A test method, system, device, program product and storage medium

Abstract

The present application discloses a testing method, system, device, program product and storage medium. The method comprises the following steps: obtaining a video to be tested and a standard video, wherein the standard video is a video containing known correct content and used as a comparison benchmark, and the video to be tested is used to characterize the display content of an environmental information display module to be tested; marking test features in the video to be tested and the standard video based on a detection model, respectively, wherein the test features are information with visual characteristics in the video to be tested and the standard video that is identified and marked by the detection model; the detection model is pre-trained and used to identify and mark the test features in the video to be tested and the standard video; and comparing the test features in the video to be tested and the standard video frame by frame to obtain a test report of the video to be tested, thereby improving the accuracy and consistency of the test, shortening the test time and reducing the test cost.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a testing method, system, device, program product and storage medium. Background Art

[0002] In order to improve test efficiency and simulate more realistic driving scenarios, experienced testers tend to integrate multiple test cases to build more complex and comprehensive test scenarios. This strategy allows multiple functions to be evaluated simultaneously during a single test. For example, while verifying the navigation on Autopilot (NOA) NOA animation, the responsiveness and accuracy of icons and buttons are also checked. However, this integration also brings challenges: as the complexity of the test scenarios increases, the number of elements that testers need to monitor and evaluate at the same time also increases, and testers may not be able to maintain their best state at all times. This increases the risk of errors or anomalies being missed and reduces test accuracy.

[0003] Testing each category of scenarios separately and independently, such as NOA animation, Lane Centering Control (LCC) animation, obstacle perception, icons and buttons, and active safety, can ensure the accuracy of the test, but different test categories may involve similar or identical driving scenarios, which will increase the number of repeated tests of the same scenario, thereby extending the test time. For example, when testing NOA animation and LCC animation, it may be necessary to carry out lane keeping. This means that if these two functions are tested separately, the lane keeping scenario will be tested multiple times, resulting in a waste of resources and time. Therefore, it is currently impossible to increase the test speed while ensuring the accuracy of the test. Summary of the invention

[0004] Based on the above problems, the present application provides a testing method, system, device, program product and storage medium to improve the testing speed, that is, to improve the testing efficiency, while ensuring the testing accuracy.

[0005] To solve the above problems, the technical solutions provided in the embodiments of the present application are as follows:

[0006] The first aspect of the present application provides a testing method, comprising:

[0007] Acquire a video to be tested and a standard video, wherein the standard video is a video containing known correct content and is used as a comparison benchmark, and the video to be tested is used to characterize the display content of the environment information display module to be tested;

[0008] Based on the detection model, the test features in the video to be tested and the standard video are respectively annotated, wherein the test features are information with visual characteristics in the video to be tested and the standard video that is identified and annotated by the detection model; the detection model is pre-trained and used to identify and annotate the test features in the video to be tested and the standard video;

[0009] The test features in the video to be tested and the standard video are compared frame by frame to obtain a test report of the video to be tested.

[0010] In a possible implementation, the frame-by-frame comparison of the test features in the video to be tested and the standard video to obtain a test report of the video to be tested includes:

[0011] According to the video playback order, the test features contained in the corresponding frames of the video to be tested and the standard video are compared to obtain a comparison result, wherein the corresponding frames are frames of the video to be tested and the standard video at the same time point;

[0012] Executing the step of comparing the test features contained in the corresponding frames of the video to be tested and the standard video in the video playback order to obtain the comparison result until all frames of the video to be tested and the standard video are compared;

[0013] The comparison results of each frame of the video to be tested are integrated to form a test report of the video to be tested.

[0014] In a possible implementation, the test feature includes a test element, which is a component with visual characteristics in the video to be tested and the standard video that is identified and marked by the detection model. The comparison of the test features contained in the corresponding frames of the video to be tested and the standard video to obtain a comparison result includes:

[0015] Determine whether the number of test elements in the corresponding frames of the video to be tested and the standard video is the same;

[0016] When there is a difference in the number of test elements in the corresponding frames of the test video and the standard video, the information that the current frame of the video to be tested is in an abnormal state and the number difference is recorded in the comparison result.

[0017] In a possible implementation manner, the test feature further includes attribute information of the test element, and the method further includes:

[0018] When the number of test elements in the corresponding frames of the test video and the standard video is the same, determining whether there is a difference in attribute information of the test elements in the corresponding frames of the test video and the standard video;

[0019] When there is a difference in the attribute information of the test element in the corresponding frames of the test video and the standard video, the abnormal state of the current frame of the video to be tested and the attribute difference information are recorded in the comparison result.

[0020] In a possible implementation, the test feature further includes a user interaction effect of the test element, and the method further includes:

[0021] When the attribute information of the test element in the corresponding frame in the test video and the standard video is the same, determining whether there is a difference in the user interaction effect of the test element in the corresponding frame in the test video and the standard video;

[0022] When there is a difference in the user interaction effects of the test elements in the corresponding frames in the test video and the standard video, the information that the current frame of the video to be tested is in an abnormal state and the difference in the user interaction effects is recorded in the comparison result.

