Test case selection method for scientific research after-sales difference analysis based on equipment software

Abstract

The application discloses a test case selection method for scientific research after-sales difference analysis based on equipment software. The differences between scientific research and after-sales stages are quantified in four dimensions of test strategy, test technology and method, test environment and test data to quantify the stage differences, and then the test cases reusable for the improvement of the after-sales stage are matched from the existing case result library of the scientific research stage, so that the problems of lack of pertinence of case selection for the scientific research and after-sales stages of equipment software, single quantitative dimension and inability of continuously improving the reuse accuracy caused by the fact that the differences between the scientific research and after-sales stages are not systematically distinguished and modeled in the related technologies are solved, the cases in the existing case result library of the scientific research stage can be fully and effectively utilized, and the repetitive test design work is reduced.

Description

Technical Field

[0001] This application belongs to the field of software testing technology, and in particular relates to a test case selection method for scientific research and post-sales difference analysis of equipment software. Background Technology

[0002] Equipment software lifecycles include a research and development phase and an after-sales phase. The differences in core objectives and application scenarios between these two phases lead to differences in testing objectives. In the research and development phase, equipment software undergoes functional iteration and performance optimization according to requirements. The core testing objective is to verify the completeness of functions, the compliance of technical indicators, and to identify potential design flaws, ensuring the quality of the delivered software. In the after-sales phase, the software has been delivered and is in use. The testing objective shifts to solving actual user problems, conducting targeted repairs and verifications for faults, ensuring product stability, and achieving user satisfaction.

[0003] However, current testing optimization focuses only on a single phase, and quantitative methods often concentrate on a single dimension of functional matching, lacking systematic modeling of differences across multiple dimensions such as testing strategies, methods, environments, and data. Furthermore, existing technologies lack analysis and dissection of the fundamental differences between the research and development phases of software testing and the post-sales phase, and fail to quantify these differences to aid in decision-making and design for test case selection. This results in test case design lacking specificity and exhibiting insufficient test case adaptability. Summary of the Invention

[0004] This application provides a method for selecting test cases for scientific research and after-sales difference analysis based on equipment software.

[0005] In a first aspect, embodiments of this application provide a method for selecting test cases based on the post-sales difference analysis of equipment software, including: Based on the zeroing report, fault information is determined according to a preset hierarchical structure. The fault information includes the functional point information of the functional sub-module where the fault occurred and a list of problems of the target functional point. Based on the analysis of the test differences between the research and development stage and the after-sales stage of equipment software in preset dimensions, a quantitative relationship table of level mapping corresponding to each feature item in multiple dimensions is established. The preset dimensions include at least test strategies, test methods and technologies, test environment, and test data. Based on the fault information and the grade mapping quantification relationship table, query the research stage quantification value and after-sales stage quantification value of the matching feature item from the grade mapping quantification relationship table; Based on the quantitative values ​​of the research stage and the after-sales stage, calculate the degree of difference between the scores of each dimension of the research stage and the scores of each dimension of the after-sales stage. Extract the set of test cases for the research phase corresponding to the fault information from the test case library and their covered function point tags, and calculate the functional matching degree of the corresponding test cases based on the tags and the target function point problem list; Based on the degree of difference and functional matching, a matching test case reuse strategy is determined to screen out target test cases that can be reused in the after-sales stage and meet the preset conditions.

[0006] Optionally, the step of calculating the degree of difference between the scores of each dimension of the research stage and the scores of each dimension of the after-sales stage based on the quantitative values ​​of the research stage and the after-sales stage includes: Using the coefficient of variation method, based on the quantitative values ​​of the scientific research stage and the after-sales stage, the feature weights of each feature item and the dimensional weights of each dimension are calculated. Based on the feature item weights and dimension weights, calculate the scores of each dimension in the research stage and the scores of each dimension in the after-sales stage, and then calculate the comprehensive score of the difference between the research stage and the after-sales stage. The overall difference score is compared with a preset threshold to determine the corresponding difference level, which includes high difference, medium difference, and low difference.

[0007] Optionally, the step of calculating the scores for each dimension in the research phase and the scores for each dimension in the after-sales phase based on the feature item weights and dimension weights, and then calculating the comprehensive score of the difference between the research phase and the after-sales phase, includes: The scores for each dimension of the research phase, the scores for each dimension of the after-sales phase, and the comprehensive score for the difference between the research phase and the after-sales phase are calculated using the following formulas:

[0008]

[0009]

[0010]

[0011]

[0012] in, Indicates the first In the dimension of the first The weights of each feature term, Indicates the first The number of feature terms in each dimension Indicates the first The average weight of multiple feature terms in each dimension. This represents the normalized dimension weights. , This indicates the scores for each dimension during the research phase. This indicates the scores for each dimension during the after-sales phase. Indicates the first In the dimension of the first Quantitative values ​​of each characteristic item at the research stage. Indicates the first In the dimension of the first Quantitative values ​​of each characteristic item in the after-sales stage. The score represents the overall difference between the research and development stage and the after-sales stage.

