need to calculate the completion rate by dividing the number of completed test cases by the total number of test tasks based on basic data such as the number of completed test cases and test tasks. Then, based on their personal experience and plans, they need to evaluate the test progress.

[0005] Due to differences in TSE and VM experience and focus, plans and tasks vary. Some plans prioritize testing key and difficult points, while others complete basic and relatively simple tests first. Testing progress is judged based on personal experience using fundamental data such as the number of completed test cases and test tasks. This approach heavily relies on the individual's ability and experience as a TSE or VM specialist, and the resulting progress is subject to error. Summary of the Invention

[0006] To address the aforementioned technical issues, this invention provides a machine learning-based server test metric management method and system. This system accurately measures server test progress by digitizing test experience and personal experience data, relying on data analysis to evaluate server test progress rather than experience. It continuously accumulates test data and server test metric data, analyzes the errors, and continuously optimizes the analysis algorithm and calculation formula to further improve the accuracy of server test metrics, providing more accurate data for the next step of planning and task allocation.

[0007] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0008] In a first aspect, in one embodiment of the present invention, a server test measurement management system based on machine learning is provided, including a test case management module, a test progress measurement module, and an algorithm parameter iteration optimization module;

[0009] The test case management module is used to manage and record basic information about the execution of server test cases.

[0010] The test progress measurement module is used to read all the plans and tasks of the project server test, and to calculate the quantized time of each test case and the total quantized time based on the total number of test cases to be executed and the number of times each test case needs to be executed. It is also used to obtain the deviation between the quantized time and the project execution time, as well as the current project progress.

[0011] The algorithm parameter iterative optimization module is used to calculate the changing trends of average test duration and pass rate, and adjust the duration coefficient X and pass rate coefficient Y according to the changes in the trends.

[0012] As a further aspect of the present invention, the test case management module records basic information about the execution of test cases, including the number of executions, duration, and pass rate. The test case management module is also used for adding and maintaining test cases.

[0013] As a further aspect of the present invention, the test progress measurement module is used to read all the plans and tasks of the server test for the project when the progress measurement is triggered during the server test, and to obtain all the test cases to be executed and the number of times each test case needs to be executed.

[0014] As a further aspect of the present invention, when calculating the quantized time for each test case, the test progress measurement module calculates the quantized time required to execute the test case based on the historical execution data, average execution time, test pass rate, and duration coefficient X and pass rate coefficient Y of the test case.

[0015] As a further aspect of the present invention, the test progress measurement module calculates the quantitative time required for test case execution using the formula: average execution time × (X + (Y ÷ pass rate)).

[0016] As a further aspect of the present invention, the test progress measurement module is also used to multiply the total quantized time of all test cases in the plan by the number of executions and sum them up after calculating the quantized time of each test case in sequence to obtain the total quantized time required for the completion of the plan.

[0017] As a further aspect of the present invention, the test progress measurement module is also used to sequentially calculate the total quantization time of the completed test cases and the time of the completed test cases, and divide the actual execution time by the completed quantization time to obtain the deviation between the quantization time and the execution time of the project.

[0018] As a further aspect of the present invention, the test progress measurement module is also used to obtain the current progress of the project by dividing the completed quantized time by the total planned quantized time, and to obtain the remaining planned time by multiplying the total planned quantized time by the execution deviation and then subtracting the time already executed.

[0019] As a further aspect of the present invention, when the algorithm parameter iterative optimization module adjusts the duration coefficient X and the pass rate coefficient Y, it increases the duration coefficient X when the average duration trend is increasing, decreases the duration coefficient X when the average duration trend is decreasing, increases the pass rate coefficient Y when the pass rate trend is increasing, and decreases the pass rate coefficient Y when the pass rate trend is decreasing.

[0020] Secondly, in one embodiment of the present invention, a server test metric management method based on machine learning is provided, the method comprising the following steps:

[0021] Step 1: Query and obtain all test cases to be executed and the number of times each test case needs to be executed;

[0022] Step 2: Traverse the test cases and obtain test case details. Query the historical execution data of the test case based on its unique identifier, the test case number.

[0023] Step 3: Calculate the average execution time of the test cases, the pass rate of the test cases, and the quantized time of each test case. Based on the historical execution data, average execution time, test pass rate, and the time coefficient X and pass rate coefficient Y of the test case, calculate the quantized time required to execute the test case.

[0024] Step 4: After calculating the quantized time for each use case in sequence, multiply the quantized time of all use cases in the plan by the number of executions and sum them up to obtain the total quantized time for the plan to complete the requirements.