[0023] In a possible implementation, the test report includes a first integration result presented in a first format and a second integration result presented in a second format, and the comparison result of each frame of the video to be tested is integrated to form the test report of the video to be tested, including:

[0024] Based on the first format, the frames of the video to be tested in an abnormal state and the difference information corresponding to each frame in the abnormal state are integrated to obtain a first integration result; based on the second format, the frames of the video to be tested in a normal state are integrated to obtain a second integration result.

[0025] In a possible implementation, before obtaining the video to be tested and the standard video, the process further includes:

[0026] The test node obtains a test task issued by the cloud platform, where the test task is created by the cloud platform and is used to instruct the test node to perform a test operation;

[0027] After the test node establishes a connection with the cloud platform through the software development kit and receives the task data information of the test task, the test node starts the image detection software including the detection model;

[0028] The test node uses the image detection software to re-inject task data information and record the content presented by the human-computer interface to obtain a video to be tested, and the video to be tested is the input data of the test task.

[0029] A second aspect of the present application provides a testing system, comprising:

[0030] A first acquisition unit is used to acquire a video to be tested and a standard video, wherein the standard video is a video containing known correct content and used as a comparison reference, and the video to be tested is used to represent the display content of the environment information display module to be tested;

[0031] A labeling unit, used for labeling the test features in the video to be tested and the standard video respectively based on the detection model, wherein the test features are information with visual characteristics in the video to be tested and the standard video that is identified and labeled by the detection model; the detection model is pre-trained and used for identifying and labeling the test features in the video to be tested and the standard video;

[0032] The first comparison unit is used to compare the test features in the video to be tested and the standard video frame by frame to obtain a test report of the video to be tested.

[0033] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the test method described in the first aspect is implemented.

[0034] A fourth aspect of the present application provides a computer program product. When the computer program product is run on a computer, the computer executes the test method as described in the first aspect.

[0035] A fifth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the test method as described in the first aspect above.

[0036] Compared with the prior art, this application has the following beneficial effects:

[0037] The present application is based on the detection model, and the test features in the video to be tested and the standard video are marked respectively, and the test features in the video to be tested and the standard video are compared frame by frame to obtain the test report of the video to be tested. The method of the present application allows multiple test features in the video to be tested and the standard video to be tested simultaneously in the same test time period. For example, while verifying the NOA motion effect by comparing the test features, the responsiveness and accuracy of the icons and buttons can be checked by comparing the test features in the same time period. Compared with the traditional method of manually integrating multiple test cases to build complex test scenarios, this method greatly improves the test efficiency. In addition, the traditional manual testing method is easily affected by the subjective factors of the tester, such as fatigue, distraction, etc., resulting in inconsistency in the test results. Through automated video comparison, it can be ensured that the execution of each test is consistent, eliminating the possibility of human error. Automated tools can be tested in strict accordance with predetermined standards without external interference, thereby improving the accuracy of test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A flow chart of a testing method provided in an embodiment of the present application;

[0040] Figure 2 A test flow chart provided for an embodiment of the present application;

[0041] Figure 3 A schematic diagram of the image detection software structure provided in the embodiment of the present application;

[0042] Figure 4 A structural diagram of a test system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] To facilitate understanding of the technical solutions provided by the embodiments of the present application, the professional terms involved in the embodiments of the present application will be explained below.

[0045] Environment Information Display (EID) is a technology that presents the vehicle's surrounding environment information, vehicle status information and driving assistance information to the driver in an intuitive and easy-to-understand way. It combines advanced sensor technology, image processing technology and human-computer interaction design to provide drivers with comprehensive and real-time driving assistance information, improving driving safety and convenience.

[0046] NOA animation refers to the animation effects displayed by the EID system when the vehicle is driving automatically according to the navigation route in the Navigate on Autopilot mode, such as lane change instructions and path planning.

[0047] LCC animation refers to the animation of Lane Centering Control, which refers to the visual feedback provided by the EID system when the vehicle maintains the center of the lane in automatic driving or assisted driving mode.

[0048] To facilitate understanding of the technical solutions provided by the embodiments of the present application, the background technology involved in the embodiments of the present application will be described below.

[0049] As mentioned above, in the process of sharing control between the driver and the driving system, the driver may feel anxious and panic due to unfamiliarity with the new technology or fear of losing control of the vehicle. In order to alleviate this emotion, the EID display system plays a key role. The EID system provides the driver with comprehensive and accurate information about the surrounding environment by sensing the information of all external vehicles. This helps the driver better understand the current driving environment and make more accurate judgments and decisions. Given the important role of the EID system in the process of autonomous driving, it is particularly important to conduct comprehensive testing and verification of it. This can ensure the accuracy and reliability of the EID system, thereby ensuring the safety of the driver and passengers. At the same time, it also helps to discover potential problems and repair and optimize them in a timely manner.

