Test data verification method, apparatus, device, and product

Abstract

This application discloses a test data verification method, apparatus, device, and product, relating to the field of data testing technology and applied to a data testing platform. The data testing platform includes an auxiliary execution plugin. The method uses the auxiliary execution plugin to capture user-triggered information retrieval commands, and obtains target test case information based on the information retrieval commands. Natural language processing is performed on the target test case information to obtain case steps and business characteristics, and basic test data and business test data are matched according to the business characteristics. Data testing is performed on the basic test data and business test data through the case steps to obtain test results. The auxiliary execution plugin parses the case steps to obtain expected result fields, and logical verification is performed on the test results based on the expected result fields to obtain verification results, thereby improving the verification efficiency of test data verification.

Description

Technical Field

[0001] This application relates to the field of data testing technology, and in particular to a test data verification method, apparatus, equipment, and product. Background Technology

[0002] Currently, testing in the financial sector is still mainly done manually. The testing process relies on manual completion of data preparation, case execution, result verification, and asset query. Some long-process business is difficult to automate due to its complexity and numerous steps. At the same time, test data needs to be manually created and maintained in conjunction with business scenarios. Test assets such as operation manuals, API documents, and parameter configurations are stored in various platforms. Test tools and functions are mostly fixed and do not provide flexible adaptation and expansion capabilities for different products. Overall, it relies on manual operation and experience.

[0003] However, the aforementioned traditional testing methods have many shortcomings. The overall level of intelligence and adaptability is low. Not only are there many manual operation steps and low efficiency in data preparation and asset retrieval, but there are also problems such as insufficient coverage of automated verification and easy errors in result verification. In addition, the tools are difficult to adapt flexibly to the business needs of different products, which ultimately leads to long testing cycles, high labor costs, and difficulty for non-core personnel to get started, and cannot meet the requirements of efficient, accurate and scalable intelligent testing.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a test data verification method, apparatus, equipment and product, which aims to solve the technical problem that data testing is highly dependent on manual labor and has poor tool compatibility, resulting in low data testing efficiency.

[0006] To achieve the above objectives, this application proposes a test data verification method, which is applied to a data testing platform. The data testing platform includes an auxiliary execution plugin, and the test data verification method includes: The auxiliary execution plugin captures user-triggered information retrieval commands and obtains target test case information based on these commands. Natural language processing is performed on the target test case information to obtain the case steps and business characteristics, and basic test data and business test data are matched according to the business characteristics; The basic test data and business test data are tested through the steps described in the case study to obtain test results. The expected result field is obtained by parsing the case steps through the auxiliary execution plugin, and the test result is logically verified based on the expected result field to obtain the verification result.

[0007] In one embodiment, before the step of capturing user-triggered information retrieval commands through the auxiliary execution plugin and obtaining target test case information based on the information retrieval commands, the method further includes: Receive the user's login request, verify the login request, and obtain the login verification result; If the login verification result is successful, load the user's plugin configuration; The page style, plugin function priority, and display scope of the auxiliary execution plugin are determined based on the plugin configuration. The auxiliary execution plugin was adjusted by modifying the page style, plugin function priority, and display range to obtain the adjustment result.

[0008] In one embodiment, the step of capturing user-triggered information retrieval commands through the auxiliary execution plugin and obtaining target test case information based on the information retrieval commands includes: The auxiliary execution plugin is used to perform page inspection on the test execution page; When a user-triggered information capture command is detected on the test execution page, elements are captured on the test execution page to obtain test case association information and test user information; The test case association information and test user information are integrated to obtain the target test case information.

[0009] In one embodiment, the step of performing natural language processing on the target test case information to obtain test case steps and business characteristics, and matching basic test data and business test data based on the business characteristics, includes: The target test case information is analyzed using natural language processing to obtain the test case text; The case steps are determined based on the case text, and features are extracted from the case text to obtain business features; The test data pool is compared and matched using the aforementioned business characteristics to obtain matching results; If the matching result is successful, then the corresponding basic test data and business test data are extracted from the test data pool based on the matching result; If the matching result is unsuccessful, then based on the business characteristics, the auxiliary execution plugin is used to call the data generation script to perform simulation data generation, thereby obtaining basic test data and business test data.

[0010] In one embodiment, the step of performing data testing on the basic test data and business test data through the case steps to obtain test results includes: A standardized testing process is generated based on the steps described in the case study. The basic test data and business test data are tested according to the standardized test process to obtain the original test results. The original test results are cleaned and standardized to obtain the final test results.

[0011] In one embodiment, the step of parsing the case steps to obtain the expected result field through the auxiliary execution plugin, and performing logical verification on the test result based on the expected result field to obtain the verification result includes: The auxiliary execution plugin extracts the original expected content from the target test case information; The auxiliary execution plugin performs semantic parsing on the case steps to obtain the verification dimensions and inspection items; Generate the expected result field based on the original expected content; Based on the verification dimensions and inspection items, the expected result field and the test result are verified item by item to obtain the verification result.