[0025] Step 5: Based on Step 3 and Step 4, calculate the total quantization time and the time of each completed test case in sequence. Divide the actual execution time by the completed quantization time to obtain the deviation between the quantization time and the execution time of the project.

[0026] Step 6: Divide the completed quantified time by the total planned quantified time to obtain the current project progress; multiply the total planned quantified time by the execution deviation, and then subtract the completed time to obtain the remaining planned time.

[0027] As a further aspect of the present invention, the historical execution data of the use case obtained by querying includes the execution time and the execution result.

[0028] As a further aspect of the present invention, when calculating the average execution time of a test case, the average execution time of the test case is obtained by summing the total execution time of the test case and dividing by the number of executions.

[0029] As a further aspect of the present invention, when calculating the pass rate of a test case, the pass rate of the test case is obtained by dividing the number of times the test case that passed by PASS is executed by the total number of executions.

[0030] As a further aspect of the present invention, when calculating the quantified time required to execute the use case, the calculation formula is: average execution time × (X + (Y ÷ pass rate)) × number of use case executions.

[0031] Thirdly, in another embodiment provided by the present invention, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor loads and executes the computer program to implement the steps of a machine learning-based server test metric management method.

[0032] Fourthly, in another embodiment of the present invention, a storage medium is provided storing a computer program that, when loaded and executed by a processor, implements the steps of the machine learning-based server test metric management method.

[0033] The technical solution provided by this invention has the following beneficial effects:

[0034] The present invention provides a server test measurement management method and system based on machine learning. It builds a server test management platform based on the Spring, Spring Boot and MyBatis frameworks, uses independent measurement algorithms and calculation formulas to accurately calculate the server test progress, and relies on the idea of ​​machine learning to continuously improve the parameters in the formulas and algorithms to improve the measurement accuracy.

[0035] This invention mainly involves three modules: a test case management module, a test progress measurement module, and an algorithm parameter iteration and optimization module. Based on machine learning, this invention provides a server test measurement method and system that can accurately measure server test progress, provide data support for testers such as TSE and VM, and more reasonably improve and adjust subsequent plans and task arrangements, ensuring that the test work can be completed efficiently within the required time, thereby improving test efficiency and test quality.

[0036] Furthermore, the machine learning-based server test measurement method of this invention relies on machine learning concepts and technologies to construct an accurate and efficient algorithm and formula for measuring server test progress. This algorithm and formula can be continuously improved and adjusted as test data accumulates. Ultimately, it can predict subsequent server test plans and task arrangements, providing significant convenience to testers.

[0037] These or other aspects of the invention will become more apparent from the following description of embodiments. It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention.

[0038] These or other aspects of the invention will become more apparent from the following description of embodiments. It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. In the drawings:

[0040] Figure 1 This is a structural block diagram of a machine learning-based server test metric management system according to an embodiment of the present invention.

[0041] Figure 2 This is a flowchart illustrating a machine learning-based server test metric management method according to an embodiment of the present invention.

[0042] Figure 3 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0045] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0046] In the current server testing process, TSE and VM need to calculate the completion rate by dividing the number of completed test cases by the total number of test tasks based on basic data such as the number of completed test cases and test tasks. Then, based on their personal experience and plans, they need to evaluate the test progress.

[0047] Due to differences in TSE and VM experience and focus, plans and tasks vary. Some plans prioritize testing key and difficult points, while others complete basic and relatively simple tests first. Testing progress is judged based on personal experience using fundamental data such as the number of completed test cases and test tasks. This approach heavily relies on the individual's ability and experience as a TSE or VM specialist, and the resulting progress is subject to error.

[0048] To address the issue of accurately measuring server testing progress, this invention provides a machine learning-based server testing measurement management method and system. This method digitizes testing experience and personal experience, relying on data analysis to evaluate server testing progress rather than experience. It continuously accumulates test data and server testing measurement data, analyzes the errors, and continuously optimizes the analysis algorithm and calculation formula to further improve the accuracy of server testing measurements, providing more accurate data for subsequent planning and task allocation.

[0049] Specifically, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0050] Please see Figure 1 , Figure 1 This is a structural block diagram of a machine learning-based server test metric management system provided in an embodiment of the present invention, as shown below. Figure 1 As shown, in one embodiment of the present invention, a server test measurement management system based on machine learning is provided. The server test management platform is built based on the Spring, Spring Boot, and MyBatis frameworks. It uses independent measurement algorithms and calculation formulas to accurately calculate the server test progress. Relying on the idea of ​​machine learning, the parameters in the formulas and algorithms are continuously improved to enhance the measurement accuracy.