[0013] Optionally, after selecting reusable target test cases that meet preset conditions for the after-sales phase, the method further includes: In response to the completion of the target test case, during the after-sales test task archiving stage, the indicator data of the target test case is calculated. The indicator data includes reuse success rate, modification time weighting value, test effectiveness, and test case adaptability. The indicator data is preprocessed to obtain preprocessed indicator data; Based on the preprocessed index data, determine whether the objective function meets the preset training termination condition; If the objective function does not meet the preset training termination condition, the feature item weights and dimension weights are updated by gradient descent, and the process returns to the step of determining a matching test case reuse strategy based on the difference level and functional matching degree to filter out target test cases that can be reused in the after-sales stage and meet the preset conditions, until the preset training termination condition is met.

[0014] Optionally, the preset hierarchical structure includes: system, subsystem, functional module, functional sub-module, functional point, and problem description.

[0015] Optionally, the features of the test strategy dimension should at least include the test scope and driving factors; the features of the test method and technology dimension should at least include the test method, test technology, and test type; the features of the test environment dimension should at least include the hardware environment, software environment, and network environment; and the features of the test data dimension should at least include the data source and data volume.

[0016] Secondly, embodiments of this application provide a test case selection device for scientific research and post-sales difference analysis based on equipment software, comprising: The determination module is used to determine fault information according to the zeroing report and a preset hierarchical structure. The fault information includes the function point information of the functional sub-module where the fault occurred and a list of problems of the target function point. A module is established to analyze the differences in testing between the research and development phase and the after-sales phase of equipment software in preset dimensions, and to establish a quantitative relationship table of level mapping for each feature item in multiple dimensions. The preset dimensions include at least testing strategies, testing methods and technologies, testing environment, and testing data. The query module is used to query the research stage quantification value and after-sales stage quantification value of the matching feature item from the level mapping quantification relationship table based on the fault information and the level mapping quantification relationship table. The first calculation module is used to calculate the degree of difference between the scores of each dimension of the scientific research stage and the scores of each dimension of the after-sales stage based on the quantitative values ​​of the scientific research stage and the quantitative values ​​of the after-sales stage. The second calculation module is used to extract the set of test cases for the scientific research stage corresponding to the fault information and their covered function point tags from the test case library, and to calculate the functional matching degree of the corresponding test cases based on the tags and the target function point problem list. The filtering module is used to determine a matching test case reuse strategy based on the degree of difference and functional matching, so as to filter out target test cases that can be reused in the after-sales stage and meet the preset conditions.

[0017] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the steps of the test case selection method for scientific research post-sales difference analysis based on equipment software as described in any embodiment of the first aspect.

[0018] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps of the test case selection method for scientific research and post-sales difference analysis based on equipment software as described in any embodiment of the first aspect.

[0019] Fifthly, embodiments of this application provide a computer program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the test case selection method for scientific research post-sales difference analysis based on equipment software provided in the first aspect of embodiments of this application.

[0020] The test case selection method based on the research and after-sales difference analysis of equipment software in this application embodiment quantifies the differences between the research and after-sales stages from four dimensions: test strategy, test technology and method, test environment, and test data. By quantifying the stage differences, reusable test cases for the current improvement in the after-sales stage are matched from the existing test case results library of the research stage. This effectively solves the problems in related technologies where the lack of systematic differentiation and systematic modeling of the differences between the research and after-sales stages leads to a lack of specificity in the selection of test cases for the research and after-sales stages of equipment software, a single quantification dimension, and an inability to continuously improve the accuracy of reuse. This allows the test cases in the existing test case results library of the research stage to be fully and effectively utilized, reducing repetitive test design work. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a test case selection method for scientific research and after-sales difference analysis based on equipment software, provided in an embodiment of this application. Figure 2 This is a schematic diagram of a test case selection device for scientific research and after-sales difference analysis based on equipment software, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0023] Figure label: The test case selection module based on the research and after-sales difference analysis of equipment software includes: device 200, module 201, module 202, query module 203, first calculation module 204, second calculation module 205, and filtering module 206. Electronic device 300, processor 301, memory 302, communication interface 303, bus 310. Detailed Implementation

[0024] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0025] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0026] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0027] Equipment software lifecycles include a research and development phase and an after-sales phase. The differences in core objectives and application scenarios between these two phases lead to differences in testing objectives. In the research and development phase, equipment software undergoes functional iteration and performance optimization according to requirements. The core testing objective is to verify the completeness of functions, the compliance of technical indicators, and to identify potential design flaws, ensuring the quality of the delivered software. In the after-sales phase, the software has been delivered and is in use. The testing objective shifts to solving actual user problems, conducting targeted repairs and verifications for faults, ensuring product stability, and achieving user satisfaction.