[0012] In one embodiment, after the steps of parsing the case steps to obtain the expected result field through the auxiliary execution plugin, and performing logical verification on the test result based on the expected result field to obtain the verification result, the method further includes: The verification results are classified and statistically analyzed to obtain the number of passed items, the number of abnormal items, and the distribution characteristics of abnormal items; The case steps are marked and located based on the distribution characteristics of the anomalies to obtain the anomaly step location results; A test verification report is generated based on the results of the anomaly location. The test verification report is sent back to the user and stored in the data test log.

[0013] Furthermore, to achieve the above objectives, this application also proposes a test data verification device applied to a data testing platform, the data testing platform including an auxiliary execution plugin, and the test data verification device comprising: The capture module is used to capture user-triggered information capture commands through the auxiliary execution plugin, and obtain target test case information based on the information capture commands; The matching module is used to perform natural language processing on the target test case information to obtain the case steps and business characteristics, and to match basic test data and business test data according to the business characteristics. The testing module is used to perform data testing on the basic test data and business test data through the case steps to obtain test results; The verification module is used to parse the case steps through the auxiliary execution plugin to obtain the expected result field, and to perform logical verification on the test result based on the expected result field to obtain the verification result.

[0014] In addition, to achieve the above objectives, this application also proposes a test data verification device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the test data verification method as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the test data verification method described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the test data verification method described above.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: This application proposes a test data verification method, apparatus, device, and product. The method is applied to a data testing platform, which includes an auxiliary execution plugin. The method captures user-triggered information retrieval instructions through the auxiliary execution plugin, obtains target test case information based on the information retrieval instructions, performs natural language processing on the target test case information to obtain case steps and business characteristics, and matches basic test data and business test data according to the business characteristics; performs data testing on the basic test data and business test data through the case steps to obtain test results; parses the case steps through the auxiliary execution plugin to obtain expected result fields, and performs logical verification on the test results according to the expected result fields to obtain verification results. Therefore, by automatically capturing and acquiring test case information through the auxiliary execution plugin, replacing manual collection and reducing manual operations, natural language processing is then used to automatically extract case steps and business characteristics, achieving intelligent matching of test data without the need for manual data creation and maintenance. Finally, the test is automatically executed according to the case steps, and automatic logical verification is performed based on the expected result fields, avoiding errors from manual verification and significantly improving data testing efficiency. This solves the problem of data testing being highly dependent on manual work and having poor tool compatibility, resulting in low data testing efficiency, and improves the verification efficiency of test data. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the test data verification method of this application in Embodiment 1. Figure 2 This is a flowchart illustrating Embodiment 2 of the test data verification method for this application. Figure 3 A simplified flowchart illustrating the test data verification method provided in Embodiment 2 of this application; Figure 4 This is a schematic diagram of the module structure of the test data verification device in an embodiment of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the test data verification method in this application embodiment.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] The main solution of this application embodiment is as follows: receiving the user's login request, verifying the login request, and obtaining a login verification result; if the login verification result is successful, loading the user's plugin configuration; determining the page style, plugin function priority, and display range of the auxiliary execution plugin according to the plugin configuration; adjusting the auxiliary execution plugin based on the page style, plugin function priority, and display range, and obtaining an adjustment result. The auxiliary execution plugin is used to perform page detection on the test execution page; when a user-triggered information capture command is detected on the test execution page, element capture is performed on the test execution page to obtain test case association information and test user information; the test case association information and test user information are integrated to obtain target test case information. Natural language processing is used to understand the target test case information to obtain test case text. Test case steps are determined based on the test case text, and features are extracted from the text to obtain business features. These business features are then compared and matched against a test data pool to obtain matching results. If the matching is successful, the corresponding basic test data and business test data are extracted from the test data pool. If the matching is unsuccessful, a data generation script is invoked through the auxiliary execution plugin to simulate data generation, obtaining basic test data and business test data. A standardized test process is generated based on the test case steps. Data testing is performed on the basic test data and business test data according to the standardized test process to obtain raw test results. The raw test results are then cleaned and standardized to obtain test results. The auxiliary execution plugin extracts the original expected content from the target test case information. The auxiliary execution plugin performs semantic parsing on the test case steps to obtain verification dimensions and inspection items. Expected result fields are generated based on the original expected content. Based on the verification dimensions and inspection items, the expected result fields and the test results are verified item by item to obtain verification results. The verification results are categorized and statistically analyzed to obtain the number of passed items, the number of abnormal items, and the distribution characteristics of abnormal items. Based on the distribution characteristics of abnormal items, the case steps are marked and located to obtain the abnormal step location results. A test verification report is generated based on the abnormal step location results. The test verification report is then fed back to the user and stored in the data test log. This solves the problem of high dependence on manual labor and poor tool compatibility in data testing, leading to low data testing efficiency, and achieves verification of test data, thus improving verification efficiency.Based on the solution of this invention, and addressing the problem that in reality, the testing process relies on manual completion of data preparation, case execution, result verification, and asset query, and that some long-process businesses are difficult to automate due to their complex scenarios and numerous steps, resulting in low efficiency, a test data verification method was designed. The effectiveness of the test data verification method of this invention was verified when verifying test data. Finally, the efficiency of test data verification using the method of this invention was significantly improved.

[0025] In this embodiment, for ease of description, the test data verification device will be used as the execution subject in the following description.

