Buried point testing method and device, electronic equipment, storage medium and vehicle

Abstract

The invention provides a buried point testing method and device, electronic equipment, a storage medium and a vehicle, and relates to the technical field of buried point testing. The method comprises the following steps: acquiring business demand information and burying point demand information of a to-be-tested application program, and first user behavior information using the to-be-tested application program; and then inputting the business demand information, the burying point demand information and the first user behavior information into a test case generation model, and obtaining a target test case output by the test case generation model. Next, executing the target test case to perform burying point test on the to-be-tested application program, and obtaining test data generated in the test process; and finally, comparing the test data with preset expected data to obtain a test result. According to the method, the buried point test efficiency and the test scene coverage are improved.

Description

Technical Field

[0001] The present invention relates to the field of buried point testing technology, and in particular to a buried point testing method, device, electronic equipment, storage medium and vehicle. Background Art

[0002] With the development of the automotive industry, users have gradually changed from physiological needs such as safety and comfort of functional cars to emotional interactive experience needs that focus on fun and social interaction. Therefore, they are increasingly relying on data analysis to optimize user experience and improve business efficiency. In order to obtain user behavior data during the use of applications, tracking points are usually set in the application.

[0003] The existing technology first determines the user behavior or system events that need to be tracked, such as user button clicks, page browsing, form submission, etc., and determines the purpose of data collection. Then determine which events need to be tracked and define the data fields that need to be collected for each event. After that, track the application in the test environment based on the above data and write test cases. Finally, run the test case in the test environment to determine whether there is a problem with the tracking.

[0004] However, the existing technology has technical problems such as low efficiency of point-testing and low coverage of test scenarios. Summary of the invention

[0005] The present invention provides a buried point testing method, device, electronic device, storage medium and vehicle, which are used to solve the technical problems of low buried point testing efficiency and low coverage of test scenarios in the prior art.

[0006] In a first aspect, the present invention provides a buried point testing method, comprising:

[0007] Obtaining business requirement information, tracking requirement information, and first user behavior information of the application to be tested;

[0008] Input the business requirement information, the embedding requirement information and the first user behavior information into a test case generation model, and obtain a target test case output by the test case generation model;

[0009] Execute the target test case to perform a point-by-point test on the application to be tested, and obtain test data generated during the test process;

[0010] The test data is compared with preset expected data to obtain a test result.

[0011] Through the above-mentioned technical means, a fully automated process is designed to carry out tracking test. The business requirement information, tracking requirement information, and the first user behavior information are fully analyzed through the test case generation model to generate the target test case. At the same time, the target test case is executed to perform automated tracking test on the application to be tested, and further compared with the preset expected data to obtain the test results. It can be seen that in the whole process, the relevant staff only need to input the relevant business requirement information and tracking requirement information. The subsequent generation of target test cases, execution of target test cases, and comparison of test data are all automated, which greatly saves manpower and tracking test efficiency. At the same time, according to the analysis of the first user behavior information in the test case generation model, the coverage of the target test case for the test scenario can be improved, thereby improving the accuracy of the tracking test.

[0012] Further, the step of inputting the business requirement information, the embedding requirement information, and the first user behavior information into a test case generation model to obtain a target test case output by the test case generation model includes:

[0013] The business requirement information, the embedding requirement information and the first user behavior information are processed by the test scenario generation module of the test case generation model to generate target test scenario information;

[0014] The business requirement information, the embedding point requirement information, the first user behavior information and the target test scenario information are processed by the test case generation module of the test case generation model to generate the target test case.

[0015] According to the above technical means, the test scenario generation module automatically combines different requirements and conditions by deeply understanding and learning the business requirement information, the embedding requirement information, and the first user behavior information, and then generates the target test scenario information. Then, the test case generation module automatically generates the corresponding target test case according to the target test scenario information, which can accurately improve the richness of the test scenario corresponding to the generated target test case.

[0016] Further, executing the target test case to perform a tracking test on the application to be tested and obtaining test data generated during the test process includes:

[0017] Generate a target test script according to the target test case and the code writing strategy;

[0018] Execute the target test script to perform a point-by-point test on the application to be tested, and obtain the test data generated during the test process.

[0019] According to the above technical means, the test data generated during the execution of the target test script is collected to ensure that the test data can be verified smoothly later.

[0020] Furthermore, the method further comprises:

[0021] Outputting a first confirmation request, where the first confirmation request is used to request the first user to determine whether the target test case needs to be modified;

[0022] Acquire first feedback information returned by the first user based on the first confirmation request, where the first feedback information is used to indicate whether the target test case needs to be modified;

[0023] When the first feedback information indicates that the target test case needs to be modified, a new target test case modified based on the target test case is obtained.

[0024] Through the above technical means, the manual verification and modification functions of the target test cases are provided to ensure the accuracy of subsequent tracking tests.

[0025] Furthermore, the method further comprises:

[0026] Outputting a second confirmation request, where the second confirmation request is used to request a second user to determine whether the target test script needs to be modified;

[0027] Acquire second feedback information returned by the second user based on the second confirmation request, where the second feedback information is used to indicate whether the target test script needs to be modified;

[0028] When the second feedback information indicates that the target test script needs to be modified, a new target test script modified based on the target test script is obtained.

[0029] Through the above technical means, the manual verification and modification functions of the target test script are provided to further ensure the accuracy of subsequent tracking tests.

[0030] Furthermore, the test scenario generation module of the test case generation model processes the business requirement information, the embedding requirement information and the first user behavior information to generate target test scenario information, including:

[0031] The embedding point evaluation module of the test case generation model is used to evaluate the embedding point demand information and generate an evaluation result;

[0032] When the evaluation result indicates that the tracking point demand information has passed the evaluation, the business demand information, the tracking point demand information and the first user behavior information are processed by the test scenario generation module of the test case generation model to generate the target test scenario information;

[0033] The evaluation process includes at least one of the following: determining whether the data of the burying point demand information is complete, determining whether there is a logical conflict in the burying point demand information, and determining whether the burying point demand information meets the business demand information and preset conditions.

[0034] Based on the above-mentioned technical means, the rationality of the design of the tracking point requirements is evaluated, and the completeness, rationality and accuracy of the tracking point events and parameters in the tracking point requirements are determined, thereby ensuring the accuracy of the generated target test cases and the accuracy of subsequent tracking point tests.

[0035] Furthermore, the method further comprises:

[0036] When the evaluation result indicates that the burying point demand information has not passed the evaluation, a reminder message is output, where the reminder message is used to remind the third user to modify the burying point demand information.

[0037] According to the above technical means, it is possible to discover and correct possible errors or ambiguities in the demand description.