[0051] In this embodiment of the invention, the machine learning-based server test measurement management system includes a test case management module 100, a test progress measurement module 200, and an algorithm parameter iteration optimization module 300.

[0052] The test case management module 100 is used to manage and record basic information about the execution of server test cases.

[0053] The test progress measurement module 200 is used to read all the plans and tasks of the project server test, and to calculate the quantized time of each test case and the total quantized time based on the obtained total number of test cases to be executed and the number of times each test case needs to be executed; it is also used to calculate the quantized time of each test case and the total quantized time, to obtain the deviation between the quantized time and the project execution time and the current project progress.

[0054] The algorithm parameter iteration optimization module 300 is used to calculate the changing trends of the average test duration and pass rate, and adjust the duration coefficient X and pass rate coefficient Y according to the changes in the trends.

[0055] In the embodiments of this application, the test case management module 100 records basic information about the execution of test cases, including the number of executions, duration, and pass rate. The test case management module 100 is also used for adding and maintaining test cases.

[0056] See Figure 1 As shown, the test case management module 100 mainly manages server test cases, records information such as the number of times test cases are executed, duration, and pass rate, and is the data foundation of this invention.

[0057] The test case management module 100 mainly implements the following functions:

[0058] 1) Adding and maintaining test cases.

[0059] 2) Recording of test case execution logs, including the execution time and results of the test cases at the time of execution.

[0060] In the embodiments of this application, the test progress measurement module 200 is used to read all the plans and tasks of the server test of the project when the progress measurement is triggered during the server test, and to determine the number of times each test case needs to be executed based on all the test cases to be executed.

[0061] In the embodiments of this application, when calculating the quantized time for each test case, the test progress measurement module 200 calculates the quantized time required to execute the test case based on the historical execution data, average execution time, test pass rate, and duration coefficient X and pass rate coefficient Y of the test case.

[0062] In the embodiments of this application, when the test progress measurement module 200 calculates the quantified time required for test case execution, the calculation formula is: average execution time × (X + (Y ÷ pass rate)).

[0063] In an embodiment of this application, the test progress measurement module 200 is further configured to, after calculating the quantized time of each test case in sequence, multiply the quantized time of all test cases in the plan by the number of executions and sum them up to obtain the total quantized time required for the completion of the plan.

[0064] In the embodiments of this application, the test progress measurement module 200 is further used to sequentially calculate the total quantization time of the completed test cases and the time of the completed test cases, and divide the actual execution time by the completed quantization time to obtain the deviation between the quantization time and the execution time of the project.

[0065] In the embodiments of this application, the test progress measurement module 200 is further used to obtain the current progress of the project by dividing the completed quantified time by the total planned quantified time, and to obtain the remaining planned time by multiplying the total planned quantified time by the execution deviation and then subtracting the time already executed.

[0066] In the embodiments of this application, the test progress measurement module 200 mainly implements the following five functions:

[0067] 1) When the progress metric is triggered during server testing, first read all the plans and tasks of the server test for this project, and then determine the number of times each test case needs to be executed.

[0068] 2) Calculate the quantized time for each test case. Based on the historical execution data of the test case, the average execution time, the test pass rate, and the duration coefficient X and pass rate coefficient Y, calculate the quantized time required to execute the test case. The calculation formula is: average execution time × (X + (Y ÷ pass rate)).

[0069] 3) After calculating the quantized time of each use case in sequence, multiply the quantized time of all use cases in the plan by the number of executions and sum them up to obtain the total quantized time for the plan to complete the requirements.

[0070] 4) Following the same logic as step 3, calculate the total quantized time for all completed test cases and the time taken to complete each test case. Divide the actual execution time by the completed quantized time to obtain the deviation between the quantized time and the overall execution time of the project.

[0071] 5) Divide the completed quantified time by the total planned quantified time to get the current project progress; multiply the total planned quantified time by the execution deviation, and then subtract the time already completed to get the remaining planned time.

[0072] In the embodiments of this application, when the algorithm parameter iterative optimization module 300 adjusts the duration coefficient X and the pass rate coefficient Y, it increases the duration coefficient X when the average duration trend is increasing, decreases the duration coefficient X when the average duration trend is decreasing, increases the pass rate coefficient Y when the pass rate trend is increasing, and decreases the pass rate coefficient Y when the pass rate trend is decreasing.