[0026] Because current testing in the financial sector relies on manual execution, the overall efficiency and stability of testing are difficult to guarantee. One issue is manual operation: data preparation, case execution, and result verification all need to be completed manually, which is cumbersome and prone to errors, directly reducing testing efficiency. Another issue is business adaptation: long-process business scenarios are complex and involve many steps, making it difficult to achieve full-process automation coverage, resulting in inconsistent test execution and low efficiency. Furthermore, there are asset management issues: test data and document assets are stored in a scattered manner, making retrieval difficult and hindering their rapid availability for testing. Therefore, in traditional testing models, tool adaptability also has significant shortcomings. Testing tools are fixed and not customized or extended for different financial products, relying on human experience, which further reduces overall testing effectiveness.

[0027] This application provides a solution that automatically captures and acquires test case information through an auxiliary execution plugin, replacing manual collection and reducing manual operations. Then, it uses natural language processing to automatically extract case steps and business characteristics, achieving intelligent matching of test data without the need for manual data creation and maintenance. Finally, it automatically executes tests according to case steps and automatically performs logical verification based on expected result fields, avoiding errors from manual verification and significantly improving data testing efficiency. This solves the problem of data testing being highly dependent on manual work and having poor tool compatibility, resulting in low data testing efficiency, and improves the verification efficiency of test data.

[0028] Based on this, embodiments of this application provide a test data verification method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the test data verification method of this application.

[0029] In this embodiment, the test data verification method is applied to a data testing platform, which includes an auxiliary execution plugin. The test data verification method includes steps S01 to S04: Step S01: Capture the information capture command triggered by the user through the auxiliary execution plugin, and obtain the target test case information based on the information capture command; Before the implementation of this embodiment, it should be clear that the testing process relies on manual completion of data preparation, case execution, result verification, and asset query. Some long-process business is difficult to automate due to its complexity and numerous steps. At the same time, test data needs to be manually created and maintained in conjunction with business scenarios. Test assets such as operation manuals, API documents, and parameter configurations are scattered across various platforms. Test tools and functions are mostly fixed and do not provide flexible adaptation and expansion capabilities for different products. Overall, it relies on manual operation and experience.

[0030] Therefore, in order to solve the above problems, this embodiment deploys the auxiliary execution plugin on the lean test management platform to monitor user operations on the test execution page in real time. When the user clicks the information capture button to trigger an instruction, the plugin responds immediately and retrieves the relevant test case information corresponding to the user's current task from the platform database, thus quickly obtaining the target test case information.

[0031] Step S02: Perform natural language processing on the target test case information to obtain the case steps and business characteristics, and match basic test data and business test data according to the business characteristics; The acquired target test case information is input into a natural language processing model to complete text parsing and feature extraction, break down clear case execution steps, extract core business features such as business functions, customer country, and currency, and then compare these features with the test data pool to match the basic test data and business test data that meet the requirements.

[0032] Step S03: Perform data testing on the basic test data and business test data through the case steps to obtain test results; Following the steps of the decomposed case, the matched basic test data and business test data are sequentially substituted into the test process to simulate real business scenarios and perform test operations. The execution data and feedback information of each step are recorded and integrated to form preliminary test results.

[0033] Step S04: The expected result field is obtained by parsing the case steps through the auxiliary execution plugin, and the test result is logically verified based on the expected result field to obtain the verification result.

[0034] The auxiliary execution plugin performs semantic parsing on the case steps, extracts the fields related to the expected results, compares them item by item with the test results obtained during the testing process, determines whether the two are consistent, and finally generates a verification result containing pass and fail items.

[0035] Specifically, before step S01 above, which involves capturing user-triggered information retrieval commands through the auxiliary execution plugin and obtaining target test case information based on the information retrieval commands, the method further includes: Step S0101: Receive the user's login request, verify the login request, and obtain the login verification result; Step S0102: If the login verification result is successful, load the user's plugin configuration; Step S0103: Determine the page style, plugin function priority, and display range of the auxiliary execution plugin according to the plugin configuration; Step S0104: Adjust the auxiliary execution plugin by adjusting the page style, plugin function priority, and display range to obtain the adjustment result.

[0036] The system receives login information such as username and password entered by users on the Lean Test Management Platform. At the same time, it collects auxiliary verification information such as the hardware identifier of the user's terminal device and the login IP address. It connects to the unified identity verification system in the backend to perform double verification of the user's identity. On the one hand, it verifies the correctness of the username and password, and on the other hand, it verifies whether the terminal device and IP address are within the preset security whitelist. It also strictly verifies whether the user has the relevant operation permissions for test data verification, distinguishes the permission scope of different roles such as ordinary testers, test administrators, and product specialists, and generates a login verification result by combining the three indicators of identity legality, device security, and operation permissions.

[0037] If the login verification is successful, the backend system will immediately retrieve the plugin configuration information that the user has saved in advance. This configuration information is set by the user in advance according to their own testing needs, the product group they belong to (such as Wholesale Testing Room 2), and the type of business they are responsible for (such as international settlement and individual remittance business) and stored in the metadata service.