[0038] Furthermore, before obtaining the business requirement information, tracking requirement information, and first user behavior information of the application to be tested, the method further includes:

[0039] Obtain multiple sample test cases for the sample application, as well as sample business requirement information, sample embedding requirement information, sample test scenario information, and sample user behavior information using the sample application corresponding to each sample test case;

[0040] Model training is performed based on each sample test case and the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case to generate the test case generation model.

[0041] According to the above-mentioned technical means, by training a large number of sample test cases, sample business demand information, sample embedding demand information, sample test scenario information, and sample user behavior information, it is ensured that the test case generation model can accurately understand the relationship between embedding demand, test cases, and user behavior, thereby generating effective and accurate test cases and improving the coverage of test cases for test scenarios.

[0042] Further, the model training is performed according to each sample test case and the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case to generate the test case generation model, including:

[0043] According to the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case, the first module of the initial model is trained to obtain the test scenario generation module;

[0044] Model training is performed according to each sample test case and the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case, the second module of the initial model is trained, and the test case generation module is obtained to obtain the test case generation model.

[0045] According to the above technical means, the first module and the second module are trained separately, so that the test case generation model obtained by training can subsequently deeply understand and learn the business demand information, the embedding demand information, and the first user behavior information, automatically combine different requirements and conditions, and then generate target test scenario information. Then, the test case generation module automatically generates corresponding target test cases according to the target test scenario information, which can accurately improve the richness of the test scenarios corresponding to the generated target test cases.

[0046] Furthermore, if the target test case is the new target test case, after executing the target test case to perform a point-test on the application to be tested, the method further includes:

[0047] Obtaining second user behavior information collected after executing the new target test case;

[0048] determining the sum of the first user behavior information and the second user behavior information as target user behavior information;

[0049] According to the new target test case, and the target user behavior information, the business requirement information, and the embedding requirement information corresponding to the new target test case, the test case generation model is updated and trained to generate an updated test case generation model.

[0050] Through the above technical means, the user behavior information that has been manually modified in actual applications can be fed back to the test case generation model to achieve continuous training and optimization of the test case generation model, thereby achieving the purpose of improving the accuracy of the test case generation model.

[0051] In a second aspect, the present invention provides a buried point testing device, comprising:

[0052] An acquisition module, used to acquire business requirement information, tracking requirement information, and first user behavior information of the application to be tested;

[0053] An input module, used to input the business requirement information, the embedding requirement information and the first user behavior information into a test case generation model, and obtain a target test case output by the test case generation model;

[0054] An execution module is used to execute the target test case to perform a point-by-point test on the application to be tested and obtain test data generated during the test process;

[0055] The comparison module is used to compare the test data with preset expected data to obtain the test results.

[0056] Furthermore, the input module is specifically used for:

[0057] The business requirement information, the embedding requirement information and the first user behavior information are processed by the test scenario generation module of the test case generation model to generate target test scenario information;

[0058] The business requirement information, the embedding point requirement information, the first user behavior information and the target test scenario information are processed by the test case generation module of the test case generation model to generate the target test case.

[0059] Furthermore, the execution module is specifically used to:

[0060] Generate a target test script according to the target test case and the code writing strategy;

[0061] Execute the target test script to perform a point-by-point test on the application to be tested, and obtain the test data generated during the test process.

[0062] Furthermore, the buried point testing device also includes:

[0063] An output module, configured to output a first confirmation request, wherein the first confirmation request is used to request a first user to determine whether the target test case needs to be modified;

[0064] An acquisition module, configured to acquire first feedback information returned by the first user based on the first confirmation request, wherein the first feedback information is used to indicate whether the target test case needs to be modified;

[0065] The acquisition module is further configured to acquire a new target test case modified based on the target test case when the first feedback information indicates that the target test case needs to be modified.

[0066] Furthermore, the output module is further used to output a second confirmation request, where the second confirmation request is used to request a second user to determine whether the target test script needs to be modified;

[0067] The acquisition module is further used for:

[0068] Acquire second feedback information returned by the second user based on the second confirmation request, where the second feedback information is used to indicate whether the target test script needs to be modified;

[0069] When the second feedback information indicates that the target test script needs to be modified, a new target test script modified based on the target test script is obtained.

[0070] Furthermore, the input module is specifically used for:

[0071] The embedding point evaluation module of the test case generation model is used to evaluate the embedding point demand information and generate an evaluation result;

[0072] When the evaluation result indicates that the tracking point demand information has passed the evaluation, the business demand information, the tracking point demand information and the first user behavior information are processed by the test scenario generation module of the test case generation model;

[0073] The evaluation process includes at least one of the following: determining whether the data of the burying point demand information is complete, determining whether there is a logical conflict in the burying point demand information, and determining whether the burying point demand information meets the business demand information and preset conditions;

[0074] The output module is further used to output reminder information when the evaluation result indicates that the burying point demand information has not passed the evaluation, and the reminder information is used to remind the third user to modify the burying point demand information.

[0075] Furthermore, before obtaining the business requirement information, tracking requirement information, and first user behavior information of the application to be tested, the tracking test device further includes:

[0076] The acquisition module is also used to acquire multiple sample test cases for the sample application, as well as sample business requirement information, sample embedding requirement information, sample test scenario information and sample user behavior information using the sample application corresponding to each sample test case;

[0077] A training module is used to perform model training based on each sample test case and the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case, so as to generate the test case generation model.

[0078] Furthermore, the training module is specifically used for:

[0079] According to the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case, the first module of the initial model is trained to obtain the test scenario generation module;

[0080] Model training is performed according to each sample test case and the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case, the second module of the initial model is trained, and the test case generation module is obtained to obtain the test case generation model.

[0081] Further, if the target test case is the new target test case, after executing the target test case to perform a tracking test on the application to be tested, the tracking test device further includes:

[0082] The acquisition module is further used to acquire the second user behavior information collected after executing the new target test case;

[0083] a determination module, configured to determine the sum of the first user behavior information and the second user behavior information as target user behavior information;

[0084] The training module is also used to update the test case generation model according to the new target test case and the target user behavior information, the business requirement information and the tracking point requirement information corresponding to the new target test case, so as to generate an updated test case generation model.

[0085] In a third aspect, the present invention provides an electronic device, comprising:

[0086] Processor and memory;

[0087] Wherein, the memory stores computer-executable instructions;

[0088] The processor executes the computer-executable instructions stored in the memory to implement the buried point testing method as described in the first aspect and various possible implementation methods of the first aspect.

[0089] In a fourth aspect, the present invention provides a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the embedded point testing method as described in the first aspect and various possible implementation methods of the first aspect.

[0090] In a fifth aspect, the present invention provides a vehicle, comprising: a vehicle body and a vehicle controller;

[0091] Wherein, the vehicle controller includes a processor, and a memory communicatively connected to the processor;

[0092] The memory stores computer-executable instructions;

[0093] The processor executes the computer-executable instructions stored in the memory to implement the buried point testing method as described in the first aspect and various possible implementation methods of the first aspect.