[0028] Specifically, software testing during the research and development phase focuses on verifying the technical implementation of equipment software "from scratch." The core objective is to ensure that the software is 100% implemented according to functional requirements and design specifications. Test case design aims for "full coverage and in-depth verification." Test cases are designed using testing techniques such as equivalence class partitioning, boundary value analysis, guessing, and exploratory testing, covering functional testing, boundary testing, performance testing, interface testing, human-computer interaction testing, stress testing, margin testing, security testing, and recovery testing. The test environment and test data typically use simulated environments and simulated data.

[0029] Software testing in the after-sales phase focuses on fault repair and stability assurance of equipment software "from existence to excellence". The core objective is to focus on verifying the repair effect. Test case design takes "precision and efficiency, ensuring repair effect" as the core objective. Test cases are designed based on user problems and the scope of impact, based on user operation paths, historical defect patterns and typical scenario processes. Functional testing and performance testing are usually carried out. Human-computer interaction interface testing and stress testing are carried out according to the problems raised. The test environment and test data usually adopt the actual installation environment and real data.

[0030] However, current testing optimization focuses only on a single phase, and quantitative methods often concentrate on a single dimension of functional matching, lacking systematic modeling of differences across multiple dimensions such as testing strategies, methods, environments, and data. Furthermore, existing technologies lack analysis and dissection of the fundamental differences between the research and development phases of software testing and the post-sales phase, and fail to quantify these differences to aid in decision-making and design for test case selection. This results in test case design lacking specificity and exhibiting insufficient test case adaptability.

[0031] Furthermore, testing in the research and development phase and the after-sales phase are usually conducted by different organizations or personnel. The test case sets formed after testing of the equipment software during the research phase lack a knowledge accumulation mechanism for the test data from both phases and are not effectively correlated with the phase characteristics. This leads to a disconnect between the test cases in the research and after-sales phases. Each test requires manual screening from the results library, which is not only time-consuming but also prone to missing key test cases. Consequently, the test case sets designed in the research phase cannot be efficiently reused in the after-sales phase, resulting in low utilization of existing results and increased repetitive work. In addition, existing technologies lack a closed-loop logic for back-optimizing the weights of difference features from the test case selection results, making it impossible to continuously improve the accuracy of test reuse.

[0032] To address the problems in related technologies, this application provides a method for selecting test cases based on the post-sales difference analysis of equipment software.

[0033] The following, in conjunction with the accompanying drawings, provides a detailed explanation of the test case selection method for the scientific research and after-sales difference analysis based on equipment software provided in this application, through specific embodiments and application scenarios.

[0034] Figure 1 This document illustrates a flowchart of a test case selection method for post-sales performance differentiation analysis of equipment software, based on an embodiment of this application. Figure 1 As shown, the test case selection method for the research and development post-sales difference analysis based on equipment software can specifically include the following steps: S101. Based on the zeroing report, determine the fault information according to the preset hierarchical structure. The fault information includes the functional point information of the functional sub-module where the fault occurred and the list of problems of the target functional point. S102. Based on the analysis of the test differences between the research and development stage and the after-sales stage of equipment software in preset dimensions, establish a level mapping quantitative relationship table corresponding to each feature item in multiple dimensions. The preset dimensions include at least test strategies, test methods and technologies, test environment, and test data. S103. Based on the fault information and the grade mapping quantification relationship table, query the research stage quantification value and after-sales stage quantification value of the matching feature item from the grade mapping quantification relationship table. S104. Based on the quantitative values ​​of the research stage and the after-sales stage, calculate the degree of difference between the scores of each dimension of the research stage and the scores of each dimension of the after-sales stage. S105. Extract the set of test cases for the research phase corresponding to the fault information and their covered function point tags from the test case library, and calculate the functional matching degree of the corresponding test cases based on the tags and the target function point problem list. S106. Based on the degree of difference and functional matching, determine a matching test case reuse strategy to screen out target test cases that can be reused in the after-sales stage and meet the preset conditions.

[0035] Therefore, the test case selection method based on the difference analysis between scientific research and post-sales of equipment software quantifies the differences between the scientific research and post-sales stages from four dimensions: test strategy, test technology and methods, test environment, and test data. By quantifying the stage differences, reusable test cases for the current improvement in the post-sales stage are matched from the existing test case results library of the scientific research stage. This effectively solves the problems in related technologies where the differences between the scientific research and post-sales stages are not systematically distinguished and systematically modeled, resulting in a lack of specificity in the selection of test cases for the scientific research and post-sales stages of equipment software, a single quantification dimension, and an inability to continuously improve the accuracy of reuse. This allows the test cases in the existing test case results library of the scientific research stage to be fully and effectively utilized, reducing repetitive test design work.

[0036] The specific implementation methods for each of the above steps are described below.