[0038] Based on the plugin configuration information, the page layout, button styles, color schemes, font sizes, and other page style parameters are clearly defined to ensure that the page display conforms to user habits. Then, the priority order of plugin functions such as AI data generation, asset recommendation, automated verification, and review document generation is determined. For example, testers of national settlement business can set the data generation function as the highest priority, while testers of individual exchange business can set the asset recommendation function as the priority display. Simultaneously, the scope of functions currently available to users is defined, eliminating redundant functions that do not need to be displayed to avoid overly complex functions affecting operational efficiency. According to the determined page style, function priority, and display scope, the front-end interface of the auxiliary execution plugin is adjusted in real time, synchronously updating the plugin's function menu, operation buttons, and display order to ensure that the adjusted plugin interface is concise and intuitive, and the function display fits the actual testing scenario needs of users. This forms the final adjustment result, which users can use for subsequent test data verification operations. The adjustment result is also synchronously stored in the user configuration file for easy access and modification later.

[0039] More specifically, step S01 above, which involves capturing user-triggered information retrieval commands through the auxiliary execution plugin and obtaining target test case information based on the information retrieval commands, includes: Step S011: Perform page detection on the test execution page using the auxiliary execution plugin; Step S012: When the information capture command triggered by the user is detected on the test execution page, the elements of the test execution page are captured to obtain the test case association information and the test user information. Step S013: Integrate the test case association information and test user information to obtain target test case information.

[0040] After being deployed on the Lean Test Management Platform, the auxiliary execution plugin automatically loads and enters the running state upon platform startup. It continuously monitors the test execution page in real time, using a page element listening algorithm to monitor changes in DOM elements, user mouse clicks, keyboard inputs, and other operations, as well as trigger signals for instructions such as information capture and data query. This ensures that the user's operational intent can be captured in a timely and accurate manner, avoiding omissions or accidental triggering of instructions.

[0041] When the plugin detects that a user has initiated an information capture command by clicking a preset information capture button on the page, triggering it with a shortcut key, or selecting it from the right-click menu, the plugin immediately starts the page element capture module. It uses a multi-dimensional element extraction algorithm to comprehensively and accurately capture the core information in the test execution page. The test case related information includes the case name, case number, case steps, expected case results, test priority, test module, and function items, ensuring that all the core information required for test execution is covered.

[0042] Simultaneously, the system captures the current user's account, product group, operating permissions, responsible business line, and the ID of the currently executing test task, ensuring that the captured information is complete, accurate, and without any omissions of key content.

[0043] The captured test case association information and test user information are preprocessed. First, duplicate data is removed using a deduplication algorithm. Then, format standardization is performed, converting different formats of dates, numbers, permission identifiers, etc., into a preset standard format to remove invalid, redundant, and interfering information. Subsequently, the two types of information are integrated according to the association relationship of "user information - test task - case information" and a preset data structure. The user operation background is associated with the corresponding test case information to form target test case information containing user operation permissions, test task context, and core case content. This provides complete and standardized basic data support for subsequent natural language processing, data matching, automated verification, and other operations, ensuring the smoothness and accuracy of subsequent operations.

[0044] Further, step S02 above, which involves performing natural language processing on the target test case information to obtain the test case steps and business characteristics, and matching the basic test data and business test data based on the business characteristics, includes: Step S021: Perform text understanding on the target test case information using natural language processing to obtain the case text; Step S022: Determine the case steps based on the case text, and extract features from the case text to obtain business features; Step S023: Compare and match the test data pool using the business characteristics to obtain the matching result; Step S024: If the matching result is a successful match, then extract the corresponding basic test data and business test data from the test data pool based on the matching result; Step S025: If the matching result is unsuccessful, then according to the business characteristics, the auxiliary execution plugin is used to call the data generation script to perform simulation data generation, and obtain basic test data and business test data.

[0045] The integrated target test case information is input into a pre-set natural language processing model. This model is trained on a corpus specifically for the financial testing field and can accurately identify professional terms, business logic, and operational norms in the test cases. The model first performs word segmentation on the text content, breaking it down into keywords, professional terms, and sentence components. Then, it performs semantic analysis and contextual association processing to filter out invalid interference information, correct text ambiguities, and generate case text with clear structure and semantic clarity, ensuring that the text content can accurately reflect the core information, execution requirements, and business scenarios of the test cases.

[0046] Based on the processed case text, specific and executable case steps are broken down according to the order of test execution. The operational requirements, execution logic, input parameters and dependencies of each step are clearly defined to ensure that the steps are clearly broken down and unambiguous, so as to facilitate the subsequent insertion of test data to execute the test.

[0047] Simultaneously, through feature extraction algorithms, core business features are extracted from the case text, including business functions (such as cross-border payments, inbound remittances, foreign exchange agreement signing, etc.), account type (such as 302 physical card, Class I general card), resident status, account opening document type, customer country, currency, cash and remittance status, account status, contracting bank, online banking login method, supported services (such as Wing Lung salary direct remittance), etc. The extracted business features are standardized and encoded to form a standardized business feature array, ensuring that the feature descriptions are consistent and can be used for data comparison.