[0094] Beneficial effects of the present invention:

[0095] (1) The present invention automatically performs the following operations, from obtaining tracking requirements, analyzing tracking requirements, automatically generating and executing test cases, to comparing test data. It is no longer necessary for developers to design and execute test cases, thereby improving the tracking test efficiency.

[0096] (2) The present invention conducts in-depth understanding and learning through the test case generation model, automatically combines possible test scenarios, improves the coverage of test cases for test scenarios, and can generate more comprehensive test cases to accurately test various tracking requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 Schematic diagram of the process of the buried point test method provided by the embodiment of the present invention Figure 1 ;

[0098] Figure 2 A schematic diagram of a process for evaluating tracking point demand information provided by an embodiment of the present invention;

[0099] Figure 3 Schematic diagram of the process of the buried point test method provided by the embodiment of the present invention Figure 2 ;

[0100] Figure 4 A schematic diagram of the training process of the test case generation model provided in an embodiment of the present invention;

[0101] Figure 5 A system architecture diagram of a point tracking test method provided by an embodiment of the present invention;

[0102] Figure 6A schematic diagram of a point tracking test scenario provided by an embodiment of the present invention;

[0103] Figure 7 A schematic diagram of the structure of the buried point testing device provided by the present invention;

[0104] Figure 8 A hardware structure diagram of the electronic device provided by the present invention;

[0105] Fig. 9 The hardware structure diagram of the vehicle provided by the present invention. DETAILED DESCRIPTION

[0106] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0107] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein, for example.

[0108] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0109] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and corresponding operation entrances shall be provided for users to choose to authorize or refuse.

[0110] First, the terms involved in the present invention are explained.

[0111] Tracking: Inserting code into your application to collect relevant data when users interact with your application.

[0112] Next, the application scenario of the present invention is explained.

[0113] In the evolution of user needs in the automotive industry, in the initial stage, users focused on basic usage and safety, so their needs for cars were mainly focused on functionality, such as safety and comfort. With the development of the automotive industry, users gradually began to pay attention to emotional interactive experiences, including fun and social. At this time, users hope that cars are not only a means of transportation, but also provide entertainment and social interaction functions.

[0114] Therefore, in order to meet the ever-changing needs of users, the automotive industry is increasingly relying on data analysis. By analyzing user behavior data, manufacturers can better understand user preferences and usage habits, thereby optimizing automotive product design and user experience.

[0115] In order to obtain the behavioral data of users when using car applications, tracking points are usually set in the application. Furthermore, in order to ensure the accuracy of the tracking data, the tracking points of the application need to be tested. Specifically, the purpose of data collection needs to be clarified first, which may involve user behavior analysis, performance monitoring, product optimization, etc. Then, according to the purpose of data collection, determine the user behavior or system events that need to be tracked, and for each event that needs to be tracked, define the data fields that need to be collected. After that, track the application in the test environment based on the above data, and write test cases. Finally, run the test case in the test environment to determine whether there is a problem with the tracking point.

[0116] However, multiple tracking events are highly correlated with each other, and the range of parameter values ​​involved is wide, which makes test case design more difficult, and the test case coverage is insufficient or the test case redundancy is large. At the same time, a set of tracking requirements needs to be tested in different vehicle models, and testers need to repeat the test multiple times, which increases the test workload and extremely low test efficiency.

[0117] It is understandable that the tracking test not only involves the tracking events, but also includes the test of the parameter value, parameter type, whether it is empty, etc. of the tracking event. When executing the tracking test, it is necessary to compare the data one by one, which may lead to omissions or misdetection during the tracking test.

[0118] In addition, the design of tracking points mainly relies on the subjective or objective analysis of developers, so it may happen that after multiple iterations, it is discovered that the tracking points are missing or have errors. Developers need to modify or supplement the existing tracking points, and further design test cases based on the new tracking point requirements.

[0119] In summary, the existing technology has technical problems such as low efficiency of point tracking testing and low coverage of test scenarios.

[0120] In view of the above technical problems, in order to avoid too much manual operation in the process of tracking point testing, the inventor proposes an automated tracking point testing process, including automatic generation and execution of test cases, automatic data comparison, and automatic generation of test results, to solve the problem of low test efficiency caused by manual operation. In addition, the test case generation model adopted by the present invention can fully analyze the first user behavior information to automatically generate target test cases, which can effectively improve the coverage of test cases and ensure the accuracy of tracking point testing.

[0121] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0122] Figure 1 Schematic diagram of the process of the buried point test method provided by the embodiment of the present invention Figure 1 .like Figure 1 As shown, an embodiment of the present invention provides a method for testing a buried point, including:

[0123] S101, obtaining business requirement information, tracking requirement information, and first user behavior information of the application to be tested.

[0124] Among them, business requirement information usually defines the business requirements generated to achieve specific business functions or business processes, and is used to determine the direction and basis of the tracking test.

[0125] Among them, tracking demand information is a further refinement of business demand information, which is used to clarify which user behaviors or events need to be tracked or recorded for subsequent analysis.

[0126] For example, taking the in-vehicle application as an example, if you want to improve the user's activity, you need to understand the various interactive behaviors of users in the in-vehicle application. You can perform tracking in the in-vehicle application to collect data on users' posting of dynamics, likes, comments, private messages, etc., and test the accuracy of tracking through tracking tests to ensure that the collected data can help the product team analyze user preferences and optimize the functions of the in-vehicle application, such as improving the dynamic recommendation algorithm to make the recommended content more in line with user interests and improve user activity. In this example, "improving user activity" is the business requirement, and "tracking users' posting of dynamics, likes, comments, private messages, etc." is the tracking requirement.

[0127] It should be understood that the tracking point demand information also includes more detailed data, such as the location of the tracking point, trigger conditions, data collection format, value range, event type, etc.

[0128] It should be understood that the tracking point demand information and business demand information can be input by relevant staff or obtained from other data storage devices, and the embodiments of the present application do not impose specific restrictions on this.

[0129] The first user behavior information includes user behavior logs and user operation habit big data. The user behavior log is a log file that records the operations performed by the user in the application to be tested. The user operation habit big data log involves the analysis of a large amount of user behavior data to identify the user's operation patterns and habits.

[0130] First, user behavior information can be obtained and analyzed using data analysis tools, which can provide real-time or historical user behavior reports.

[0131] S102: Input the business requirement information, the embedding point requirement information, and the first user behavior information into a test case generation model to obtain a target test case output by the test case generation model.

[0132] Specifically, the test case generation model first analyzes the business demand information, the embedding demand information, and the first user behavior information, automatically combines possible test scenarios, and then automatically generates corresponding test cases based on the test scenarios. Explanatory, the specific process of this step is as follows:

[0133] Step a1: Process the business requirement information, the embedding requirement information and the first user behavior information through the test scenario generation module of the test case generation model to generate target test scenario information.