[0037] In some embodiments, in S101, based on the zeroing report of this improvement, the specific problem description in the solution, the improvement content, and the statistics of historical problems, according to a preset hierarchical structure, namely "Project Name (System) - Subsystem - Functional Module - Functional Sub-Module - Functional Point - [Problem Description 1, Problem Description 2, ... Problem Description n]", the functional point where the fault occurs is first located. For example, for "XXX System - Communication Subsystem - Information Transmission - Voice Communication - External Voice Communication | XX1 Link External Voice Communication Setting Failure", the functional point where the fault occurs is "External Voice Communication". Then, based on the problem description, a list of target functional point problems to be verified is determined, such as [XX1 Link External Voice Communication Setting Failure]. This yields the after-sales fault information.

[0038] In some embodiments, in S102, the differences between the research and development phase and the after-sales phase of the equipment software are determined, and specific feature items are broken down for each dimension. The feature items need to cover the core differences between the two phases to achieve the analysis of the test differences between the research and development phase and the after-sales phase of the equipment software in these dimensions. The differences include differences in testing strategies, testing methods and techniques, testing environment, testing data, and testing processes and activities, as shown in Tables 1-5 below.

[0039] Table 1 Differences in Testing Strategies

[0040] Table 2 Differences in Test Methods and Techniques

[0041] Table 3 Differences in Test Environments

[0042] Table 4. Differences in Test Data

[0043] Table 5 Differences in Testing Procedures and Activities

[0044] Furthermore, in some embodiments, since the test process and activity difference dimensions are used to guide test execution and do not have quantification conditions, feature quantification is performed on the other four dimensions (test strategy, test methods and techniques, test environment, and test data) to establish a level mapping quantification relationship table corresponding to each feature item in multiple dimensions. The specific matching score intervals and descriptions are shown in Tables 6-9 below.

[0045] Table 6. Mapping and Quantification Relationship of Test Strategy Feature Item Levels

[0046] Table 7. Quantitative Relationship between Test Methods and Technical Feature Levels

[0047] Table 8. Mapping and Quantification Relationship of Test Environment Feature Levels

[0048] Table 9. Mapping and Quantification Relationship of Test Data Feature Item Levels

[0049] As can be seen, the grade mapping quantification table is obtained through a five-level division method. Grades 1-5 correspond to 90-100 points, 70-89 points, 50-69 points, 30-49 points, and 0-29 points, respectively. Not all grades need to be selected, with grade 1 best reflecting research characteristics, grade 5 best reflecting after-sales characteristics, and grade 3 serving as a transition. This constructs a four-dimensional difference model encompassing testing strategies, testing methods and techniques, testing environment, and testing data, and refines seven feature items into quantifiable research and after-sales stage feature values ​​based on "grade-score intervals."

[0050] Furthermore, in some embodiments, in S103, the feature values ​​of each dimension of the research stage and the feature values ​​of each dimension of the after-sales stage corresponding to the functional module where the fault occurred are extracted. The feature values ​​of each dimension of the research stage are extracted from the test case library (e.g., in the research stage, a module's test case set is selected as "full coverage" in the "test scope" dimension, determined as "level 1," and set with a score of 95). The feature values ​​of each dimension of the after-sales stage are comprehensively scored based on the zeroing report, specific problem descriptions in the solution, improvement content, and historical problem statistics (e.g., in the after-sales stage, a module is analyzed as "only covering fault-related modules" in the "test scope" dimension, determined as "level 4," and set with a score of 60). This yields the quantitative values ​​for the research stage and the after-sales stage.

[0051] In some embodiments, in S104, the coefficient of variation method is used to calculate the feature weights of each feature item and the dimension weights of each dimension based on the quantitative values ​​of the research stage and the after-sales stage; based on the feature weights and dimension weights, the scores of each dimension in the research stage and the scores of each dimension in the after-sales stage are calculated, and then the comprehensive difference score between the research stage and the after-sales stage is calculated; the comprehensive difference score is compared with a preset threshold to obtain the corresponding difference level, which includes high difference, medium difference, and low difference.

[0052] In practice, the coefficient of variation method is used to determine the weights. The greater the difference between the research and after-sales stages of each dimension (i.e., the higher the coefficient of variation), the more significant the impact of this feature on the stage difference, and the higher the weight.

[0053] First, for each feature item under the difference dimension (the j-th feature item under the i-th dimension), obtain the quantitative value of the research stage. and quantification of the after-sales stage Then the mean can be calculated as It should be noted that if the faulty module has multiple test cases available in the test case library for different research phases of a project, the characteristic value of the research phase in which the faulty module belongs should be considered. It is obtained by taking the average. And, the standard deviation is calculated as follows: It should be understood that the larger the standard deviation, the more significant the difference between the research and after-sales stages of this feature value under that dimension.

[0054] Next, we can calculate the coefficient of variation of the j-th feature term in the i-th dimension: Among them, if =0, then =1 indicates that the feature value has no actual overlap between the research and after-sales stages, and the difference is extremely large.