[0048] The extracted business feature array is compared and matched with the data in the test data pool from multiple dimensions. The comparison dimensions are consistent with the design dimensions of the test data pool, covering all content of the business features. A combination of fuzzy matching and exact matching is used, prioritizing exact matching. If no result is found with exact matching, fuzzy matching is performed to ensure the accuracy and comprehensiveness of the matching. Matching results are generated based on the comparison results, indicating whether the matching is successful or unsuccessful, and relevant data during the matching process is recorded. If the matching result is successful, basic test data and business test data that fully match the business features are extracted from the test data pool. The basic test data includes data related to core interactions (such as customer number, mobile phone number, household registration number, etc.), and the business test data includes data related to signed product agreements and process flow (such as agreement number, flow status, transaction amount, etc.). After extraction, the data undergoes format and integrity checks to ensure that the data is complete and error-free, and can be directly used for test execution. If the matching result is unsuccessful, the auxiliary execution plugin automatically calls the preset data generation script based on the extracted business characteristics. The script simulates real financial business scenarios to generate data, supports the rapid generation of basic data and business data, strictly follows financial business norms during the data generation process, and ensures the authenticity and compliance of the generated data. After the data generation is completed, the data is written to the test data pool for storage, and is also fed back to the user in real time through the plugin interface for the user to use directly for testing, realizing the rapid replenishment of test data and solving the pain point of difficulty in obtaining test data.

[0049] This embodiment, through the above-described scheme, specifically captures user-triggered information retrieval commands via the auxiliary execution plugin, and obtains target test case information based on these commands. Natural language processing is then performed on the target test case information to obtain case steps and business characteristics, and basic test data and business test data are matched based on the business characteristics. Data testing is then performed on the basic test data and business test data using the case steps to obtain test results. Finally, the auxiliary execution plugin parses the case steps to obtain expected result fields, and logical verification is performed on the test results based on these expected result fields to obtain verification results. Thus, the auxiliary execution plugin automatically captures and obtains test case information, replacing manual collection and reducing manual operations. Subsequently, natural language processing is used to automatically extract case steps and business characteristics, achieving intelligent matching of test data without the need for manual data creation and maintenance. Finally, tests are automatically executed according to the case steps, and automatic logical verification is performed based on the expected result fields, avoiding errors from manual verification. This significantly improves data testing efficiency and solves the problem of high dependence on manual labor and poor tool compatibility leading to low data testing efficiency, thereby improving the verification efficiency of test data.

[0050] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 In step S03, the test data verification method further includes steps S031-S033, whereby the test data is verified by performing data testing on the basic test data and business test data through the example steps to obtain the test results. Step S031: Generate a standardized testing process based on the case steps; Step S032: Perform data testing on the basic test data and business test data according to the standardized test process to obtain the original test results; Step S033: Perform data cleaning and standardization on the original test results to obtain the test results.

[0051] Based on the broken-down case steps, combined with financial testing industry standards, product business specifications, and internal team testing execution requirements, a unified execution logic and operational specifications were identified. Redundant, repetitive, and invalid steps were eliminated, and the execution order, operational standards, judgment conditions, and exception handling methods for each step were clarified. A standardized testing process was generated, which was presented in a visual format, annotating the relationships between each step, execution time thresholds, and dependent test data types. This ensures that when different users execute the same case in different scenarios, the process is consistent, the operation is standardized, and the results are comparable, avoiding deviations in test results due to differences in operation.

[0052] Following a standardized testing process, basic test data and business test data obtained through matching or data generation are substituted into each test step one by one to simulate real financial business operation scenarios. During the test, the execution status of each step (such as execution success, execution failure, execution in progress), feedback data, abnormal information (such as data errors, process interruption), execution time and other key information are recorded in real time. At the same time, intermediate data during the test process is captured to form raw test results containing original data, unprocessed feedback and abnormal records. The raw test results completely retain all data in the test process, which is convenient for subsequent traceability and problem investigation.

[0053] The original test results are cleaned by using data filtering algorithms to remove invalid, abnormal, and duplicate data, and data correction algorithms to correct data format deviations and missing data, and to supplement missing key data fields to ensure the accuracy and completeness of the data.

[0054] Subsequently, the cleaned data is standardized according to the preset standardization rules, unifying data units, formats and expressions, converting non-standardized feedback information into standardized codes, classifying test results into grades, clarifying the judgment criteria for different result types such as test pass, test failure, and test anomaly, and finally forming test results with clear structure, accurate data and direct usability for verification. At the same time, test result details are generated, marking the specific data and execution status of each test step, providing clear data support for subsequent logical verification.

[0055] Specifically, step S04 above, which involves parsing the case steps using the auxiliary execution plugin to obtain the expected result field, and then performing logical verification on the test results based on the expected result field to obtain the verification result, includes the following steps: Step S041: Extract the original expected content from the target test case information through the auxiliary execution plugin; Step S042: The auxiliary execution plugin performs semantic parsing on the case steps to obtain the verification dimensions and inspection items; Step S043: Generate the expected result field based on the original expected content; Step S044: Based on the verification dimensions and inspection items, verify the expected result field and the test result item by item to obtain the verification result.

[0056] The auxiliary execution plugin starts the expected result extraction module, which accurately extracts the original expected content explicitly recorded in the target test case information, including the expected execution results of each test step, data thresholds, business rules, and anomaly judgment criteria. At the same time, it extracts the expected requirements implicit in the case, ensuring that the extracted content is complete, unbiased, and without omissions. After the extraction is completed, the original expected content is initially sorted out, redundant expressions are removed, and the core expected indicators are clarified.