[0050] At present, the test scheme for the five working conditions of EID, namely NOA dynamic effect, LCC dynamic effect, obstacle perception, icons and buttons, and active safety, is manual testing. This test scheme has the following disadvantages:

[0051] Testers need to observe the information on the EID screen with their naked eyes to judge its accuracy and timeliness. However, human eye recognition is subjective and limited, and may not accurately capture all subtle differences or rapidly changing information.

[0052] In order to improve test efficiency and simulate more realistic driving situations, experienced testers tend to combine multiple test cases to build more complex and comprehensive test scenarios. This strategy allows multiple functions to be evaluated simultaneously during a single test. As the complexity of the test scenario increases, the number of elements displayed on the screen at the same time increases dramatically. Testers need to quickly and accurately identify the state changes, interaction effects, and potential errors or anomalies of these elements. This poses a huge challenge to the visual recognition ability of testers, especially when dealing with dynamic effects that change at high speed. In complex test scenarios, testers need to focus on multiple test points at the same time to ensure that nothing is missed. This multitasking requires testers to have a high degree of concentration in order to quickly switch focus and make accurate judgments in a short period of time. Maintaining this high-intensity state of attention for a long time can easily lead to tester fatigue and distraction, which in turn affects the accuracy of test results.

[0053] If each category of scenarios (such as NOA animation, LCC animation, obstacle perception, icons and buttons, active safety) is tested separately, although this method can ensure that each individual function is fully verified. Different test categories may involve similar or identical driving scenarios. For example, when testing NOA animation and LCC animation, both may need to be carried out under lane keeping. This means that if these two functions are tested separately, the lane keeping scenario will be tested multiple times, resulting in a waste of resources. Repeatedly setting up similar test environments not only consumes time, but also requires more manpower to prepare and monitor each test. This is an additional burden for the testing team.

[0054] In order to solve the above problems, the present application embodiment provides a test method for comparing the test features frame by frame by using the video to be tested and the standard video. Use a pre-trained detection model to identify and mark the test features in the video to be tested and the standard video, and compare each frame in the video to be tested and the standard video frame by frame to check whether the test features in the corresponding frames are consistent. This step can ensure that even minor changes can be captured, thereby improving the accuracy and meticulousness of the test. This method no longer relies on the naked eye recognition of the tester, but automatically captures and identifies the test features in the video through the detection model obtained by pre-training. In each video frame, as long as these test features appear in the expected position and conform to the preset style and state, the frame is considered to have passed the test. This frame-by-frame comparison method ensures the accuracy and consistency of the test while avoiding the inaccuracy and subjectivity of human eye recognition.

[0055] With the support of automated scripts and tools, testers can configure and start multiple test cases at one time, which can be executed in parallel to compare the test video with the standard video frame by frame. This parallel testing method greatly improves the test efficiency, allowing testers to complete the execution of a large number of test cases in a short period of time, thereby significantly shortening the test time.

[0056] In the software development process, version iteration is the norm. When faced with frequent version updates, traditional testing methods often require frequent adjustments to test scripts and use cases to adapt to new functions and interface changes. The solution proposed in this application is based on frame-by-frame comparison of test features in video frames, so it has high flexibility and reusability. Even in the new version, as long as the position and style of the test features have not changed fundamentally, the original test scripts and use cases can still be reused without frequent adjustments. This greatly reduces the cost and difficulty of test maintenance, allowing the test team to focus more on testing and verification of new functions.

[0057] In summary, this application effectively solves the problems of high overlap of category scenarios, cross-coverage, and low test efficiency in traditional manual testing by introducing an automated testing method that compares test features frame by frame. This method not only improves the accuracy and consistency of the test, but also greatly shortens the test time and reduces the test cost. At the same time, its high flexibility and reusability also enable it to adapt to the needs of large-scale version iterations, providing strong testing support for software development teams.

[0058] It should be noted that the test method, system, device, program product and storage medium provided in the present application can be applied to the field of computer technology. The above is only an example, and the application field of the test method, system, device, program product and storage medium provided in the present application is not limited. In addition, the embodiment of the present application may not limit the execution subject of the test, for example, the test method of the embodiment of the present application can be applied to data processing equipment such as terminal equipment or server. Among them, the terminal equipment can be electronic devices such as smart phones, computers, personal digital assistants (PDA), tablet computers, etc. The server can be a stand-alone server, a cloud server or a cluster server composed of multiple servers.

[0059] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0060] The following is an example to illustrate a test method provided by the present application. Figure 1 ,Should Figure 1 A flow chart of a testing method provided in an embodiment of the present application, the method comprising:

[0061] S101: Obtain a video to be tested and a standard video.

[0062] The standard video is a video containing known correct content and used as a comparison benchmark. That is, the standard video is a pre-prepared video containing known correct content and covering the correct test case logic, which serves as a comparison benchmark during the test process. The standard video can accurately reflect the expected output of the EID module under ideal conditions.

[0063] The video to be tested is used to characterize the display content of the EID module to be tested. The video is a video actually captured or generated by the EID module, and is used to characterize the display content of the EID module under specific conditions. This video may include the output result of the EID module when processing a certain preset input signal or scene.