[0073] The machine learning-based server test measurement management system of this invention can digitize testing experience and personal experience, and rely on data analysis to evaluate server test progress rather than experience; it continuously accumulates test data and server test measurement data, analyzes the errors, and continuously optimizes the analysis algorithm and calculation formula to further improve the accuracy of server test measurement, providing more accurate data for the next step of planning and task allocation.

[0074] Please see Figure 2 , Figure 2 This is a flowchart illustrating a machine learning-based server test metric management method according to an embodiment of the present invention, as shown below. Figure 2 As shown, in one embodiment of the present invention, a machine learning-based server test metric management method is provided, which includes steps one to six.

[0075] Step 1: Query and obtain all test cases to be executed and the number of times each test case needs to be executed;

[0076] Step 2: Traverse the test cases and obtain test case details. Query the historical execution data of the test case based on its unique identifier, the test case number.

[0077] Step 3: Calculate the average execution time of the test cases, the pass rate of the test cases, and the quantized time of each test case. Based on the historical execution data, average execution time, test pass rate, and the time coefficient X and pass rate coefficient Y of the test case, calculate the quantized time required to execute the test case.

[0078] Step 4: After calculating the quantized time for each use case in sequence, multiply the quantized time of all use cases in the plan by the number of executions and sum them up to obtain the total quantized time for the plan to complete the requirements.

[0079] Step 5: Based on Step 3 and Step 4, calculate the total quantization time and the time of each completed test case in sequence. Divide the actual execution time by the completed quantization time to obtain the deviation between the quantization time and the execution time of the project.

[0080] Step 6: Divide the completed quantified time by the total planned quantified time to obtain the current project progress; multiply the total planned quantified time by the execution deviation, and then subtract the completed time to obtain the remaining planned time.

[0081] In an embodiment of the present invention, the query of the historical execution data of the use case includes the execution time and the execution result.

[0082] In the embodiments of this application, when calculating the average execution time of a use case, the average execution time of the use case is obtained by summing the total execution time of the use case and dividing by the number of executions.

[0083] In the embodiments of this application, when calculating the pass rate of a test case, the pass rate of the test case is obtained by dividing the number of times the test case that passed by PASS is executed by the total number of executions.

[0084] In the embodiments of this application, when calculating the quantified time required to execute the use case, the calculation formula is average execution time × (X + (Y ÷ pass rate)) × number of use case executions.

[0085] The present invention provides a server test measurement management method and system based on machine learning. It builds a server test management platform based on the Spring, Spring Boot and MyBatis frameworks, uses independent measurement algorithms and calculation formulas to accurately calculate the server test progress, and relies on the idea of ​​machine learning to continuously improve the parameters in the formulas and algorithms to improve the measurement accuracy.

[0086] The invented machine learning-based server test measurement method relies on machine learning principles and techniques to construct an accurate and efficient algorithm and formula for measuring server test progress. Furthermore, this algorithm and formula can be continuously improved and adjusted as test data accumulates. Ultimately, it can predict subsequent server test plans and task arrangements, providing significant convenience to testers.

[0087] It should be understood that although the above description follows a certain order, these steps are not necessarily executed in that order. Unless otherwise expressly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, some steps in this embodiment may include multiple steps or multiple stages, which are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least a portion of the steps or stages in other steps.

[0088] In one embodiment, see Figure 3As shown, an embodiment of the present invention also provides a computer device 1000, including at least one processor 1001 and a memory 1002 communicatively connected to the at least one processor 1001. The memory 1002 stores instructions executable by the at least one processor 1001. The instructions are executed by the at least one processor 1001 to cause the at least one processor 1001 to execute the machine learning-based server test metric management method. When the processor 1001 executes the instructions, it implements the steps in the above-described method embodiment:

[0089] 1. Retrieve all test cases to be executed and the number of times each test case needs to be executed.

[0090] 2. Traverse the test cases and obtain test case details. Based on the unique identifier of the test case number, query the historical execution data of the test case, including execution time, execution result, etc.

[0091] 3. Calculate the average execution time of the test case: sum up the total execution time of the test case and divide by the number of executions to get the average execution time of the test case.

[0092] 4. Calculate the pass rate of a test case: Divide the number of times a test case that passes by the total number of times it is executed to get the pass rate of that test case.

