A vehicle-mounted software intelligent testing method and related device

Abstract

This application discloses an intelligent testing method and related apparatus for in-vehicle software, relating to the field of artificial intelligence. It obtains target test case stimuli and the expected specific response results of the target test case stimuli; and obtains the test response results of the target test case stimuli through a vehicle model. In related technologies, a passing test result may indicate problems with the test-related response results. To address this, this application's embodiments obtain detection results through an intelligent monitoring model. These detection results characterize whether the test response results match the test case stimuli, i.e., the detection results include whether there are problems with the test-related response results. If the detection result is positive and the test-specific response result matches the expected specific response result, the software functional module is determined to have passed the test. The intelligent testing of in-vehicle software provided by this application's embodiments avoids the problems present in related technologies if the test result is passing, thus improving the accuracy of the test results.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to an intelligent testing method and related apparatus for vehicle software. Background Technology

[0002] With the rapid development of automotive technology, the number of software functional modules installed in vehicle controllers is increasing. During the development and iteration of these modules, testing is necessary. In related technologies, if the test results indicate that the software functional module fails, further improvements are made; if the test results indicate that the software functional module passes, it is then installed in the vehicle controller.

[0003] Even if a software module passes the testing methods in related technologies, the actual response result may still differ from the expected result during actual use, indicating that the testing methods in related technologies are inaccurate. Summary of the Invention

[0004] In view of the above problems, this application provides an intelligent testing method and related apparatus for vehicle software to improve the accuracy of test results for software functional modules. The specific solution is as follows:

[0005] The first aspect of this application provides an intelligent testing method for in-vehicle software, comprising:

[0006] Obtain the target test case stimulus and the expected specific response result of the target test case stimulus;

[0007] The test response results of the target test case stimulus are obtained through a vehicle model. The test response results include test-specific response results and test-related response results. The vehicle model includes the software functional modules to be tested. The test-related response results include parameters that affect the test-specific response results.

[0008] The test response result and the target test case stimulus are input into a pre-built intelligent monitoring model, and the detection result is obtained through the intelligent monitoring model; the detection result indicates whether the test response result and the test case stimulus match.

[0009] If the detection result is yes and the test-specific response result matches the expected specific response result, the software functional module is determined to have passed the test.

[0010] In one possible implementation, the step of obtaining the target test case stimulus and the expected specific response result of the target test case stimulus includes:

[0011] The target software function name is determined from a plurality of software function names, wherein the target software function name is the name of the software function module;

[0012] From the preset correspondence between the software function name, test case incentive, and expected specific response result, find the target test case incentive corresponding to the target software function name and the expected specific response result corresponding to the target test case incentive.

[0013] In one possible implementation, the step of obtaining the target test case stimulus and the expected specific response result of the target test case stimulus includes:

[0014] The target software function name is determined from a plurality of software function names, wherein the target software function name is the name of the software function module;

[0015] Send an acquisition instruction including the target software function name to the server; the target software function name is the basis for the server to find the target test case incentive corresponding to the target software function name and the expected specific response result corresponding to the target test case incentive from a preset correspondence between the software function name, test case incentive and expected specific response result;

[0016] Receive the target test case incentives corresponding to the target software function name and the expected specific response results corresponding to the target test case incentives from the server.

[0017] In one possible implementation, the method for obtaining the correspondence between the target software function name, the target test case stimulus, and the expected specific response result includes:

[0018] The system acquires input parameters and response parameters belonging to the same set time period; the input parameters include input signals generated by the user in response to vehicle operations, and the response parameters include response signals from the vehicle in response to the input signals.

[0019] From the preset correspondence between the software function name, the first field, and the second field, find the target first field and the target second field corresponding to the target software function name; the first field is the field included in the test case incentive, and the second field is the field included in the expected specific response result;

[0020] The value of the target first field included in the input parameters is determined to be the target test case incentive corresponding to the target software function name;

[0021] The value of the target second field included in the response parameter is determined to be the expected specific response result of the target test case stimulus;

[0022] Construct a correspondence between the target software function name, the target test case incentive, and the expected specific response result corresponding to the target test case incentive.