[0064] S102: Based on the detection model, respectively label the test features in the video to be tested and the standard video.

[0065] The test features are visually perceptible information points in the test video and the standard video that are identified and annotated by the detection model. The detection model can be pre-built based on machine learning or deep learning technology to automatically identify and annotate the test features in the test video and the standard video.

[0066] In an exemplary implementation, the test feature may include a test element. The test element may be a component of a video frame with visual characteristics, such as text, image, icon, dynamic data, etc. The detection model may analyze the video content frame by frame and identify predefined test elements in the video frame.

[0067] In another exemplary implementation, the test feature may also include attribute information corresponding to each test element. After the detection model analyzes the video content frame by frame and identifies the predefined test elements, the detection model may further annotate the attribute information such as the position, size, color, and shape of each test element.

[0068] In actual application scenarios, the category of test elements identified by the detection model may depend specifically on the display content and test requirements of the EID module.

[0069] S103 : Compare the test features in the video to be tested and the standard video frame by frame to obtain a test report of the video to be tested.

[0070] After the labeling of the video to be tested and the standard video is completed, the test node compares the video to be tested and the standard video frame by frame to check the consistency or difference of the labeled test features in each frame.

[0071] Based on the comparison results, the test node can generate a detailed test report. This report can include an overview of the test process, list all detected differences, and include analysis and evaluation of these differences. The purpose of the test report is to help developers or testers quickly locate the problem and take appropriate measures to repair or optimize it.

[0072] Since the test script focuses on the location and status of the test elements, there is no need to frequently adjust the script when different versions are iterated unless the interface layout changes significantly. This helps reduce test preparation time and allows the test team to respond quickly to version updates.

[0073] In a possible implementation, the frame-by-frame comparison of the test features in the video to be tested and the standard video to obtain a test report of the video to be tested includes:

[0074] Step A1: compare the test features contained in the corresponding frames of the video to be tested and the standard video in the order of video playback to obtain a comparison result, wherein the corresponding frames are frames of the video to be tested and the standard video at the same time point.

[0075] Determine the starting frames of the video to be tested and the standard video to ensure that they are at the same starting point before the comparison begins. In actual application, a data structure such as a list, an array, or a database table can be prepared to store the comparison results of each frame.

[0076] In the order of video playback, read corresponding frames from the video to be tested and the standard video in turn. Corresponding frames refer to frames at the same time point of the two. For each pair of corresponding frames, use the pre-trained detection model to extract test features. These test features can be pre-defined and used to evaluate key components in aspects such as interface layout and content accuracy.

[0077] In actual application scenarios, the test features in the video to be tested and the standard video can be compared frame by frame according to the content of the test features. Exemplarily, when the test features include test elements, the test elements in the video frame to be tested can be compared with the corresponding test elements in the standard video frame; when the test features also include attribute information of the test elements, further, the attribute information of the test elements in the video frame to be tested can be compared with the attribute information of the corresponding test elements in the standard video frame; when the test features also include user interaction effects of the test elements, further, the user interaction effects of the test elements in the video frame to be tested can be compared with the user interaction effects of the corresponding test elements in the standard video frame. The specific comparison process is described in detail below and will not be repeated here.

[0078] Step A2, executing the step of comparing the test features contained in the corresponding frames of the video to be tested and the standard video in the video playback order to obtain the comparison result, until all frames of the video to be tested and the standard video are compared.

[0079] Repeat the above steps until all frames of the video to be tested and the standard video are compared. This means that the entire video playback process is checked one by one, and no frame in the video to be tested or the standard video is missed.

[0080] During the comparison process, the comparison results of each frame can be continuously added to the previously prepared data structure for subsequent integration.

[0081] Step A3: Integrate the comparison results of each frame of the video to be tested to form a test report of the video to be tested.

[0082] After all frames have been compared, the comparison results for each frame are integrated. This process may involve counting the number of frames that have been successfully matched, the number of frames with differences, the type of differences (such as position offset, color mismatch, etc.), and a description of the specific differences. Based on the integrated comparison results, a test report for the video to be tested is generated. The test report is used to display the test process, comparison results, problems found, and suggestions for improvements that can be implemented.

[0083] For example, based on the test requirements and standards, it can be determined which differences are acceptable, i.e., minor deviations that do not affect the function, and which ones require attention, i.e., significant deviations that may affect the user experience or function implementation. The acceptable differences and differences that require attention are presented in different ways in the test report.

[0084] For example, the test report can be output in a readable format, such as PDF, Word document or HTML page, so that testers, developers and other relevant personnel can consult it. The tester or developer analyzes the test report, identifies the problems in the video to be tested, and repairs or optimizes it accordingly. If the test report shows that the performance of the video to be tested meets expectations, it can be considered to have passed the test and is ready for the next step of deployment or use.

[0085] In a possible implementation, comparing the test elements contained in the corresponding frames of the video to be tested and the standard video in sequence according to the video playback order includes:

[0086] Step B1: Determine whether the number of test elements in the corresponding frames of the video to be tested and the standard video is the same. For each pair of corresponding frames, select the corresponding frames in the video to be tested and the standard video for comparison according to the video playback order. Calculate and compare the number of test elements in the corresponding frames of the two videos.