[0057] The case steps are subjected to in-depth semantic analysis. Combining the extracted business characteristics and testing requirements, corresponding verification dimensions are decomposed, mainly including core dimensions such as data accuracy, process compliance, business logic, data integrity, and format standardization. At the same time, the specific inspection items under each verification dimension are clarified, and the inspection standards, judgment thresholds, and comparison methods for each inspection item are refined. For example, under the data accuracy dimension, the inspection items include household registration number format, customer information consistency, and transaction amount accuracy. Each inspection item has clear comparison rules and judgment standards to ensure that the verification process is standardized, without omissions, and without ambiguity.

[0058] Based on the extracted original expected content, expected result fields are generated according to a preset structured format. The expected value, judgment condition, and error range (if any) corresponding to each inspection item are specified. The expected result fields are standardized and encoded to form a structured expected result dataset. The dataset adopts a directly comparable format to facilitate efficient comparison with subsequent test results.

[0059] Based on the defined verification dimensions and inspection items, the logic verification module is activated. The expected result fields are compared and verified item by item with the processed test results. A field-by-field, step-by-step comparison method is used to determine whether the test results of each inspection item meet the expected requirements. For numerical results, the comparison is performed according to a preset error range; for logical results, the judgment is made according to business rules; and for formatted results, the verification is performed according to a standardized format. During the comparison process, passed and abnormal items are recorded in real time, noting the specific deviation content, location, degree of deviation, and possible causes of the abnormal items. Simultaneously, the corresponding test steps and test data are associated, ultimately integrating to form a verification result that includes verification details, abnormal explanations, deviation analysis, and verification conclusions. The verification result is presented in a visual format for easy viewing and understanding by users.

[0060] More specifically, after step S04 above, which involves parsing the case step using the auxiliary execution plugin to obtain the expected result field, performing logical verification on the test result based on the expected result field, and obtaining the verification result, the method further includes: Step S05: Classify and statistically analyze the verification results to obtain the number of passed items, the number of abnormal items, and the distribution characteristics of abnormal items; Step S06: Mark and locate the case steps according to the distribution characteristics of the anomalies to obtain the anomaly step location results; Step S07: Generate a test verification report based on the abnormal step location results; Step S08: The test verification report is fed back to the user and stored in the data test log.

[0061] The generated verification results are categorized and statistically processed. The statistical analysis module is activated to split the data according to multiple dimensions such as verification dimension, test step, and business type. The specific number of passed items and the specific number of abnormal items are counted separately. Core statistical indicators such as test pass rate and abnormality rate are calculated. At the same time, the distribution of abnormal items in each verification dimension and each test step is analyzed to sort out the patterns and common problems of abnormal occurrences. For example, it is analyzed whether there are frequent abnormalities in a certain test step or a high abnormality rate in a certain verification dimension. The common causes of abnormality are analyzed in combination with business scenarios.

[0062] Based on the distribution characteristics of anomalies, and combined with the execution order and logical connections of case steps, the case steps that have anomalies are accurately marked and located. The specific location of the anomaly step, the anomaly type (such as data anomaly, logic anomaly, process anomaly), the deviation content, and the occurrence time are marked. At the same time, the corresponding test data, expected results, and verification standards are associated to generate a detailed list of anomaly step locations, which makes it easier for users to quickly locate the root cause of the problem and reduce the time spent troubleshooting.

[0063] Based on the anomaly location results, the system integrates verification details, statistics on passed and failed items, root cause analysis, and improvement suggestions. A test verification report is generated according to a pre-defined standardized template. The report consists of five parts: basic information, test overview, verification details, anomaly analysis, and improvement suggestions. Basic information includes test task ID, test case name, testers, and test time. The test overview includes core indicators such as pass rate and failure rate. Verification details record the verification status of each step in detail. Anomaly analysis delves into the causes of anomalies. Improvement suggestions provide feasible optimization solutions tailored to the business scenario, ensuring the report is comprehensive, logically clear, and highly practical. The generated test verification report is fed back to the user in real time through the auxiliary execution plugin interface and simultaneously pushed to the user's designated receiving channels (such as WeChat Work and email). The test verification report is also stored in the data test log, categorized by timestamp, product type, test task ID, and testers, establishing a complete test archive for easy subsequent querying, tracing, review, and optimization. Simultaneously, statistical data and anomaly information are pushed to the team management platform, providing solid data support for subsequent test management, skill optimization, and plugin function iteration.

[0064] This embodiment, through the above-described scheme, specifically generates a standardized test process based on the case steps; performs data testing on the basic test data and business test data according to the standardized test process to obtain raw test results; and cleans and standardizes the raw test results to obtain the final test results. Thus, by automatically capturing and acquiring test case information through an auxiliary execution plugin, replacing manual collection and reducing manual operations, and then automatically extracting case steps and business features using natural language processing, intelligent matching of test data is achieved, eliminating the need for manual data creation and maintenance. Finally, the test is automatically executed according to the case steps, and automatic logical verification is performed based on the expected result fields, avoiding errors from manual verification and significantly improving data testing efficiency. This solves the problem of high dependence on manual labor and poor tool compatibility in data testing, leading to low data testing efficiency, and improves the verification efficiency of test data.