[0134] Among them, the test scenario generation module deeply understands and learns the business demand information, the tracking demand information and the first user behavior information, automatically combines different requirements and conditions into possible target test scenarios, and generates corresponding target test scenario information. The test scenario generation module takes into account various possible user behaviors and test scenarios, including but not limited to event triggers in different orders, combinations of different parameters, special events under specific conditions, etc. This dynamic and flexible test scenario generation method can better simulate the behavior and environment of real users, thereby performing more accurate tracking tests.

[0135] It should be understood that the tracking demand information obtained in step S101 is manually written and may contain errors or inaccuracies. For example, some key events or parameters may be missing in the tracking demand information, resulting in the inability to fully track user behavior. Therefore, it is very necessary to first evaluate the tracking demand information.

[0136] In a possible implementation, the test case generation model also includes a embedding point evaluation module, which evaluates and processes the embedding point demand information to generate an evaluation result. If the evaluation result indicates that the embedding point demand information passes the evaluation, the business demand information, the embedding point demand information, and the first user behavior information are processed by the test scenario generation module to generate target test scenario information. If the evaluation result indicates that the embedding point demand information fails the evaluation, a reminder message is output to remind the third user to modify the embedding point demand information.

[0137] It should be understood that evaluating the tracking point requirement information before generating the target test scenario through the test scenario generation module can ensure the accuracy of the target test scenario generated subsequently.

[0138] Among them, the third user can be a staff member who edits the tracking point demand information, or other staff members who are responsible for processing the tracking points.

[0139] It should be explained that the evaluation and processing of the tracking demand information includes at least one of the following: determining whether the data of the tracking demand information is complete, determining whether there is a logical conflict in the tracking demand information, and determining whether the tracking demand information meets the business information and preset conditions. That is, the tracking evaluation module analyzes the current tracking demand information to determine the completeness, rationality and accuracy of the tracking events and parameter formulation in the demand.

[0140] Among them, the integrity assessment is mainly to determine whether the tracking point demand information provides all the necessary information, such as event type, trigger condition, parameter type and value range, etc. If any important information is missing, the tracking point assessment module will remind the third user to make additional modifications.

[0141] Reasonableness evaluation includes judging whether the tracking demand information conforms to business logic, user behavior patterns, etc. For example, if a tracking event type and its triggering condition are logically conflicting, then this demand may be unreasonable. For another example, if it is a common and important operation step for users, but it is not reflected in the tracking demand information, then it is necessary to prompt or suggest relevant personnel to supplement it. Through reasonableness evaluation, you can avoid executing tests that may be misleading or erroneous.

[0142] Accuracy assessment is mainly to determine whether the tracking point demand information is accurate, such as the value range of parameters, the definition of trigger conditions, etc. Accuracy assessment can help discover and correct possible errors or ambiguous demand descriptions.

[0143] It should be understood that if a third user subsequently modifies the burying point demand information according to the reminder information, the third user can re-upload new burying point demand information, and the electronic device re-obtains the new burying point demand information uploaded by the user and re-evaluates the new burying point demand information.

[0144] In one possible implementation, Figure 2 A flow chart of the evaluation of the tracking point demand information provided by the embodiment of the present invention is as follows: Figure 2 As shown in the figure, the overall operation process of evaluating the tracking point demand information includes the following steps:

[0145] Step b1, upload the tracking point demand information;

[0146] Step b2: determine whether the format of the tracking point demand information is correct;

[0147] If not, proceed to step b3; if so, proceed to step b4.

[0148] Step b3: If an error occurs in uploading the tracking point demand information, the system returns to step b1;

[0149] Step b4: The system prompts that the tracking point demand information has been uploaded successfully, and then proceeds to step b5;

[0150] Step b5: Evaluate the tracking point demand information;

[0151] Step b6: Determine whether the evaluation is successfully performed;

[0152] If not, return to step b5; if so, execute step b7.

[0153] Step b7, generating evaluation results;

[0154] Step b8: Determine whether the tracking point demand information needs to be modified;

[0155] If not, the evaluation process ends; if so, execute step b9.

[0156] Step b9, edit the tracking point demand information;

[0157] Step b10: Submit the edited tracking point requirement information;

[0158] Step b11, generating new tracking point demand information;

[0159] Step b12: Notify relevant personnel and end the evaluation process.

[0160] Step a2: Process the business requirement information, the embedding requirement information, the first user behavior information and the target test scenario information through the test case generation module of the test case generation model to generate a target test case.

[0161] Among them, the test case generation module automatically generates more comprehensive target test cases based on the automatically combined possible target test scenario information, combined with business demand information, tracking demand information, and first user behavior information. Specifically, each target test case includes specific and detailed preconditions, operation steps, and expected results, which can not only accurately test various tracking requirements, but also provide a basis for subsequent test result verification.

[0162] Furthermore, a first confirmation request for requesting the first user to determine whether the target test case needs to be modified can be output, and first feedback information returned by the first user based on the first confirmation request can be obtained. The first feedback information is used to indicate whether the target test case needs to be modified. When the first feedback information indicates that the target test case needs to be modified, a new target test case modified based on the target test case can be obtained.

[0163] It should be understood that the new target test case is obtained by the first user modifying the target test case.

[0164] In this technical solution, a manual verification and modification function for the target test cases is provided, which can enable timely manual access when there are problems with the target test cases generated by the test case generation model, thereby avoiding the failure of the embedded test caused by the subsequent execution of erroneous target test cases, and effectively improving the test accuracy.

[0165] S103: Execute the target test case to perform a point-by-point test on the application to be tested, and obtain the test data generated during the test process.

[0166] Specifically, this step includes two parts: one is to convert the target test case into an automated target test script according to the target test case and the coding strategy, and the other is to execute the target test script to perform automated testing on the application to be tested.

[0167] It should be understood that the target test case may be a target test case output by a test case generation model, or may be a new target test case that has been manually modified.

[0168] It is worth noting that the process of converting the target test case into the target test script also needs to provide the ability of manual verification and modification. Specifically, after the target test case is converted into the target test script, a second confirmation request is output to request the second user to determine whether the target test script needs to be modified, and the second feedback information returned by the second user based on the second confirmation request is obtained. Among them, the second feedback information is used to indicate whether the target test script needs to be modified. When the second feedback information indicates that the target test script needs to be modified, a new target test script modified based on the target test script can also be obtained.

[0169] It should be understood that the third user, the second user and the first user may be the same staff member or different staff members.

[0170] Similar to the target test case, this technical solution provides a manual verification and modification function for the target test script, which can provide timely manual intervention when there are problems with the generated target test script, thereby avoiding the failure of the target test script execution due to script syntax problems, and the failure of the embedded test caused by the execution of the erroneous target test script, thereby effectively improving the test accuracy.