[0087] Step B2: When there is a difference in the number of test elements in the corresponding frames of the test video and the standard video, the information that the current frame of the video to be tested is in an abnormal state and the number difference is recorded in the comparison result.

[0088] If the comparison result indicates that there is a difference in the number of test elements in the corresponding frames of the test video and the standard video, the comparison result of the current frame of the video to be tested is recorded as an abnormal state. This means that the video content has been changed during editing, transmission or processing, or there is an error in the standard video itself.

[0089] When recording abnormal status, detailed abnormal information can be attached, such as missing elements, extra elements, and specific values ​​of differences, etc., to facilitate subsequent analysis and repair.

[0090] Regardless of the comparison result of the current frame, the next pair of corresponding frames will be compared in the video playback order until all frames of the video to be tested and the standard video are compared.

[0091] In a possible implementation, step B1 may further include:

[0092] Step C1: when the number of test elements in the corresponding frames of the test video and the standard video is the same, determine whether there is a difference in attribute information of the test elements in the corresponding frames of the test video and the standard video, wherein the attribute information is used to characterize the visual features of the test elements.

[0093] The attribute information may include, but is not limited to: position (coordinates), size (dimensions), color (RGB value or color name), shape (such as rectangle, circle, etc.), texture, rotation angle (if the test element may be rotated), transparency, etc. These attribute information together constitute the visual characteristics of the test element. Compare the attribute information of each test element in the corresponding frame of the test video and the standard video one by one.

[0094] Exemplarily, for each attribute, a tolerance range can be set. The size of the tolerance range depends on the strictness of the test requirements and the characteristics of the test element. For example, for the position attribute, a deviation of several pixels can be allowed; for the color attribute, a certain degree of color difference can be allowed. If a certain attribute information of a test element exceeds the tolerance range, it is considered that the attribute information has a difference.

[0095] Step C2: When there is a difference in the attribute information of the test element in the corresponding frames of the test video and the standard video, the abnormal state of the current frame of the video to be tested and the attribute difference information are recorded in the comparison result.

[0096] If the attribute information of all test elements in the corresponding frame is successfully compared (ie, there is no difference beyond the tolerance range), the comparison result of the frame is recorded as a normal state.

[0097] If there is any difference in attribute information, the comparison result of the current frame of the video to be tested is recorded as an abnormal state. When recording the abnormal state, it can be further specified which attribute information of which test elements is different, and the specific value or description of the difference, so as to facilitate subsequent analysis and repair.

[0098] Therefore, the above test method not only focuses on whether the number of test elements is consistent, but also goes deep into the attribute information level of the test elements for comparison, so as to more comprehensively evaluate the display quality of the video to be tested and its consistency with the standard video.

[0099] In a possible implementation, step C1 may further include:

[0100] Step D1: when the comparison result indicates that the attribute information of the test element in the corresponding frame in the test video and the standard video is the same, determine whether there is a difference in the user interaction effect of the test element in the corresponding frame in the test video and the standard video.

[0101] Before the comparison, you can predetermine which user interaction effects are considered key test points. User interaction effects may include, but are not limited to: the reaction after clicking the test element (such as pop-up menus, page jumps, element color changes, etc.), the behavior when dragging the test element (such as whether it is smooth, whether there are boundary restrictions, etc.), and the effect when hovering over the test element (such as displaying prompt information, element highlighting, etc.).

[0102] For example, an automated testing tool or script may be used to simulate the user's interactive operation on the test element in actual use. In this process, technical means such as image recognition, coordinate positioning, and mouse event simulation may be integrated according to actual needs.

[0103] In the context of the corresponding frames in the test video and the standard video, perform the same user interaction operation and capture the effect after the operation. Compare the test elements in the two videos one by one to see if the effect after the interaction is consistent. The presentation effect in this process can cover many aspects, such as changes in interface layout, new or disappearing elements, consistency of animation effects, response speed, etc.

[0104] Step D2: When there is a difference in the user interaction effects of the test elements in the corresponding frames of the test video and the standard video, the information that the current frame of the video to be tested is in an abnormal state and the difference in the user interaction effects are recorded in the comparison result.

[0105] If the user interaction effects of all test elements in the corresponding frame are successfully compared, the comparison result of the frame is recorded as a normal state.

[0106] If there are any differences in user interaction effects, whether functional or visual, the comparison results of the current frame of the video to be tested should be recorded as an abnormal state. When recording the abnormal state, the specific manifestations of the differences can be described in detail, such as which interactive operations lead to inconsistent effects, and the possible impact of these inconsistent effects on the user experience.

[0107] Through the above-mentioned process of verifying the user interaction effect, the test process provided in the embodiment of the present application can more comprehensively cover the interaction process between the user and the video content, thereby ensuring that the EID module not only meets the requirements in visual display, but also meets the expected standards in user experience and functional implementation.