[0055] Next, the feature term weights are calculated. Feature term weights reflect the contribution of that feature to the variance within its dimension; the higher the coefficient of variation, the higher the weight. The weight of the j-th feature term in the i-th dimension is calculated as follows: ,in, Indicates the first The number of feature terms in each dimension Normalization is performed to ensure that the total weight is 1.

[0056] Further, the dimensional weights are calculated. Dimensional weights reflect the overall contribution of that dimension to the four-dimensional difference model. The calculation involves two steps: first, averaging the weights of all feature terms for each dimension, and then normalizing to obtain the dimensional weights. That is, they can be calculated using the following formula: The mean weight of the feature term in the dimension is: ; Dimension weights are normalized as follows: ,and This is to avoid any one dimension from dominating the difference assessment.

[0057] Therefore, the scores for each dimension of the research stage were calculated as follows: The scores for each dimension during the after-sales stage are as follows: Furthermore, the comprehensive score for the difference between the research and development stage and the after-sales stage was calculated: , The larger the value, the more significant the stage difference. Specifically, by doubling the product, the score is mapped to the 0-100 point range, facilitating grade division.

[0058] Therefore, the overall score is based on the differences. The magnitude of the difference can be used to categorize the degree of difference into different levels, thus achieving a classification of difference levels. Specifically, in When the difference is ≥80, the level of difference is high, indicating that the feature dimension differs greatly between the research and after-sales stages. Priority should be given to use cases specifically designed for the after-sales stage and highly matching this feature. If none are available, significant customization of research use cases should be considered. When the difference is ≤40... When the score is <80, the difference level is considered moderate, indicating a significant difference in this dimension. Use cases that better fit the characteristics of the after-sales stage should be prioritized. If research use cases exist, they can be reused after appropriate modifications. If the difference is less than 40, the difference level is low, indicating that the difference in this dimension is small, and the use cases in the research stage or general use cases can be directly reused.

[0059] In some embodiments, in S105, a unique function point identifier is established for each functional module of the equipment software, in the format: Project Name (System) - Subsystem - Functional Module - Functional Sub-module - Function Point, for example: XXX System - Communication Subsystem - Information Transmission - Voice Communication - External Voice Communication. Then, in the labels of the test cases, the function points covered by the function point identifier are explicitly marked, in the format: Covered Function Points: [Function Point Label 1, Function Point Label 2, ...], for example, a research test case label covers the function points as: [XX1 Link External Voice Communication, XX2 Link External Voice Communication]. For the target function point problem list of the after-sales fault module (e.g., [XX1 Link External Voice Communication Setting Failure]), the number of target function points to be verified after-sales is determined. Based on the target function point problem list to be verified after-sales, the number of matching test cases with the corresponding function points in the library is determined by fuzzy matching and assigned to the number of target function points covered by the test case set. The function matching degree M in the test case library, in units of function points, is calculated. The specific calculation formula is as follows: .

[0060] Taking the total number of target function point issues [XX1 link voice communication setting failure] that need to be verified after-sales as 1, and the number of overlapping function points [XX1 link external voice communication, XX2 link external voice communication] determined by fuzzy matching as 1 in the research use case, then the function matching degree = 1 / 1*100% = 100%, which is a high match.

[0061] Furthermore, in some embodiments, in S106, a two-dimensional filtering rule is constructed based on the degree of difference and functional matching degree. A matching test case reuse strategy is then determined based on this rule, i.e., suitable test cases and modification schemes are selected. Then, relevant impact test cases and typical test cases that meet the conditions are analyzed and selected based on the functional module name and the filled-in impact domain, serving as reusable target test cases for the after-sales stage. Specifically, the two-dimensional filtering rule is shown in Table 10 below.

[0062] Table 10 Two-dimensional filtering rules

[0063] As an optional embodiment, after selecting reusable target test cases that meet preset conditions in the after-sales stage, the method further includes: in response to the completion of the target test cases, during the after-sales test task archiving stage, calculating the indicator data of the target test cases, the indicator data including reuse success rate, modification time weighting value, test effectiveness, and test case adaptability; preprocessing the indicator data to obtain preprocessed indicator data; determining whether the objective function meets the preset training termination condition based on the preprocessed indicator data; if the objective function does not meet the preset training termination condition, updating the feature item weights and dimension weights using gradient descent, and returning to the step of determining a matching test case reuse strategy based on the difference level and functional matching degree to select reusable target test cases that meet preset conditions in the after-sales stage, until the preset training termination condition is met.

[0064] In other words, an iterative mechanism of "indicator feedback - parameter optimization - formula update" is constructed to dynamically optimize the calculation parameters of scientific research difference feature values ​​based on historical selection data, thereby improving the accuracy of subsequent test case selection. The core logic is as follows: by quantifying the key indicators of test case selection results, optimizing dimension weights through gradient descent, adjusting calculation parameters and updating them to the system to support the next round of selection decisions, and further improving the matching degree between the feature values ​​of each dimension in the scientific research stage and the after-sales scenario through iterative optimization.