[0065] For example, to help understand the implementation flow of the test data verification method obtained in this embodiment combined with the above embodiment one, please refer to... Figure 3 , Figure 3 A simplified flowchart of a test data verification method is provided, specifically: Users first enter their account, password, and other login information through the Lean Test Management Platform to initiate a login request. The system then connects to the unified identity verification system in the backend to perform dual verification of the user's identity, terminal device security, and operation permissions. After successful verification, the backend automatically retrieves the user's pre-saved plugin configuration information and adjusts the page style, function priority, and display scope of the auxiliary execution plugin according to the configuration information to complete the plugin initialization and ensure that the plugin interface conforms to the user's usage habits and business testing needs.

[0066] After the plugin is initialized, the page detection function is automatically started, continuously monitoring changes in elements and user actions on the test execution page. When a user initiates an information capture command by clicking the information capture button or triggering a shortcut key on the test execution page, the plugin immediately starts the element capture module to comprehensively capture test case related information (including case name, steps, expected results, etc.) and test user information (including account, room / group, permissions, etc.). After deduplication and standardization of the captured information, it is integrated into target test case information according to the relationship, providing basic data support for subsequent operations.

[0067] The target test case information is then input into a natural language processing model specifically trained for the financial testing field. The model performs word segmentation, semantic parsing, and contextual processing on the text, breaking it down into clear case steps and extracting core business features such as business functions, account types, and customer nationalities. Based on these business features, a multi-dimensional comparison and matching is performed with the test data pool. If the match is successful, the corresponding basic test data and business test data are extracted. If the match is unsuccessful, the data generation script is automatically invoked to simulate real financial business scenarios and generate test data that meets the requirements, which is then written into the test data pool and simultaneously fed back to the user.

[0068] After obtaining the test data, a standardized test process is generated based on the broken-down case steps and industry standards and business specifications. The test data is then fed into the process one by one to execute the test. The execution status, feedback data and abnormal information of each step are recorded in real time to form the original test results. The original test results are then cleaned and standardized to remove invalid data and correct format deviations, resulting in a final test result with a clear structure and accurate data.

[0069] The auxiliary execution plugin then parses the case steps, extracts the original expected content, breaks down the verification dimensions and inspection items, generates standardized expected result fields, and compares the expected result fields with the test results item by item based on the verification dimensions and inspection items. It records the passed items and the abnormal items, marks the abnormal details, and integrates them to form a complete verification result.

[0070] Finally, the verification results are classified and statistically analyzed to identify the distribution characteristics of anomalies, pinpoint the abnormal steps, and generate a test verification report containing a test overview, anomaly analysis, and improvement suggestions according to a standardized template. The report is then fed back to the user and stored synchronously in the data test log, establishing a complete test archive and completing the entire test data verification process. This process achieves semi-automation of test data acquisition, test execution, result verification, and report generation, significantly reducing reliance on manual intervention and improving test efficiency and accuracy.

[0071] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the test data verification method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0072] This application also provides a test data verification device, please refer to... Figure 4 The test data verification device is applied to a data testing platform, which includes an auxiliary execution plugin. The test data verification device includes: The capture module 10 is used to capture information capture instructions triggered by the user through the auxiliary execution plugin, and obtain target test case information based on the information capture instructions; The matching module 20 is used to perform natural language processing on the target test case information to obtain the case steps and business characteristics, and to match basic test data and business test data according to the business characteristics. Test module 30 is used to perform data testing on the basic test data and business test data through the case steps to obtain test results; The verification module 40 is used to parse the case steps through the auxiliary execution plugin to obtain the expected result field, and to perform logical verification on the test result based on the expected result field to obtain the verification result.

[0073] The test data verification device provided in this application, employing the test data verification method in the above embodiments, can solve the technical problem of low data testing efficiency due to the high dependence on manual labor and poor tool adaptability in data testing. Compared with the prior art, the beneficial effects of the test data verification device provided in this application are the same as those of the test data verification method provided in the above embodiments, and other technical features in the test data verification device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0074] This application provides a test data verification device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the test data verification method in the first embodiment described above.

[0075] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a test data verification device suitable for implementing embodiments of this application. The test data verification device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The test data verification device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0076] like Figure 5As shown, the test data verification device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in the read-only memory 1002 or a program loaded from the storage device 1003 into the random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the test data verification device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the test data verification device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show test data verification devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0077] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0078] The test data verification device provided in this application, employing the test data verification method in the above embodiments, can solve the technical problem of low data testing efficiency due to the high dependence on manual labor and poor tool adaptability in data testing. Compared with the prior art, the beneficial effects of the test data verification device provided in this application are the same as those of the test data verification method provided in the above embodiments, and other technical features of this test data verification device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0079] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0080] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0081] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the test data verification method in the above embodiments.

[0082] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0083] The aforementioned computer-readable storage medium may be included in the test data verification device; or it may exist independently and not be assembled into the test data verification device.