[0108] In a possible implementation, the test report includes a first integration result presented in a first format and a second integration result presented in a second format, and the comparison result of each frame of the video to be tested is integrated to form the test report of the video to be tested, including:

[0109] Based on the first format, the frames of the video to be tested in an abnormal state and the difference information corresponding to each frame in the abnormal state are integrated to obtain a first integration result; based on the second format, the frames of the video to be tested in a normal state are integrated to obtain a second integration result.

[0110] The first format selection focuses on details and accuracy so that testers can clearly understand the specific situation of each abnormal frame. For example, a table format can be selected, where each row represents an abnormal frame, and the columns include information such as frame index, abnormal type, and difference description.

[0111] The second format can be more flexible and varied, depending on actual needs. If the main purpose of the test report is to show the overall video quality, you can choose a statistical chart to present the proportion or distribution of normal frames; if the report needs to include brief information about all frames, you can choose a concise list or summary to list the frame numbers or identification information of all normal frames.

[0112] See also Figure 2 , Figure 2 A test flow chart provided for the embodiment of the present application includes a cloud platform, task creation, test nodes, a cloud platform SDK (Software Development Kit), the main control part TA (Test Automation) of the AI ​​image detection software, a detection model part, and a test report. Figure 2 The test process provided in the embodiment of the present application is described.

[0113] The cloud platform first creates an AI image detection type task. This may include selecting the task type (such as image classification, target detection, etc.) and setting task parameters (such as detection targets, accuracy requirements, etc.). Test cases are selected based on project requirements, user scenarios, and test objectives. The test cases cover common image types, different lighting conditions, occlusion conditions, angle changes, etc. to ensure the robustness and accuracy of the model. Create a test node in the cloud platform, specify the node specifications (such as the number of CPUs, memory size, etc.) and the operating system environment. Configure the necessary software environment for the test node on the cloud platform side, including the operating system, dependent libraries, test frameworks, etc. Ensure that the test node on the cloud platform side can communicate with the bench test environment. In addition, a unique Node ID can be assigned to the test node to ensure the uniqueness and identifiability of the node in the cloud platform.

[0114] Bench configuration is a key step to ensure a stable and reliable test environment. According to the specifications of the test nodes on the cloud platform side, prepare the corresponding hardware equipment (such as servers, industrial computers, etc.) and the vehicle environment, and install the operating system and software environment that matches the cloud platform nodes on the hardware equipment to ensure the compatibility of the two at the hardware and software levels. Therefore, the test nodes on the bench side can be installed with necessary software and services, such as the main control part TA (TestAutomation) of the AI ​​image detection software, the detection model part, etc. Ensure that the network connection between the test node and the cloud platform is stable so that data can be received and sent. In addition, a unique Node ID can be configured for the test node on the bench side to ensure that it corresponds to the test node in the cloud platform.

[0115] After configuring the test nodes on the cloud platform, you need to verify the test nodes on the test bench with those on the cloud platform. Ensure that the test nodes on the cloud platform and the test bench can be accurately matched through the Node ID. Verify whether the vehicle environment (including hardware and software) of the test node is consistent with the requirements of the cloud platform to ensure that the test case can be correctly executed in the target environment.

[0116] In actual application scenarios, click the "Start Test" button on the cloud platform, the cloud platform will automatically create a test task and send the task to the test node on the cloud platform side. The test node on the cloud platform side, as a physical or virtual device that executes the test task, can continuously listen to instructions and data from the cloud platform. Once the cloud platform sends a new test task, the test node on the cloud platform side identifies and receives the task. The receiving process may include verifying the validity of the task, parsing task parameters, and preparing necessary resources. The test node on the cloud platform side sends the test case and test task to the bench-side test node corresponding to the test node on the cloud platform side through the cloud platform SDK, so that the bench-side test node performs the test according to the steps and expected results defined in the test case, and collects data and logs during the test process. After the test is executed, the test node on the bench side uploads the test report back to the cloud platform.

[0117] In order to interact effectively with the cloud platform, the test node on the test bench side can pre-install and configure the software development kit SDK provided by the cloud platform. SDK provides a set of predefined interfaces and functions that allow the test node on the test bench side to interact with the cloud platform, including receiving tasks, sending status reports, uploading test results, etc. By calling specific functions in the SDK, the test node on the test bench side can establish a stable connection with the cloud platform and request to receive task data information.

[0118] After the SDK successfully establishes a connection with the cloud platform and receives the task instruction, the SDK further triggers the startup of the main control part TA on the test node on the test bench side. For example, the main control part TA can be controlled through the COM interface. The main control part is the core component in the test process. It is responsible for controlling the execution of the entire test process, including data exchange with the cloud platform, configuration of the test environment, sending of test commands, collection of test results, etc.

[0119] Reinjecting FSD data means injecting pre-prepared test data (such as images, videos, etc.) into the test environment. The operation process on the human-machine interface HU screen is recorded by the detection software to generate the video Video_Test to be tested. This video is the video to be tested and will be used as input data for subsequent image detection tasks.