[0171] Optionally, when executing a target test script to perform automated testing on the application to be tested, the target test script may be automatically generated, or may be a new target test script obtained after manual modification.

[0172] Optionally, the test data generated during the test process includes, but is not limited to, the execution status of the target test case, the triggered tracking events, the generated parameters, etc., so as to facilitate subsequent verification of the test results.

[0173] S104: Compare the test data with the preset expected data to obtain the test results.

[0174] Specifically, this step compares the test data generated by the test process with the expected data to verify the correctness of the embedded test. If the test data is consistent with the expected data, it means that the test result is correct; otherwise, it means that the test result is defective.

[0175] In summary, Figure 3 Schematic diagram of the process of the buried point test method provided by the embodiment of the present invention Figure 2 ,like Figure 3 As shown in the figure, the complete point tracking test process includes the following steps:

[0176] Step d1, upload business demand information, embedding demand information, and first user behavior information;

[0177] Step d2: determine whether the formats of the business demand information, the embedding demand information, and the first user behavior information are correct;

[0178] If not, execute step d3; if so, execute step d4.

[0179] Step d3: prompt information upload error, return to step d1;

[0180] Step d4: The message is uploaded successfully, and the process continues to step d5;

[0181] Step d5, evaluating the tracking point demand information;

[0182] Step d6, determining whether the evaluation is successfully performed;

[0183] If not, return to step d5; if so, execute step d7.

[0184] Step d7, generating a target test case through a test case generation model;

[0185] Step d8: Determine whether the target test case needs to be modified;

[0186] If yes, go to step d9; if no, go to step d11.

[0187] Step d9, modify the target test case and execute step d10;

[0188] Step d10, submit the modified target test case and execute step d11;

[0189] Step d11, generating a target test script;

[0190] Step d12: determine whether the target test script needs to be modified;

[0191] If yes, execute step d13; if no, execute step d15.

[0192] Step d13, modify the target test script and execute step d14;

[0193] Step d14, submit the modified target test script and execute step d15;

[0194] Step d15, executing the target test script;

[0195] Step d16, determining whether the target test script is executed successfully;

[0196] If not, execute step d17; if yes, execute step d18.

[0197] Step d17: prompt execution error, return to step d1;

[0198] Step d18, generating test results;

[0199] Step d19, determining whether the test result is correct;

[0200] If yes, the test process ends; if no, execute step d20.

[0201] Step d20: automatically submit error information and end the test process.

[0202] The buried point test method provided by the present invention processes the acquired business demand information, buried point demand information, and the first user behavior information of the application to be tested through the test case model to generate a target test case. Next, the target test case is converted into a corresponding target test script, and the buried point test is performed on the application to be tested by executing the target test script, and the test data generated by the test process is obtained. Finally, the test data is compared with the preset expected data to obtain the test result. It can be seen that the present invention has the functions of automatically combining test scenarios, automatically generating test cases, automatically executing test scripts, automatically comparing data, and automatically generating test results. The entire buried point test process is automatically executed, and it is no longer necessary to design and execute test cases by developers, which improves the efficiency of buried point testing. At the same time, the present invention can also evaluate the buried point requirements and provide an optimization solution; it can also modify the test cases and test scripts, while improving the coverage of the test cases through the test case generation model, it also ensures the accuracy of the buried point test.

[0203] Next, the training process of the test case generation model is explained in detail.

[0204] In one possible implementation, the training process of the test case generation model includes:

[0205] Step c1: obtain multiple sample test cases for the sample application, as well as sample business requirement information, sample embedding requirement information, sample test scenario information, and sample user behavior information using the sample application corresponding to each sample test case.

[0206] Among them, sample test cases, sample business requirement information, sample tracking requirement information, and sample test scenario information are used to train the correlation between tracking requirements and test cases, and sample user behavior information is used to supplement tracking requirements and improve the coverage of test cases.

[0207] Step c2: Perform model training based on each sample test case and the sample business requirement information, sample embedding requirement information, sample test scenario information, and sample user behavior information corresponding to each sample test case to generate a test case generation model.

[0208] Specifically, according to the sample business requirement information, sample embedding requirement information, sample test scenario information and sample user behavior information corresponding to each sample test case, the first module of the initial model is trained to obtain a test scenario generation module.

[0209] Furthermore, according to each sample test case, sample business requirement information, sample embedding requirement information, sample test scenario information, and sample user behavior information, the second module of the initial model is trained to obtain a test case generation module, and then a test case generation model is obtained. The test case generation model includes a test scenario generation module and a test case generation module.

[0210] Optionally, model training can be performed based on each sample test case and the sample business requirement information, sample embedding requirement information, sample test scenario information and sample user behavior information corresponding to each sample test case. Model training can also be performed in combination with the historical defects of each sample test case to improve the accuracy of the target test cases generated by the test case generation model in the subsequent reasoning process.

[0211] It should be understood that model training is performed through each sample test case and the sample business requirement information, sample embedding requirement information, sample test scenario information and sample user behavior information corresponding to each sample test case, so that the test case generation model can learn the correlation between the business requirement information, embedding requirement information, test scenario information, user behavior information and the test case, thereby improving the accuracy of the model reasoning process and the coverage of the test scenario.

[0212] Since the model needs to be continuously learned and trained, in the initial stage of use, there will inevitably be some problems in the construction of the target test cases by the test case generation model. Therefore, a large amount of training data is required, and the corresponding correction capabilities need to be increased to achieve continuous training and optimization of the test case generation model.

[0213] For example, if an error is found in the operation steps of a target test case after the target test case is generated, the operation steps of the target test case can be adjusted, and the adjusted target test case can be used as a new training sample and fed back to the test case generation model. Similarly, the user behavior information corresponding to the execution of the adjusted target test case can also be fed back to the test case generation model.

[0214] Specifically, the second user behavior information collected after executing the new target test case can be obtained, and the sum of the first user behavior information and the second user behavior information can be determined as the target user behavior. According to the new target test case, and the target user behavior information, business requirement information, and tracking requirement information corresponding to the new target test case, the test case generation model is updated and trained to generate an updated test case generation model.

[0215] That is to say, the test case generation model also provides the ability of manual verification and modification. If editing and correction are performed, the correction results are fed back to the test case generation model for model optimization training to improve the accuracy of the embedded point test.

[0216] In one possible implementation, Figure 4 Schematic diagram of the training process of the test case generation model provided in the embodiment of the present invention Figure 1 ,like Figure 4 As shown, the tracking point requirement information, business requirement information, and first user behavior information can be input into the test case generation model. The test case generation model outputs the target test case and converts the target test case into a target test script based on the writing strategy. Finally, the target test script is executed to generate the test results and the collected first user behavior information for use in the next model reasoning.

[0217] Next, a specific embodiment is used to Figure 1 The buried point test method shown is further explained.