[0084] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the test data verification device, the test data verification device performs the following actions: captures user-triggered information capture instructions via the auxiliary execution plugin; obtains target test case information based on the information capture instructions; performs natural language processing on the target test case information to obtain case steps and business characteristics; matches basic test data and business test data according to the business characteristics; performs data testing on the basic test data and business test data through the case steps to obtain test results; parses the case steps through the auxiliary execution plugin to obtain expected result fields; performs logical verification on the test results based on the expected result fields to obtain verification results.

[0085] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0086] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0087] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0088] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described test data verification method. This solves the technical problem that data testing is highly dependent on manual labor and suffers from poor tool compatibility, leading to low data testing efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the test data verification method provided in the above embodiments, and will not be repeated here.

[0089] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the test data verification method described above.

[0090] The computer program product provided in this application can solve the technical problem that data testing is highly dependent on manual labor and suffers from poor tool compatibility, resulting in low data testing efficiency. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the test data verification method provided in the above embodiments, and will not be repeated here.

[0091] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A test data verification method, characterized in that, The test data verification method is applied to a data testing platform, which includes an auxiliary execution plugin. The test data verification method includes: The auxiliary execution plugin captures user-triggered information retrieval commands and obtains target test case information based on these commands. Natural language processing is performed on the target test case information to obtain the case steps and business characteristics, and basic test data and business test data are matched according to the business characteristics; The basic test data and business test data are tested through the steps described in the case study to obtain test results. The expected result field is obtained by parsing the case steps through the auxiliary execution plugin, and the test result is logically verified based on the expected result field to obtain the verification result.

2. The test data verification method as described in claim 1, characterized in that, Before the step of capturing user-triggered information retrieval commands through the auxiliary execution plugin and obtaining target test case information based on the information retrieval commands, the method further includes: Receive the user's login request, verify the login request, and obtain the login verification result; If the login verification result is successful, load the user's plugin configuration; The page style, plugin function priority, and display scope of the auxiliary execution plugin are determined based on the plugin configuration. The auxiliary execution plugin was adjusted by modifying the page style, plugin function priority, and display range to obtain the adjustment result.

3. The test data verification method as described in claim 1, characterized in that, The step of capturing user-triggered information retrieval commands through the auxiliary execution plugin and obtaining target test case information based on the information retrieval commands includes: The auxiliary execution plugin is used to perform page inspection on the test execution page; When a user-triggered information capture command is detected on the test execution page, elements are captured on the test execution page to obtain test case association information and test user information; The test case association information and test user information are integrated to obtain the target test case information.

4. The test data verification method as described in claim 1, characterized in that, The steps of performing natural language processing on the target test case information to obtain the case steps and business characteristics, and matching the basic test data and business test data based on the business characteristics, include: The target test case information is analyzed using natural language processing to obtain the test case text; The case steps are determined based on the case text, and features are extracted from the case text to obtain business features; The test data pool is compared and matched using the aforementioned business characteristics to obtain matching results; If the matching result is successful, then the corresponding basic test data and business test data are extracted from the test data pool based on the matching result; If the matching result is unsuccessful, then based on the business characteristics, the auxiliary execution plugin is used to call the data generation script to perform simulation data generation, thereby obtaining basic test data and business test data.

5. The test data verification method as described in claim 1, characterized in that, The steps for performing data testing on the basic test data and business test data through the aforementioned case steps to obtain test results include: A standardized testing process is generated based on the steps described in the case study. The basic test data and business test data are tested according to the standardized test process to obtain the original test results. The original test results are cleaned and standardized to obtain the final test results.

6. The test data verification method as described in claim 1, characterized in that, The step of parsing the case steps using the auxiliary execution plugin to obtain the expected result field, and then performing logical verification on the test results based on the expected result field to obtain the verification result includes: The auxiliary execution plugin extracts the original expected content from the target test case information; The auxiliary execution plugin performs semantic parsing on the case steps to obtain the verification dimensions and inspection items; Generate the expected result field based on the original expected content; Based on the verification dimensions and inspection items, the expected result field and the test result are verified item by item to obtain the verification result.

7. The test data verification method as described in claim 1, characterized in that, After the step of parsing the case steps to obtain the expected result field through the auxiliary execution plugin, and performing logical verification on the test result based on the expected result field to obtain the verification result, the method further includes: The verification results are classified and statistically analyzed to obtain the number of passed items, the number of abnormal items, and the distribution characteristics of abnormal items; The case steps are marked and located based on the distribution characteristics of the anomalies to obtain the anomaly step location results; A test verification report is generated based on the results of the anomaly location. The test verification report is sent back to the user and stored in the data test log.

8. A test data verification device, characterized in that, The test data verification device is applied to a data testing platform, which includes an auxiliary execution plugin. The test data verification device includes: The capture module is used to capture user-triggered information capture commands through the auxiliary execution plugin, and obtain target test case information based on the information capture commands; The matching module is used to perform natural language processing on the target test case information to obtain the case steps and business characteristics, and to match basic test data and business test data according to the business characteristics. The testing module is used to perform data testing on the basic test data and business test data through the case steps to obtain test results; The verification module is used to parse the case steps through the auxiliary execution plugin to obtain the expected result field, and to perform logical verification on the test result based on the expected result field to obtain the verification result.

9. A test data verification device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the test data verification method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the test data verification method as described in any one of claims 1 to 7.

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