[0120] like Figure 3 As shown, Figure 3The schematic diagram of the image detection software structure provided for the embodiment of the present application, the AI ​​image detection software includes a detection model part and a main control part. Among them, the AI ​​image detection software can be used to mark the video for training, and train the detection model based on the marking results. Annotation refers to identifying and marking the test elements in the video, such as lane center keeping (LCC) signs, icons, buttons, etc. These annotated data are used to train the detection model so that it can recognize and understand the test elements and their features in the graphical user interface (Graphical User Interface, GUI).

[0121] The trained detection model can be used to annotate the detection elements in the standard video and the video to be detected. This step is to deploy the model to the actual test environment so that the detection model can automatically identify the test elements in the video. The attribute information of the GUI elements identified in each frame is saved as a JSON file. JSON (JavaScript Object Notation) is a lightweight data exchange format that is easy to read and write, and is also easy for machines to parse and generate. These JSON files contain detailed information about each frame of the video, and they are passed to the main control part TA. The main control part TA reads the json file and wakes up the comparison process, compares the two videos frame by frame based on the preset rules, and issues a test report.

[0122] Through the above steps, the test node can efficiently receive the test tasks issued by the cloud platform and start the AI ​​image detection software by calling the local SDK. The AI ​​image detection software uses the task data information to re-inject input data and record the human-machine interface operation process to generate the video to be tested. This process provides the necessary input data for subsequent image detection tasks and ensures the accuracy and reliability of the test.

[0123] The above are some specific implementations of the test method provided in the embodiment of the present application. Based on this, the present application also provides a corresponding system for testing. The system provided in the embodiment of the present application will be introduced from the perspective of functional modularization. Figure 4 A structural diagram of a test system provided in an embodiment of the present application.

[0124] The system comprises:

[0125] A first acquisition unit 110 is used to acquire a video to be tested and a standard video, wherein the standard video is a video containing known correct content and is used as a comparison reference, and the video to be tested is used to represent the display content of the environment information display module to be tested;

[0126] The labeling unit 111 is used to label the test features in the video to be tested and the standard video respectively based on the detection model, wherein the test features are information with visual characteristics in the video to be tested and the standard video that is identified and labeled by the detection model; the detection model is pre-trained and used to identify and label the test features in the video to be tested and the standard video;

[0127] The first comparison unit 112 is used to compare the test features in the video to be tested and the standard video frame by frame to obtain a test report of the video to be tested.

[0128] In a possible implementation, the first comparison unit includes:

[0129] A second comparison unit is used to compare the test features contained in the corresponding frames of the video to be tested and the standard video according to the video playback order to obtain a comparison result, wherein the corresponding frames are frames of the video to be tested and the standard video at the same time point;

[0130] The third comparison unit performs the step of comparing the test features contained in the corresponding frames of the video to be tested and the standard video in the video playback order to obtain the comparison result until all frames of the video to be tested and the standard video are compared;

[0131] The integration unit is used to integrate the comparison results of each frame of the video to be tested to form a test report of the video to be tested.

[0132] In a possible implementation manner, the second comparison unit is specifically used to:

[0133] Determine whether the number of test elements in the corresponding frames of the video to be tested and the standard video is the same; when there is a difference in the number of test elements in the corresponding frames of the test video and the standard video, the abnormal state of the current frame of the video to be tested and the quantity difference information are recorded in the comparison result.

[0134] In a possible implementation, the system further includes:

[0135] A first judgment unit, configured to judge whether there is a difference in attribute information of test elements in corresponding frames in the video to be tested and the standard video when the number of test elements in corresponding frames in the test video and the standard video is the same;

[0136] The first recording unit is used to record the abnormal state of the current frame of the video to be tested and the attribute difference information in the comparison result when there is a difference in the attribute information of the test element in the corresponding frame in the test video and the standard video.

[0137] In a possible implementation, the system further includes:

[0138] A second judgment unit is used to judge whether there is a difference in user interaction effects of the test elements in the corresponding frames in the test video and the standard video when the attribute information of the test elements in the corresponding frames in the test video and the standard video is the same;

[0139] The second recording unit is used to record information that the current frame of the video to be tested is in an abnormal state and the difference in user interaction effect into the comparison result when there is a difference in user interaction effect of the test element in the corresponding frames in the test video and the standard video.

[0140] In a possible implementation manner, the integration unit is specifically used to:

[0141] Based on the first format, the frames of the video to be tested in an abnormal state and the difference information corresponding to each frame in the abnormal state are integrated to obtain a first integration result; based on the second format, the frames of the video to be tested in a normal state are integrated to obtain a second integration result.

[0142] In a possible implementation, the system further includes:

[0143] A second acquisition unit is used for the test node to acquire a test task issued by the cloud platform, where the test task is created by the cloud platform and is used to instruct the test node to perform a test operation;

[0144] A starting unit, configured to start the image detection software including the detection model after the test node establishes a connection with the cloud platform through the software development kit and receives the task data information of the test task;

[0145] The recording unit is used for the test node to use the image detection software to re-inject task data information and record the content presented by the human-computer interface to obtain a video to be tested, and the video to be tested is the input data of the test task.