[0093] 5. Calculate the quantized time for each test case. Based on the historical execution data of the test case, the average execution time, the test pass rate, and the duration coefficient X and pass rate coefficient Y, calculate the quantized time required to execute the test case. The calculation formula is: average execution time × (X + (Y ÷ pass rate)) × number of test case executions.

[0094] 6. After calculating the quantized time for each use case in sequence, multiply the quantized time of all use cases in the plan by the number of executions and sum them up to obtain the total quantized time for the plan to complete the requirements.

[0095] 7. Following the same logic as 3-6, calculate the total quantization time and execution time of all completed test cases in sequence. Divide the actual execution time by the completed quantization time to obtain the deviation between the quantization time and the overall execution time of the project.

[0096] 8. Divide the completed quantified time by the total planned quantified time to obtain the current project progress; multiply the total planned quantified time by the execution deviation, and then subtract the completed time to obtain the remaining planned time.

[0097] In this context, "computer device," also known as "PC," refers to an intelligent electronic device that can perform predetermined processing procedures such as numerical calculations and / or logical calculations by running predetermined programs or instructions. It may include a processor 1001 and a memory 1002. The processor 1001 executes pre-stored instructions in the memory 1002 to perform the predetermined processing procedures, or the predetermined processing procedures are performed by hardware such as ASICs, FPGAs, and DSPs, or a combination of both. Computer device 1000 includes, but is not limited to, servers, personal computers, laptops, tablets, and smartphones.

[0098] The computer device 1000 includes user equipment and network equipment. The user equipment includes, but is not limited to, computers, smartphones, and PDAs. The network equipment includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing, which is a type of distributed computing consisting of a loosely coupled set of computers forming a super virtual computer. The computer device 1000 can operate independently to implement this invention, or it can connect to a network and interact with other computer devices 1000 within the network to implement this invention. The network in which the computer device 1000 is located includes, but is not limited to, the Internet, wide area networks (WANs), metropolitan area networks (MANs), local area networks (LANs), and VPN networks.

[0099] It should also be understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0100] In one embodiment of the present invention, a storage medium storing a computer program is also provided, which, when executed by processor 1001, implements the steps in the above method embodiments:

[0101] 1. Retrieve all test cases to be executed and the number of times each test case needs to be executed.

[0102] 2. Traverse the test cases and obtain test case details. Based on the unique identifier of the test case number, query the historical execution data of the test case, including execution time, execution result, etc.

[0103] 3. Calculate the average execution time of the test case: sum up the total execution time of the test case and divide by the number of executions to get the average execution time of the test case.

[0104] 4. Calculate the pass rate of a test case: Divide the number of times a test case that passes by the total number of times it is executed to get the pass rate of that test case.

[0105] 5. Calculate the quantized time for each test case. Based on the historical execution data of the test case, the average execution time, the test pass rate, and the duration coefficient X and pass rate coefficient Y, calculate the quantized time required to execute the test case. The calculation formula is: average execution time × (X + (Y ÷ pass rate)) × number of test case executions.

[0106] 6. After calculating the quantized time for each use case in sequence, multiply the quantized time of all use cases in the plan by the number of executions and sum them up to obtain the total quantized time for the plan to complete the requirements.

[0107] 7. Following the same logic as 3-6, calculate the total quantization time and execution time of all completed test cases in sequence. Divide the actual execution time by the completed quantization time to obtain the deviation between the quantization time and the overall execution time of the project.

[0108] 8. Divide the completed quantified time by the total planned quantified time to obtain the current project progress; multiply the total planned quantified time by the execution deviation, and then subtract the completed time to obtain the remaining planned time.

[0109] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Furthermore, any references to memory 1002, storage, database, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0111] In summary, the machine learning-based server test measurement management method and system provided by the invention builds a server test management platform based on the Spring, Spring Boot, and MyBatis frameworks. It uses independent measurement algorithms and calculation formulas to accurately calculate server test progress and relies on machine learning ideas to continuously improve the parameters in the formulas and algorithms, thereby improving measurement accuracy.

[0112] This invention mainly involves three components: a test case management module 100, a test progress measurement module 200, and an algorithm parameter iteration and optimization module 300. This invention is a server test measurement method and system based on machine learning, which can accurately measure the server test progress, provide data support for testers such as TSE and VM, and more reasonably improve and adjust subsequent plans and task arrangements, ensuring that the test work can be completed efficiently within the required time, thereby improving test efficiency and test quality.