[0065] In practice, four types of quantifiable indicators were selected, including adaptation efficiency indicators, adaptation cost indicators, test effect indicators, and feature matching indicators, as shown in Table 11. Furthermore, the indicator data was bound and stored with the selected functional modules, their corresponding four-dimensional difference feature values, and the test case sets in the test case library, comprehensively covering the efficiency, effectiveness, adaptability, and economy of test case selection.

[0066] Table 11 Four Types of Quantifiable Indicators

[0067] In practice, after the test cases for this functional module have been executed and the defects have been closed, i.e., during the after-sales test task archiving phase, the reuse success rate is... The system automatically captures and associates test case IDs, functional module identifiers, and corresponding scientific research characteristic values; the modification time and modification level weight of a single test case (used to calculate the weighted value of modification time). Total number of defects in functional modules (used to calculate test effectiveness) This information is added by the testers when they complete the after-sales test archiving.

[0068] In practice, preprocessing the indicator data can include the following steps: First, outlier removal, removing outliers whose transformation time is greater than 3 times the average, and testing effectiveness. Abnormal data such as =0 (due to test execution error) were then processed. Next, the data was normalized, mapping all indicators to the [0,1] interval to eliminate dimensional differences. For example, =85%→0.85, =120 minutes → 120 / MAX_T, where MAX_T is the historical maximum modification time.

[0069] In practical implementation, the objective function G for optimization is constructed as follows: ; in, To increase the success rate of reuse, Weighted value for renovation time. To test validity, For test case fit; , , , The range is (0.1-0.45), which can be adjusted according to the scenario, such as: =0.25、 =0.2、 =0.35、 =0.2.

[0070] In practice, the core objective is to improve the overall optimization target value G, and the weights of certain feature terms in a certain dimension are adjusted during the research phase. With dimensional weights The detailed steps are as follows: First, initialization: The baseline dimension weights are calculated, and their values ​​are... The initial calculation formula for the characteristic value of scientific research differences is as follows: ,in (Weighted score of the research stage in the i-th dimension). For calculating the baseline feature weights .

[0071] Then, partial derivatives are calculated: G is calculated for each partial derivatives It reflects The degree of impact of the change on G. Calculate G for each partial derivatives It reflects The extent to which the change affects G.

[0072] Furthermore, the weights are updated: , , where n is the learning rate, set to 0.05, which can be adjusted to control the adjustment range.

[0073] Therefore, constraint verification: ensure 0.1 ≤ ≤0.4, if outside the range, take the boundary value, while ensuring (Normalization adjustment); Ensure 0 ≤ ≤1, and satisfy "all under the same dimension" The constraint that "the sum of all equals 1" applies; if the value exceeds the range, the boundary value is used (e.g., ...). After calculation, if the value is greater than 1, take 1; if it is less than 0, take 0. Then normalize the weights of all feature terms under this dimension.

[0074] In some optional embodiments, the triggering conditions include starting the iteration when any of the following conditions are met: the comprehensive target value G selected in a single instance is less than 0.6 (out of 1.0, and values ​​below the threshold are considered poor); or, after three consecutive selections, the cumulative increase in G is less than 10% (the optimization has reached a bottleneck); or, a new equipment software type is added (the functional module is included in the selection range for the first time, and there is no historical data).

[0075] Therefore, iteration is achieved through the following steps: reading the identifier of the selected functional module and the scores of each dimension corresponding to the research stage. 4 types of indicator data ( , , , ), Initialize baseline value , , Substitute the values ​​into the G formula to obtain the current performance score, compare it with the historical baseline value, and determine whether the optimization condition has been triggered. If triggered, update the score using gradient descent. , Generate optimized feature values ​​for the research stage. ; Optimized research stage feature values By binding the data to the functional module identifier and updating it to the scientific research feature parameter library, the optimized scientific research feature values ​​will be automatically invoked in the next round of selection for the same / similar functional modules. In this way, after the completion of this after-sales improvement, by collecting feature values ​​and various indicator data of relevant test cases in various dimensions under the scientific research stage, the gradient descent method can be used to dynamically optimize the matching degree of test cases selected in the scientific research stage during the after-sales stage, and continuously improve the accuracy of test case reuse.

[0076] As can be seen, the test case selection method based on the research and post-sales difference analysis of equipment software in this application constructs a test case screening system for equipment software based on "four-dimensional difference quantification + functional matching degree + iterative optimization". It summarizes the differences between the research and post-sales stages of equipment software for the first time, and extracts four dimensions of differences—test strategy, test methods and technologies, test environment, and test data—for feature quantification and weight modeling to quantify stage differences; it establishes a two-dimensional screening rule based on the four-dimensional difference model and functional matching degree to achieve effective reuse and modification of test cases; and it designs a dynamic iterative mechanism with indicator feedback and parameter optimization to enable continuous self-optimization of the system. This method breaks through the limitations of single-dimensional and static rules, and is original in multi-dimensional difference modeling and dynamic and accurate test case selection.