[0146] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the test method solutions provided in the embodiments of the present application.

[0147] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the test method described in any embodiment of the present application.

[0148] The computer storage medium stores codes, and when the codes are executed, a device executing the codes implements the test method described in any embodiment of the present application.

[0149] An embodiment of the present application further provides a computer program product. When the computer program product runs on a computer, the computer executes the testing method described in any embodiment of the present application.

[0150] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the system or device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0151] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0152] It should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0153] It should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0154] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0155] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0156] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A testing method, characterized in that: Applied to test nodes, including: Acquire a video to be tested and a standard video, wherein the standard video is a video containing known correct content and is used as a comparison benchmark, and the video to be tested is used to characterize the display content of the environment information display module to be tested; Based on the detection model, the test features in the video to be tested and the standard video are respectively annotated, wherein the test features are information with visual characteristics in the video to be tested and the standard video that is identified and annotated by the detection model; the detection model is pre-trained and used to identify and annotate the test features in the video to be tested and the standard video; The test features in the video to be tested and the standard video are compared frame by frame to obtain a test report of the video to be tested.

2. The method according to claim 1, characterized in that: The frame-by-frame comparison of the test features in the video to be tested and the standard video to obtain a test report of the video to be tested includes: According to the video playback order, the test features contained in the corresponding frames of the video to be tested and the standard video are compared to obtain a comparison result, wherein the corresponding frames are frames of the video to be tested and the standard video at the same time point; Executing the step of comparing the test features contained in the corresponding frames of the video to be tested and the standard video in the video playback order to obtain the comparison result until all frames of the video to be tested and the standard video are compared; The comparison results of each frame of the video to be tested are integrated to form a test report of the video to be tested.

3. The method according to claim 2, characterized in that The test feature includes a test element, which is a component with visual characteristics in the video to be tested and the standard video that is identified and marked by the detection model. The comparison result obtained by comparing the test features contained in the corresponding frames of the video to be tested and the standard video includes: Determine whether the number of test elements in the corresponding frames of the video to be tested and the standard video is the same; When there is a difference in the number of test elements in the corresponding frames of the test video and the standard video, the information that the current frame of the video to be tested is in an abnormal state and the number difference is recorded in the comparison result.

4. The method according to claim 3, characterized in that The test feature also includes attribute information of the test element, and the method further includes: When the number of test elements in the corresponding frames of the test video and the standard video is the same, determining whether there is a difference in attribute information of the test elements in the corresponding frames of the test video and the standard video; When there is a difference in the attribute information of the test element in the corresponding frames of the test video and the standard video, the abnormal state of the current frame of the video to be tested and the attribute difference information are recorded in the comparison result.

5. The method according to claim 4, characterized in that The test feature also includes a user interaction effect of the test element, and the method further includes: When the attribute information of the test element in the corresponding frame in the test video and the standard video is the same, determining whether there is a difference in the user interaction effect of the test element in the corresponding frame in the test video and the standard video; When there is a difference in the user interaction effects of the test elements in the corresponding frames in the test video and the standard video, the information that the current frame of the video to be tested is in an abnormal state and the difference in the user interaction effects is recorded in the comparison result.

6. The method according to any one of claims 2 to 5, characterized in that: The test report includes a first integration result presented in a first format and a second integration result presented in a second format, and the comparison result of each frame of the video to be tested is integrated to form the test report of the video to be tested, including: Based on the first format, the frames of the video to be tested in an abnormal state and the difference information corresponding to each frame in the abnormal state are integrated to obtain a first integration result; based on the second format, the frames of the video to be tested in a normal state are integrated to obtain a second integration result.

7. The method according to claim 1, characterized in that Before obtaining the video to be tested and the standard video, the method further includes: The test node obtains a test task issued by the cloud platform, where the test task is created by the cloud platform and is used to instruct the test node to perform a test operation; After the test node establishes a connection with the cloud platform through the software development kit and receives the task data information of the test task, the test node starts the image detection software including the detection model; The test node uses the image detection software to re-inject task data information and record the content presented by the human-computer interface to obtain a video to be tested, and the video to be tested is the input data of the test task.

8. A testing system, characterized in that: The system comprises: A first acquisition unit is used to acquire a video to be tested and a standard video, wherein the standard video is a video containing known correct content and used as a comparison reference, and the video to be tested is used to represent the display content of the environment information display module to be tested; A labeling unit, used for labeling the test features in the video to be tested and the standard video respectively based on the detection model, wherein the test features are information with visual characteristics in the video to be tested and the standard video that is identified and labeled by the detection model; the detection model is pre-trained and used for identifying and labeling the test features in the video to be tested and the standard video; The first comparison unit is used to compare the test features in the video to be tested and the standard video frame by frame to obtain a test report of the video to be tested.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the test method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that When the computer program product runs on a computer, the computer executes the testing method according to any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the testing method according to any one of claims 1 to 7.

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