[0218] This embodiment provides a point-of-use test system. Figure 5 The system architecture diagram of the buried point test method provided by the embodiment of the present invention is as follows: Figure 5 As shown in the figure, the tracking point test system consists of four parts, namely, participants, tracking point display platform, tracking point test platform, and vehicle-mounted applications. The participants include testers, product teams, data analysts, and developers; the tracking point display platform includes tracking point front-end display, information upload, result display, and editing functions; the tracking point test platform includes tracking point display unit, test case generation unit, automated test unit, tracking point measurement unit, tracking point data storage unit, and intelligent algorithm unit; the vehicle-mounted application includes application programs and cockpits.

[0219] Specifically, the tracking point display unit includes tracking point data processing. First, the uploaded business demand information, tracking point demand information, and first user behavior information are converted into a format that is easy to manage and query, and stored in the tracking point data storage unit. Then, the tracking point data is read through the tracking point front-end display of the tracking point display platform and displayed to the product team and testers for easy query. In addition, the tracking point display unit will also provide an external interface for subsequent data comparison functions.

[0220] The test case generation unit includes big data processing, manual correction, controller area network (CAN) signal, and backend processing. The uploaded business demand information, embedded point demand information, and first user behavior information are processed through the test case generation model to obtain the target test case.

[0221] Specifically, the test case generation model includes a test scenario generation module, which can be implemented as a decision tree. Based on the input business demand information, tracking demand information, and first user behavior information, the decision tree uses deep learning and reinforcement learning to deeply understand the tracking demand information and business demand information, and automatically analyzes possible target test scenario information, ensuring high coverage of the test case scenario without redundancy.

[0222] For example, if a tracking requirement is to report search behavior, then possible test scenarios include: searching for correct keywords, searching for incorrect keywords, searching for empty, searching for abnormal characters, searching for blacklisted characters, searching for overlong characters, etc. For example, if three tracking requirements are a link, click the navigation button to enter the navigation, search in the navigation, and click to exit the navigation, then when designing test cases, you can combine them into one test case according to the scenarios, and complete the test of the three tracking events at one time to avoid redundancy of test cases.

[0223] In one possible implementation, Figure 6 A schematic diagram of a tracking point test scenario provided by an embodiment of the present invention, such as Figure 6 As shown, the scenario includes:

[0224] Relevant staff upload the tracking demand information and business demand information through the tracking display platform, and the tracking test platform (backstage) detects the tracking demand information and business demand information to determine whether the data meets the format requirements. After determining that the tracking demand information and business demand information have passed the test, feature extraction is performed on the tracking demand information and business demand information. At the same time, a query is performed in the user database to obtain user operation habits and user behavior logs (first user behavior information), and feature extraction is performed on the first user behavior information. After that, the extracted features are input into the test case generation model to generate the target test case. Finally, the tracking test is performed based on the target test case, and the test data generated by the tracking test is tested, and the test results are notified to the business integrator.

[0225] Furthermore, the model training process is further explained.

[0226] Step 1: Data preparation and data preprocessing.

[0227] (1) Data sources include historical test cases, corresponding historical tracking demand information, corresponding historical business demand information, historical defects, historical user behavior logs, historical user operation habit big data, and historical test scenario information.

[0228] It should be understood that historical test cases are the sample test cases mentioned above, historical embedding demand information is the sample embedding demand information mentioned above, historical business demand information is the sample business demand information mentioned above, historical user behavior logs and historical user operation habit big data are the sample user behavior information mentioned above, and historical test scenario information is the sample test scenario information.

[0229] (2) Data preparation: Clear useless characters, perform word segmentation, part-of-speech tagging, and perform named entity recognition and relationship extraction on historical test cases, historical tracking demand information, historical business demand information, historical defects, historical user behavior logs, historical user operation habit big data, and historical test scenario information, and classify the processed historical test cases, historical tracking demand information, historical business demand information, historical defects, historical user behavior logs, historical user operation habit big data, and historical test scenario information according to labels as input features. The specific data label classification is shown in the following table:

[0230]

[0231] (3) After data cleaning, historical user behavior logs and historical user operation habit big data are subjected to feature extraction. For example, the features based on operation events, operation categories, operation counts, operation frequencies, operation sequences, and user attributes are extracted. The specific extraction rules are shown in the following table:

[0232]

[0233] (4) Preprocessing: For historical test cases, historical tracking demand information, historical business demand information, historical defects, historical user behavior logs, historical user operation habit big data, and historical test scenario information, word segmentation and word embedding and other preprocessing are required, and the vector size needs to be adjusted and the context needs to be considered. For historical user behavior logs and historical user operation habit big data, data standardization conversion is required.

[0234] For example, for the user's click frequency, normalization is required because the range of values ​​varies greatly, and the range of all features is unified to 0-1. For another example, the functions clicked by users (such as search, share, navigation, etc.) can be regarded as category features, and binary encoding is used to give each category a unique integer label. Assuming there are 20 categories, they can be labeled 0-19 respectively, and each integer label is converted to a binary representation. For example, search can be encoded as 00000, share as 00001, and navigation as 10011. If there are missing values ​​in the data, they need to be filled with the mean, median, or mode.

[0235] (5) After preprocessing, the data is divided into training set, validation set and test set. Attention should be paid to the data correlation of the training set to ensure that the test case generation model can accurately understand the relationship between the tracking requirements, test cases and user behavior, so as to generate effective test cases.

[0236] Step 2, model building: First receive the data processed in step 1. Add one or more recurrent neural network (RNN) layers to process the output of the embedding layer. The RNN layer returns a new sequence for the input of the next layer. Next, add an output layer to generate the final prediction based on the output of the RNN layer.

[0237] Step 3: Model training and test case generation: After building the test case generation model, use the constructed training data set to train the model. When the model is fully trained, test cases can be generated for the uploaded business demand information, embedding demand information, and first user behavior information.

[0238] Specifically, according to the target test scenario generated by the test scenario generation module, the test case generation module will automatically generate the target test case. The format of each target test case must conform to the format of the embedded test case, that is, including the module, event number, event function description, test case title, precondition, test steps, expected results, expected reporting attributes, whether it is empty, whether it is required, and test case type. Among them, the test case type is mainly used by vehicle test automation to distinguish between upper-level user interface (User Interface, UI) automation and CAN signal automation, and is used to convert the target test case into a target test script later.

[0239] The automated test unit includes test case automatic conversion, data comparison, UI automated testing, and CAN signal automated testing. For the automatically generated target test cases, they are converted into automated target test scripts through semantic recognition and other capabilities, combined with the upper-level UI automation and CAN signal automation tags provided by the target test cases, combined with the language requirements and rules of the automation framework.