[0077] Compared to related technologies, the embodiments of this application establish multi-dimensional difference quantification and intelligent decision-making for the research and after-sales stages, completely solving the problems of accuracy in test case selection and insufficient use of test case result libraries in the research and after-sales stages of equipment software. It realizes the transformation of test cases from reliance on manual experience to data-driven precise matching. In terms of scope of application, this application can be used for various equipment software test case selection scenarios, greatly improving the efficiency of test case reuse across stages and projects. In terms of long-term value, the closed-loop iteration mechanism continuously optimizes the test case selection logic, which not only significantly reduces testing manpower and time costs, but also improves the efficient reuse of testing assets, reduces repetitive work, and effectively ensures the high reliability of equipment software throughout its entire life cycle, thus having practical engineering value.

[0078] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] Based on the same technical concept, and corresponding to any of the above embodiments, this application also provides a test case selection device 200 for scientific research and post-sales difference analysis of equipment software.

[0080] like Figure 2 As shown, the test case selection device 200 for the scientific research and post-sales difference analysis based on equipment software may include: The determination module 201 is used to determine fault information according to the zeroing report and a preset hierarchical structure. The fault information includes the function point information of the functional sub-module where the fault occurred and a list of problems of the target function point. Module 202 is established to establish a level mapping quantitative relationship table corresponding to each feature item in multiple dimensions based on the test difference analysis of the research and development stage and the after-sales stage of equipment software in preset dimensions. The preset dimensions include at least test strategies, test methods and technologies, test environment and test data. The query module 203 is used to query the scientific research stage quantification value and the after-sales stage quantification value of the matching feature item from the level mapping quantification relationship table based on the fault information and the level mapping quantification relationship table. The first calculation module 204 is used to calculate the degree of difference between the scores of each dimension of the scientific research stage and the scores of each dimension of the after-sales stage based on the quantitative values ​​of the scientific research stage and the quantitative values ​​of the after-sales stage. The second calculation module 205 is used to extract the set of test cases for the scientific research stage corresponding to the fault information and the labels of the functional points it covers from the test case library, and to calculate the functional matching degree of the corresponding test cases based on the labels and the target functional point problem list. The filtering module 206 is used to determine a matching test case reuse strategy based on the difference level and functional matching degree, so as to filter out target test cases that can be reused in the after-sales stage and meet the preset conditions.

[0081] It should be noted that, for ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0082] The apparatus described above is used to implement the test case selection method for the scientific research after-sales difference analysis based on equipment software in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0083] Based on the same technical concept, corresponding to any of the above embodiments, this application also provides an electronic device.

[0084] Figure 3 A schematic diagram of a more specific electronic device hardware structure provided in this embodiment is shown.

[0085] The electronic device 300 may include a processor 301 and a memory 302 storing computer program instructions.

[0086] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0087] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 302 is non-volatile solid-state memory.

[0088] In certain embodiments, the memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Thus, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this application.

[0089] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any of the test case selection methods for scientific research and post-sales difference analysis based on equipment software in the above embodiments.

[0090] In some examples, the electronic device 300 may also include a communication interface 303 and a bus 310. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0091] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0092] Bus 310 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not as a limitation, bus 310 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0093] For example, the electronic device 300 can be a mobile phone, tablet computer, laptop computer, handheld computer, in-vehicle electronic device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc.

[0094] Based on the same technical concept, corresponding to any of the methods in the above embodiments, this application also provides a non-transitory computer-readable storage medium. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the test case selection methods for post-sales difference analysis of equipment software based on the above embodiments. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, etc.

[0095] Based on the same technical concept, corresponding to any of the above embodiments, this application also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to perform the test case selection method for the research and development after-sales difference analysis based on equipment software. Corresponding to the execution entity for each step in each embodiment of the test case selection method for the research and development after-sales difference analysis based on equipment software, the processor executing the corresponding step can belong to the corresponding execution entity.

[0096] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0097] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0098] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0099] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0100] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for selecting test cases for post-sales difference analysis of equipment software, characterized in that, include: Based on the zeroing report, fault information is determined according to a preset hierarchical structure. The fault information includes the functional point information of the functional sub-module where the fault occurred and a list of problems of the target functional point. Based on the analysis of the test differences between the research and development stage and the after-sales stage of equipment software in preset dimensions, a quantitative relationship table of level mapping corresponding to each feature item in multiple dimensions is established. The preset dimensions include at least test strategies, test methods and technologies, test environment, and test data. Based on the fault information and the grade mapping quantification relationship table, query the research stage quantification value and after-sales stage quantification value of the matching feature item from the grade mapping quantification relationship table; Based on the quantitative values ​​of the research stage and the after-sales stage, calculate the degree of difference between the scores of each dimension of the research stage and the scores of each dimension of the after-sales stage. Extract the set of test cases for the research phase corresponding to the fault information from the test case library and their covered function point tags, and calculate the functional matching degree of the corresponding test cases based on the tags and the target function point problem list; Based on the degree of difference and functional matching, a matching test case reuse strategy is determined to screen out target test cases that can be reused in the after-sales stage and meet the preset conditions.