[0023] One possible implementation also includes:

[0024] From the preset correspondence between the software function name and the third field, find the target third field corresponding to the target software function name; the target third field is the field expected to be included in the associated response result.

[0025] The value of the target third field included in the response parameters is determined to be the expected associated response result of the target test case stimulus;

[0026] Construct a correspondence between the target software function name, the target test case incentive, and the expected response result of the target test case incentive. The expected response result includes the expected associated response result and the expected specific response result.

[0027] In one possible implementation, the method for training the intelligent monitoring model includes:

[0028] Obtain sample test case stimuli and sample response results of the sample test case stimuli; the sample response results include test-specific response results and test-related response results;

[0029] The sample test case stimulus and the sample response result are input into the intelligent monitoring model, and the intelligent monitoring model outputs a prediction result, which characterizes whether the sample response result matches the sample test case stimulus.

[0030] The intelligent monitoring model is trained with the goal of minimizing the difference between the predicted results and the labeled results corresponding to the sample test case stimuli.

[0031] In one possible implementation, the method for obtaining the sample test case stimulus and the sample response result of the sample test case stimulus includes:

[0032] The target software function name is determined from a plurality of software function names, wherein the target software function name is the name of the software function module;

[0033] From the preset correspondence between the software function name, test case incentives, and expected response results, find the sample test case incentives corresponding to the target software function name and the sample response results corresponding to the sample test case incentives.

[0034] A second aspect of this application provides an intelligent testing device for vehicle-mounted software, comprising:

[0035] The first acquisition module is used to acquire the target test case incentive and the expected specific response result of the target test case incentive;

[0036] The second acquisition module is used to obtain the test response results of the target test case stimulus through the vehicle model. The test response results include test-specific response results and test-related response results. The vehicle model includes the software functional modules to be tested. The test-related response results include parameters that affect the test-specific response results.

[0037] The third acquisition module is used to input the test response result and the target test case stimulus into a pre-built intelligent monitoring model, and acquire the detection result through the intelligent monitoring model; the detection result indicates whether the test response result matches the test case stimulus.

[0038] The first determining module is used to determine that the software functional module has passed the test if the detection result is yes and the test-specific response result matches the expected specific response result.

[0039] A third aspect of this application provides a computer program product, including computer-readable instructions, which, when executed on an electronic device, cause the electronic device to implement the vehicle software intelligent testing method described in the first aspect or any implementation thereof.

[0040] A fourth aspect of this application provides an electronic device, including at least one processor and a memory connected to the processor, wherein:

[0041] The memory is used to store computer programs;

[0042] The processor is used to execute the computer program so that the electronic device can implement the vehicle software intelligent testing method of the first aspect or any implementation thereof.

[0043] The fifth aspect of this application provides a computer storage medium carrying one or more computer programs, which, when executed by an electronic device, enable the electronic device to perform the vehicle software intelligent testing method described in the first aspect or any implementation thereof.

[0044] By employing the above technical solution, this application provides an intelligent testing method for in-vehicle software, which obtains the target test case stimulus and the expected specific response result of the target test case stimulus; and obtains the test response result of the target test case stimulus through a vehicle model. The test result includes two parts: a test-specific response result and a test-related response result. In related technologies, only the test-specific response result is considered to be present; as long as the test-specific response result meets the requirements, the test is considered passed, without considering the test-related response result. However, the test-related response result may affect the test-specific response result. Therefore, if the test-specific response result meets the requirements, but the test-related response result has problems, the inaccurate testing problem in related technologies will occur. To solve the above situation, the embodiments of this application obtain detection results through an intelligent monitoring model. This detection result characterizes whether the test response result matches the test case stimulus, that is, the detection result includes whether there are problems with the test-related response result. If the detection result is positive and the test-specific response result matches the expected specific response result, the software functional module is determined to have passed the test. The intelligent testing of in-vehicle software provided by the embodiments of this application, if the test result is passed, will not have the problems existing in related technologies, thus improving the accuracy of the test results. Attached Figure Description

[0045] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0046] Figure 1 A schematic diagram of a system architecture is provided for this application;

[0047] Figure 2 A schematic diagram of an optional hardware structure for a terminal 100 provided in this application;