[0240] After the conversion is successful, the automation execution starts directly. The automation framework can be deployed to the server and connected to the real device. The system will automatically execute according to the operation steps in the test case and collect the data generated during the execution, including tracking events, parameter values, etc., store them in the tracking data storage unit, and upload them to the tracking display platform.

[0241] The tracking point test platform obtains the generated test data through the interface, compares it with the expected data, obtains the final test results, and stores the test results in the tracking point data storage unit. If the test data is consistent with the expected data, it means the test is successful; otherwise, it means there is a problem and the error information is automatically submitted.

[0242] The embedding test includes embedding test and background processing. Through the test case generation model provided in step 2, the evaluation instructions can be input at the same time to evaluate the embedding demand information and finally generate evaluation opinions. It should be understood that by introducing artificial intelligence and machine learning technologies into the formulation and testing of data embedding through the test case generation model, it can not only automatically generate high-quality target test cases, but also perform quality evaluation on the current embedding demand information. During use, through the adjustment, correction and other operations of relevant personnel during use, the test case generation model is allowed to continue to learn and improve in repeated tests, thereby improving the accuracy and reliability of the embedding test.

[0243] The buried point test method provided by the present invention processes the business demand information, buried point demand information, and the first user behavior information of the application to be tested obtained by the test scenario generation module of the test case model, and generates corresponding target test scenario information. Then, the business demand information, buried point demand information, the first user behavior information, and the target test scenario information are processed by the test case generation module of the test case generation model to generate corresponding target test cases. The test case generation model can automatically generate a relatively comprehensive target test case, thereby improving the coverage of the buried point test. Next, the target test case is converted into a corresponding target test script, and the buried point test is performed on the application to be tested by executing the target test script, and the test data generated by the test process is obtained. Among them, a modification function is also provided for the target test case and the target test script to avoid executing tests that may be misleading or erroneous, thereby improving the accuracy of the buried point test. Finally, the test data is compared with the preset expected data to obtain the test result. It can be seen that the present invention has the functions of automatically combining test scenarios, automatically generating test cases, automatically executing test scripts, automatically comparing data, and automatically generating test results. The entire process of the buried point test is automatically executed, and it is no longer necessary for developers to design and execute test cases, which improves the efficiency of the buried point test. In addition, the present invention can also evaluate the buried point requirements, provide optimization solutions, and improve the accuracy of the buried point test.

[0244] Figure 7 This is a schematic diagram of the structure of the buried point test device provided by the present invention. Figure 7 As shown, the buried point testing device 700 includes: an acquisition module 701, an input module 702, an execution module 703, and a comparison module 704;

[0245] The acquisition module 701 is used to acquire the business requirement information, tracking requirement information, and the first user behavior information of the application to be tested;

[0246] An input module 702 is used to input the business requirement information, the embedding requirement information and the first user behavior information into the test case generation model, and obtain the target test case output by the test case generation model;

[0247] An execution module 703 is used to execute a target test case to perform a point-test on the application to be tested and obtain test data generated during the test process;

[0248] The comparison module 704 is used to compare the test data with the preset expected data to obtain the test result.

[0249] Furthermore, the input module 702 is specifically used for:

[0250] The business requirement information, the embedding requirement information, and the first user behavior information are processed by the test scenario generation module of the test case generation model to generate target test scenario information;

[0251] The test case generation module of the test case generation model processes the business requirement information, the embedding point requirement information, the first user behavior information and the target test scenario information to generate the target test case.

[0252] Further, the execution module 703 is specifically used to:

[0253] Generate target test scripts based on target test cases and code writing strategies;

[0254] Execute the target test script to perform point-by-point testing on the application to be tested and obtain the test data generated during the testing process.

[0255] Furthermore, the buried point testing device 700 also includes:

[0256] An output module, used for outputting a first confirmation request, where the first confirmation request is used for requesting the first user to determine whether the target test case needs to be modified;

[0257] An acquisition module 701 is used to acquire first feedback information returned by the first user based on the first confirmation request, where the first feedback information is used to indicate whether the target test case needs to be modified;

[0258] The acquisition module 701 is further configured to acquire a new target test case based on the modified target test case when the first feedback information indicates that the target test case needs to be modified.

[0259] Furthermore, the output module is further used to output a second confirmation request, where the second confirmation request is used to request the second user to determine whether the target test script needs to be modified;

[0260] The acquisition module 701 is further used for:

[0261] Acquire second feedback information returned by the second user based on the second confirmation request, where the second feedback information is used to indicate whether the target test script needs to be modified;

[0262] When the second feedback information indicates that the target test script needs to be modified, a new target test script based on the modified target test script is obtained.

[0263] Furthermore, the input module 702 is specifically used for:

[0264] Through the embedding point evaluation module of the test case generation model, the embedding point demand information is evaluated and processed to generate evaluation results;

[0265] When the evaluation result indicates that the tracking point demand information passes the evaluation, the business demand information, the tracking point demand information, and the first user behavior information are processed by a test scenario generation module of the test case generation model;

[0266] The evaluation process includes at least one of the following: determining whether the data of the tracking point demand information is complete, determining whether there is a logical conflict in the tracking point demand information, and determining whether the tracking point demand information meets the business demand information and preset conditions;

[0267] The output module is also used to output reminder information when the evaluation results indicate that the tracking point demand information has not passed the evaluation, and the reminder information is used to remind the third user to modify the tracking point demand information.

[0268] Furthermore, before obtaining the business requirement information, the tracking requirement information, and the first user behavior information of the application to be tested, the tracking test device 700 further includes:

[0269] The acquisition module 701 is further used to acquire multiple sample test cases for the sample application, as well as sample business requirement information, sample embedding requirement information, sample test scenario information, and sample user behavior information using the sample application corresponding to each sample test case;

[0270] The training module is used to perform model training based on each sample test case and the sample business requirement information, sample embedding requirement information, sample test scenario information and sample user behavior information corresponding to each sample test case, and generate a test case generation model.

[0271] Furthermore, the training module is specifically used to:

[0272] According to the sample business requirement information, sample embedding requirement information, sample test scenario information and sample user behavior information corresponding to each sample test case, the first module of the initial model is trained to obtain a test scenario generation module;

[0273] Model training is performed according to each sample test case and the sample business requirement information, sample embedding requirement information, sample test scenario information and sample user behavior information corresponding to each sample test case, the second module of the initial model is trained, and the test case generation module is obtained to obtain the test case generation model.

[0274] Further, if the target test case is a new target test case, after executing the target test case to perform a tracking test on the application to be tested, the tracking test device 700 further includes:

[0275] The acquisition module 701 is further used to acquire the second user behavior information collected after executing the new target test case;

[0276] A determination module, configured to determine the sum of the first user behavior information and the second user behavior information as target user behavior information;

[0277] The training module is also used to update the test case generation model according to the new target test cases and the target user behavior information, business requirement information and tracking requirement information corresponding to the new target test cases, and generate an updated test case generation model.