2. The method according to claim 1, characterized in that, The step of calculating the degree of difference between the scores of each dimension in the research stage and the scores of each dimension in the after-sales stage based on the quantitative values ​​of the research stage and the after-sales stage includes: Using the coefficient of variation method, based on the quantitative values ​​of the scientific research stage and the after-sales stage, the feature weights of each feature item and the dimensional weights of each dimension are calculated. Based on the feature item weights and dimension weights, calculate the scores of each dimension in the research stage and the scores of each dimension in the after-sales stage, and then calculate the comprehensive score of the difference between the research stage and the after-sales stage. The overall difference score is compared with a preset threshold to determine the corresponding difference level, which includes high difference, medium difference, and low difference.

3. The method according to claim 2, characterized in that, The step involves calculating the scores for each dimension in the research phase and the scores for each dimension in the after-sales phase based on the feature item weights and dimension weights, and then calculating the comprehensive score reflecting the difference between the research phase and the after-sales phase, including: The scores for each dimension of the research phase, the scores for each dimension of the after-sales phase, and the comprehensive score for the difference between the research phase and the after-sales phase are calculated using the following formulas: in, Indicates the first In the dimension of the first The weights of each feature term, Indicates the first The number of feature terms in each dimension Indicates the first The average weight of multiple feature terms in each dimension. This represents the normalized dimension weights. , This indicates the scores for each dimension during the research phase. This indicates the scores for each dimension during the after-sales phase. Indicates the first In the dimension of the first Quantitative values ​​of each characteristic item at the research stage. Indicates the first In the dimension of the first Quantitative values ​​of each characteristic item in the after-sales stage. The score represents the overall difference between the research and development stage and the after-sales stage.

4. The method according to claim 2, characterized in that, After selecting reusable target test cases that meet preset conditions for the after-sales phase, the method further includes: In response to the completion of the target test case, during the after-sales test task archiving stage, the indicator data of the target test case is calculated. The indicator data includes reuse success rate, modification time weighting value, test effectiveness, and test case adaptability. The indicator data is preprocessed to obtain preprocessed indicator data; Based on the preprocessed index data, determine whether the objective function meets the preset training termination condition; If the objective function does not meet the preset training termination condition, the feature item weights and dimension weights are updated by gradient descent, and the process returns to the step of determining a matching test case reuse strategy based on the difference level and functional matching degree to filter out target test cases that can be reused in the after-sales stage and meet the preset conditions, until the preset training termination condition is met.

5. The method according to claim 1, characterized in that, The preset hierarchical structure includes: system, subsystem, functional module, functional sub-module, functional point, and problem description.

6. The method according to claim 1, characterized in that, The characteristics of the test strategy dimension should at least include the test scope and driving factors; the characteristics of the test method and technology dimension should at least include the test method, test technology, and test type; the characteristics of the test environment dimension should at least include the hardware environment, software environment, and network environment; and the characteristics of the test data dimension should at least include the data source and data volume.

7. A test case selection device for scientific research and post-sales difference analysis based on equipment software, characterized in that, include: The determination module is used to determine fault information according to the zeroing report and a preset hierarchical structure. The fault information includes the function point information of the functional sub-module where the fault occurred and a list of problems of the target function point. A module is established to analyze the differences in testing between the research and development phase and the after-sales phase of equipment software in preset dimensions, and to establish a quantitative relationship table of level mapping for each feature item in multiple dimensions. The preset dimensions include at least testing strategies, testing methods and technologies, testing environment, and testing data. The query module is used to query the research stage quantification value and after-sales stage quantification value of the matching feature item from the level mapping quantification relationship table based on the fault information and the level mapping quantification relationship table. The first calculation module is used to calculate the degree of difference between the scores of each dimension of the scientific research stage and the scores of each dimension of the after-sales stage based on the quantitative values ​​of the scientific research stage and the quantitative values ​​of the after-sales stage. The second calculation module is used to extract the set of test cases for the scientific research stage corresponding to the fault information and their covered function point tags from the test case library, and to calculate the functional matching degree of the corresponding test cases based on the tags and the target function point problem list. The filtering module is used to determine a matching test case reuse strategy based on the degree of difference and functional matching, so as to filter out target test cases that can be reused in the after-sales stage and meet the preset conditions.

8. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; when the processor invokes the computer program instructions, it implements the test case selection method for scientific research post-sales difference analysis based on equipment software as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when invoked by a processor, implement the test case selection method for scientific research post-sales difference analysis based on equipment software as described in any one of claims 1-6.

10. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the test case selection method for scientific research and post-sales difference analysis based on equipment software as described in any one of claims 1-6.

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