[0113] Furthermore, the machine learning-based server test measurement method of this invention relies on machine learning concepts and technologies to construct an accurate and efficient algorithm and formula for measuring server test progress. This algorithm and formula can be continuously improved and adjusted as test data accumulates. Ultimately, it can predict subsequent server test plans and task arrangements, providing significant convenience to testers.

[0114] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0115] It should be understood that, as used herein, the singular form "a" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the associatedly listed items. The embodiment numbers disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0116] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A server test metric management system based on machine learning, characterized in that, It includes a test case management module, a test progress measurement module, and an algorithm parameter iteration and optimization module; The test case management module is used to manage and record basic information about the execution of server test cases. The test progress measurement module is used to read all the plans and tasks of the project server test, and to determine the number of times each test case needs to be executed based on all the test cases to be executed. It is also used to calculate the quantization time for each use case and the total quantization time, to obtain the deviation between the quantization time and the project's execution time, as well as the project's current progress; The algorithm parameter iterative optimization module is used to calculate the changing trends of average test duration and pass rate, and adjust the duration coefficient X and pass rate coefficient Y according to the changes in the trends; The test progress measurement module is used to read all the plans and tasks of the server test for the project when the progress measurement is triggered during the server test, and to determine the number of times each test case needs to be executed based on the total number of test cases to be executed. The test progress measurement module is also used to multiply the total quantized time of all test cases in the plan by the number of executions and sum them up after calculating the quantized time of each test case in turn to obtain the total quantized time required to complete the execution of the plan. The test progress measurement module is also used to sequentially calculate the total quantization time of the completed test cases and the time of the completed test cases, and divide the actual execution time by the completed quantization time to obtain the deviation between the quantization time and the execution time of the project.

2. The machine learning-based server test metric management system as described in claim 1, characterized in that, The test case management module records basic information about test case execution, including the number of executions, duration, and pass rate. The test case management module is also used for adding and maintaining test cases.

3. The machine learning-based server test metric management system as described in claim 2, characterized in that, The test progress measurement module is used to read all the plans and tasks of the server test for the project when the progress measurement is triggered during the server test, and to obtain all the test cases to be executed and the number of times each test case needs to be executed.

4. The machine learning-based server test metric management system as described in claim 1, characterized in that, The test progress measurement module is also used to obtain the current project progress by dividing the completed quantified time by the total planned quantified time, and to obtain the remaining planned time by multiplying the total planned quantified time by the execution deviation and then subtracting the time already executed.

5. The machine learning-based server test metric management system as described in claim 1, characterized in that, When adjusting the duration coefficient X and the pass rate coefficient Y, the algorithm parameter iterative optimization module increases the duration coefficient X when the average duration trend is increasing and decreases the duration coefficient X when the average duration trend is decreasing; increases the pass rate coefficient Y when the pass rate trend is increasing and decreases the pass rate coefficient Y when the pass rate trend is decreasing.

6. A server test metric management method based on machine learning, characterized in that, The following steps are performed based on the machine learning-based server test metric management system as described in any one of claims 1 to 5: Step 1: Query and obtain all test cases to be executed and the number of times each test case needs to be executed; Step 2: Traverse the test cases and obtain test case details. Query the historical execution data of the test case based on its unique identifier, the test case number. Step 3: Calculate the average execution time of the test cases, the pass rate of the test cases, and the quantized time of each test case. Based on the historical execution data, average execution time, test pass rate, and the time coefficient X and pass rate coefficient Y of the test case, calculate the quantized time required to execute the test case. Step 4: After calculating the quantized time for each use case in sequence, multiply the quantized time of all use cases in the plan by the number of executions and sum them up to obtain the total quantized time for the plan to complete the requirements. Step 5: Based on Step 3 and Step 4, calculate the total quantization time and the time of each completed test case in sequence. Divide the actual execution time by the completed quantization time to obtain the deviation between the quantization time and the execution time of the project. Step 6: Divide the completed quantified time by the total planned quantified time to obtain the current project progress; multiply the total planned quantified time by the execution deviation, and then subtract the completed time to obtain the remaining planned time.

7. The server test metric management method based on machine learning as described in claim 6, characterized in that, The historical execution data retrieved for this use case includes the execution duration and execution results; When calculating the average execution time of a test case, sum up the total execution time of the test case and divide by the number of executions to get the average execution time of the test case. When calculating the pass rate of a test case, the pass rate is obtained by dividing the number of times the test case that passed by the test case by the total number of times it was executed. When calculating the quantified time required to execute the test case, the formula is: average execution time × (X + (Y ÷ pass rate)) × number of test case executions.

Images