[0048] Figure 3 This application provides a schematic diagram of the structure of a server 200;

[0049] Figure 4 A flowchart illustrating an intelligent testing method for vehicle software provided in an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the structure of an in-vehicle software intelligent testing device provided in an embodiment of this application;

[0051] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0052] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0053] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0054] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0055] Related technologies include testing methods for software functional modules, such as Software-in-the-Loop (SIL) testing, Hardware-in-the-Loop (HIL) testing, and vehicle testing. SIL testing involves inputting SIL test case stimuli into a virtual vehicle model, which then outputs response results. HIL testing uses a real vehicle controller; simple HIL testing requires simulating bus and hardware signals to interact with the controller, while complex HIL testing uses a test bench to connect the controller to the simulated vehicle. Vehicle testing refers to testing the software functional modules within the vehicle's controller in an actual vehicle.

[0056] A virtual vehicle model comprises multiple component models, which can interact through virtual interfaces. Different component models represent different parts of the vehicle, such as the engine control unit (ECU), sensors, and actuators. Component models can be derived from mathematical models.

[0057] In this application, both virtual vehicle models and real vehicles are collectively referred to as vehicle models.

[0058] Understandably, after inputting test case stimuli into the vehicle model, the vehicle model responds to the test case stimuli and obtains response results, which include multiple parameters. For some parameters in the response results, the test engineer can clearly understand their relationship with the test case stimuli; for example, a larger brake pedal depress signal (test case stimuli) results in a faster decrease in vehicle speed (a parameter in the response results). However, for other parameters in the response results, the test engineer may not be clear about their relationship with the test case stimuli; for example, a larger brake pedal depress signal (test case stimuli) may not determine whether a larger motor speed (a parameter in the response results) increases or decreases. Based on this, this application divides the response results into: specific response results and associated response results; where the test engineer can clearly understand the relationship between specific response results and test case stimuli; however, the test engineer may not be clear about the relationship between associated response results and test case stimuli.

[0059] The testing method for software functional modules in related technologies is as follows: test case stimuli are input into the vehicle model, and test response results are obtained through the vehicle model; if the test-specific response result contained in the test response result is the same as the expected specific response result, the test result is passed; otherwise, the test result is failed.

[0060] This application refers to the response results obtained through the vehicle model as test response results; the specific response results contained in the test response results as test-specific response results; and the related response results contained in the test response results as test-related response results.

[0061] This application refers to the actual response result of the test case stimulus as the expected response result, the specific response result contained in the expected response result as the expected specific response result, and the associated response result contained in the expected response result as the expected associated response result.

[0062] The test result in the relevant technology is "passed", which may be due to the following two situations.

[0063] Scenario 1: There is a problem with the test-related response results included in the test response results; the test-specific response results included in the test response results are the same as the expected specific response results.

[0064] Scenario 2: There is no problem with the test-related response results included in the test response results, and the test-specific response results included in the test response results are the same as the expected specific response results.

[0065] If it is scenario one, the test results in the relevant technology are inaccurate. If it is scenario two, the test results in the relevant technology are accurate.

[0066] Based on this, this application provides an intelligent testing method for in-vehicle software to solve the problem caused by situation one.

[0067] See Figure 1 , Figure 1 A schematic diagram of a system architecture is shown. The system may include a terminal 100 and a server 200. The server 200 may include one or more servers (…). Figure 1 (The example includes a server), and the server 200 can provide the method provided in the embodiments of this application to one or more terminals.

[0068] The terminal 100 may have an application or webpage installed on it. The application or webpage can provide an interface. The terminal 100 can receive relevant parameters input by the user on the interface, such as the name of the software function to be tested, and send the parameters to the server 200. The server 200 can obtain the processing result based on the received parameters and return the processing result to the terminal 100.

[0069] It should be understood that in some optional implementations, the terminal 100 can also complete the action of obtaining the processing result based on the received parameters on its own, without the need for the server to cooperate. This application embodiment is not limited to this.

[0070] The following description Figure 1 The product form of the mid-terminal 100;

[0071] The terminal 100 in this application embodiment can be a mobile phone, tablet computer, wearable device, vehicle device, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), etc., and this application embodiment does not impose any restrictions on it.