[0278] The buried point testing device provided in an embodiment of the present invention can be used to execute the buried point testing method in any of the above embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0279] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0280] Figure 8 The hardware structure diagram of the electronic device provided by the present invention. Figure 8 As shown, the electronic device 800 includes:

[0281] Processor 801 and memory 802;

[0282] Memory 802 stores computer executable instructions;

[0283] The processor 801 executes the computer execution instructions stored in the memory 802, so that the electronic device 800 performs the above-mentioned buried point testing method.

[0284] The electronic device provided in an embodiment of the present invention can be used to execute the buried point testing method provided in any of the above method embodiments. Its implementation principle and technical effect are similar and will not be repeated here.

[0285] Fig. 9 The hardware structure diagram of the vehicle provided by the present invention. Fig. 9 As shown, the vehicle 900 includes:

[0286] Vehicle body 901 and vehicle controller 902

[0287] The vehicle controller includes a processor 9021 and a memory 9022;

[0288] Memory 9022 stores computer-executable instructions;

[0289] The processor 9021 executes the computer execution instructions stored in the memory 9022, so that the vehicle 900 performs the buried point testing method as described above.

[0290] The vehicle provided in an embodiment of the present invention can be used to execute the buried point testing method provided in any of the above-mentioned method embodiments. The implementation principle and technical effect are similar and will not be repeated here.

[0291] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0292] The memory may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a disk or an optical disk, etc.

[0293] An embodiment of the present invention also provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the above-mentioned tracking point testing method.

[0294] An embodiment of the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to implement the above-mentioned embedded point testing method.

[0295] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0296] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0297] It should be understood that the above device embodiments are only illustrative, and the device of the present invention can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0298] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present invention may be integrated into one unit / module, or each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0299] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. If not specifically stated, the processor may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. If not specifically stated, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc.

[0300] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0301] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0302] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The description and examples are to be considered exemplary only, and the true scope and spirit of the present invention is indicated by the following claims.

[0303] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A buried point testing method, characterized in that: include: Obtaining business requirement information, tracking requirement information, and first user behavior information of the application to be tested; Input the business requirement information, the embedding requirement information and the first user behavior information into a test case generation model, and obtain a target test case output by the test case generation model; Execute the target test case to perform a point-by-point test on the application to be tested, and obtain test data generated during the test process; The test data is compared with preset expected data to obtain a test result.

2. The method according to claim 1, characterized in that The step of inputting the business requirement information, the embedding requirement information, and the first user behavior information into a test case generation model, and obtaining a target test case output by the test case generation model, includes: The business requirement information, the embedding requirement information and the first user behavior information are processed by the test scenario generation module of the test case generation model to generate target test scenario information; The business requirement information, the embedding point requirement information, the first user behavior information and the target test scenario information are processed by the test case generation module of the test case generation model to generate the target test case.

3. The method according to claim 1 or 2, characterized in that: The executing the target test case to perform a tracking test on the application to be tested and obtaining the test data generated during the test process includes: Generate a target test script according to the target test case and the code writing strategy; Execute the target test script to perform a point-by-point test on the application to be tested, and obtain the test data generated during the test process.

4. The method according to claim 1 or 2, characterized in that: The method further comprises: Outputting a first confirmation request, where the first confirmation request is used to request the first user to determine whether the target test case needs to be modified; Acquire first feedback information returned by the first user based on the first confirmation request, where the first feedback information is used to indicate whether the target test case needs to be modified; When the first feedback information indicates that the target test case needs to be modified, a new target test case modified based on the target test case is obtained.

5. The method according to claim 3, characterized in that: The method further comprises: Outputting a second confirmation request, where the second confirmation request is used to request a second user to determine whether the target test script needs to be modified; Acquire second feedback information returned by the second user based on the second confirmation request, where the second feedback information is used to indicate whether the target test script needs to be modified; When the second feedback information indicates that the target test script needs to be modified, a new target test script modified based on the target test script is obtained.

6. The method according to claim 2, characterized in that The test scenario generation module of the test case generation model processes the business requirement information, the embedding requirement information, and the first user behavior information to generate target test scenario information, including: The embedding point evaluation module of the test case generation model is used to evaluate the embedding point demand information and generate an evaluation result; When the evaluation result indicates that the tracking point demand information has passed the evaluation, the business demand information, the tracking point demand information and the first user behavior information are processed by the test scenario generation module of the test case generation model to generate the target test scenario information; The evaluation process includes at least one of the following: determining whether the data of the burying point demand information is complete, determining whether there is a logical conflict in the burying point demand information, and determining whether the burying point demand information meets the business demand information and preset conditions.

7. The method according to claim 6, characterized in that The method further comprises: When the evaluation result indicates that the burying point demand information has not passed the evaluation, a reminder message is output, where the reminder message is used to remind the third user to modify the burying point demand information.

8. The method according to claim 4, characterized in that Before obtaining the business requirement information, tracking requirement information, and first user behavior information of the application to be tested, the method further includes: Obtain multiple sample test cases for the sample application, as well as sample business requirement information, sample embedding requirement information, sample test scenario information, and sample user behavior information using the sample application corresponding to each sample test case; Model training is performed based on each sample test case and the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case to generate the test case generation model.

9. The method according to claim 8, characterized in that The performing of model training according to each sample test case and the sample business requirement information, the sample embedding requirement information, the sample test scenario information, and the sample user behavior information corresponding to each sample test case to generate the test case generation model includes: According to the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case, the first module of the initial model is trained to obtain the test scenario generation module; Model training is performed according to each sample test case and the sample business requirement information, the sample embedding requirement information, the sample test scenario information and the sample user behavior information corresponding to each sample test case, the second module of the initial model is trained, and the test case generation module is obtained to obtain the test case generation model.

10. The method according to claim 8, characterized in that If the target test case is the new target test case, after executing the target test case to perform a point-test on the application to be tested, the method further includes: Obtaining second user behavior information collected after executing the new target test case; determining the sum of the first user behavior information and the second user behavior information as target user behavior information; According to the new target test case, and the target user behavior information, the business requirement information, and the embedding requirement information corresponding to the new target test case, the test case generation model is updated and trained to generate an updated test case generation model.

11. A buried point testing device, characterized in that: include: An acquisition module, used to acquire business requirement information, tracking requirement information, and first user behavior information of the application to be tested; An input module, used to input the business requirement information, the embedding requirement information and the first user behavior information into a test case generation model, and obtain a target test case output by the test case generation model; An execution module is used to execute the target test case to perform a point-by-point test on the application to be tested and obtain test data generated during the test process; The comparison module is used to compare the test data with preset expected data to obtain the test results.

12. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 10 when executed by a processor.

14. A vehicle, characterized in that: include: A vehicle body and a vehicle controller; Wherein, the vehicle controller includes a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 10.

Images