[0072] Figure 2 A schematic diagram of an optional hardware structure for terminal 100 is shown.

[0073] refer to Figure 2 As shown, the terminal 100 may include a radio frequency unit 110, a memory 120, an input unit 130, a display unit 140, a camera 150 (optional), an audio circuit 160 (optional), a speaker 161 (optional), a microphone 162 (optional), a headphone jack 163 (optional), a processor 170, an external interface 180, a power supply 190, and other components. Those skilled in the art will understand that... Figure 2This is merely an example of a terminal and does not constitute a limitation on the terminal. It may include more or fewer components than shown in the illustration, or combine certain components, or use different components.

[0074] The input unit 130 can be used to receive input numerical or character information, and to generate key signal inputs related to user settings and function control of the terminal. Specifically, the input unit 130 may include a touch screen 131 (optional) and / or other input devices 132. The touch screen 131 can collect touch operations performed by the user on or near it (such as operations performed by the user using fingers, knuckles, styluses, or any suitable object on or near the touch screen), and drive the corresponding connection devices according to a pre-set program. The touch screen can detect the user's touch actions, convert the touch actions into touch signals and send them to the processor 170, and can receive and execute commands sent by the processor 170; the touch signal includes at least touch point coordinate information. The touch screen 131 can provide an input interface and an output interface between the terminal 100 and the user. In addition, various types of touch screens, such as resistive capacitive, infrared, and surface acoustic wave, can be used to implement the touch screen. In addition to the touch screen 131, the input unit 130 may also include other input devices. Specifically, other input devices 132 may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0075] Among them, the input device 132 can receive input data, etc.

[0076] The display unit 140 can be used to display information input by the user or information provided to the user, various menus of the terminal 100, interactive interfaces, file display, and / or playback of any multimedia file. In this embodiment, the display unit 140 can be used to display interfaces, processing results, etc.

[0077] The memory 120 can be used to store instructions and data. The memory 120 may primarily include an instruction storage area and a data storage area. The data storage area can store various types of data, such as multimedia files and text. The instruction storage area can store software units such as operating systems, applications, and instructions required for at least one function, or subsets or extended sets thereof. It may also include non-volatile random access memory. It provides the processor 170 with hardware, software, and data resources for managing the computing device, supporting control software and applications. It is also used for storing multimedia files, as well as storing running programs and applications.

[0078] The processor 170 is the control center of the terminal 100. It connects various parts of the terminal 100 via various interfaces and lines. By running or executing instructions stored in the memory 120 and calling data stored in the memory 120, it performs various functions and processes data of the terminal 100, thereby controlling the terminal device as a whole. Optionally, the processor 170 may include one or more processing units; preferably, the processor 170 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 170. In some embodiments, the processor and memory can be implemented on a single chip; in some embodiments, they can also be implemented separately on independent chips. The processor 170 can also be used to generate corresponding operation control signals, send them to the corresponding components of the computing processing device, read and process data in the software, especially read and process data and programs in the memory 120, so that the various functional modules therein perform corresponding functions, thereby controlling the corresponding components to act according to the instructions.

[0079] The memory 120 can be used to store software code related to the vehicle software intelligent testing method, and the processor 170 can execute the steps of the vehicle software intelligent testing method, and can also schedule other units (such as the above-mentioned input unit 130 and display unit 140) to achieve the corresponding functions.

[0080] The radio frequency unit 110 (optional) can be used for receiving and transmitting signals during information transmission or calls. For example, it can receive downlink information from the base station and process it for the processor 170; additionally, it can transmit uplink data to the base station. Typically, the RF circuit includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier (LNA), a duplexer, etc. Furthermore, the radio frequency unit 110 can also communicate wirelessly with network devices and other devices. This wireless communication can use any communication standard or protocol, including but not limited to Global System for Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0081] In this embodiment of the application, the radio frequency unit 110 can send data to the server 200 and receive the processing results sent by the server 200.

[0082] It should be understood that the radio frequency unit 110 is optional and can be replaced with other communication interfaces, such as a network port.

[0083] The terminal 100 also includes a power supply 190 (such as a battery) that supplies power to various components. Preferably, the power supply can be logically connected to the processor 170 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.

[0084] Terminal 100 also includes an external interface 180, which can be a standard Micro USB interface or a multi-pin connector, which can be used to connect terminal 100 to other devices for communication or to connect a charger to charge terminal 100.

[0085] Although not shown, terminal 100 may also include a flash, a wireless fidelity (WiFi) module, a Bluetooth module, sensors with various functions, etc., which will not be described in detail here. Some or all of the methods described below can be applied to, for example... Figure 2 In the terminal 100 shown.

[0086] The following description Figure 1 The product form of the mid-range server 200;

[0087] Figure 3 A structural diagram of a server 200 is provided, as follows: Figure 3 As shown, server 200 includes bus 201, processor 202, communication interface 203, and memory 204. Processor 202, memory 204, and communication interface 203 communicate with each other via bus 201.

[0088] Bus 201 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0089] The processor 202 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0090] Memory 204 may include volatile memory, such as random access memory (RAM). Memory 204 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0091] The memory 204 can be used to store software code related to the intelligent testing method for vehicle software, and the processor 202 can execute the steps of the intelligent testing method for vehicle software of the chip, and can also schedule other units to achieve corresponding functions.

[0092] It should be understood that the aforementioned terminal 100 and server 200 can be centralized or distributed devices. The processors (e.g., processor 170 and processor 202) in the aforementioned terminal 100 and server 200 can be hardware circuits (such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), general-purpose processors, digital signal processors (DSPs), microprocessors or microcontrollers, etc.) or combinations of these hardware circuits. For example, the processor can be a hardware system with instruction execution capabilities, such as a CPU or DSP, or a hardware system without instruction execution capabilities, such as an ASIC or FPGA, or a combination of the aforementioned hardware systems without instruction execution capabilities and hardware systems with instruction execution capabilities.

[0093] Reference Figure 4 , Figure 4 This is a flowchart illustrating an intelligent testing method for in-vehicle software provided in an embodiment of this application, as shown below. Figure 4 As shown in the embodiment of this application, an intelligent testing method for vehicle software may include steps S401 to S404, which are described in detail below.

[0094] Step S401: Obtain the target test case stimulus and the expected specific response result of the target test case stimulus.

[0095] A test case stimulus is a set of input data, operation sequences, or conditions designed during software testing to verify a specific function or behavior of a software module. These input data, operation sequences, or conditions can trigger a specific response from the software module.

[0096] This application divides response results into: specific response results and related response results; among them, test engineers can clearly understand the relationship between specific response results and test case incentives; test engineers may not be able to clearly understand the relationship between related response results and test case incentives.

[0097] Assume the response results include 50 parameters, a specific response result includes 3 parameters, and a related response result includes 47 parameters. Related response results may affect the accuracy of the specific response results.

[0098] For example, the target test case stimulus and the expected specific response result of the target test case stimulus may be pre-set.

[0099] Step S402: Obtain the test response results of the target test case stimulus through the vehicle model.

[0100] The test response results include test-specific response results and test-related response results; the vehicle model includes the software functional modules to be tested.

[0101] For example, a vehicle model is a system that simulates the behavior and responses of a real vehicle, used to test and validate software functional modules in a controlled environment. The primary function of the vehicle model is to simulate the vehicle's response to specific inputs (target test case stimuli) to test the performance of the software functional modules without actually running the vehicle. If the vehicle model includes a real controller, the software functional module under test is installed within the controller. If the vehicle model is a virtual vehicle model, the software functional module under test is set up within a virtual controller component model of the virtual vehicle model.

[0102] For example, the target test case stimulus and its corresponding test response result can be stored in a database.

[0103] For example, the test-related response result includes parameters that affect the test-specific response result. If the test-specific response result includes 3 parameters, the test-related response result includes 47 parameters, and these 47 parameters may affect the 3 parameters.

[0104] Step S403: Input the test response result and the target test case stimulus into the pre-built intelligent monitoring model, and obtain the detection result through the intelligent monitoring model.

[0105] The detection result indicates whether the test response result matches the test case stimulus.

[0106] The training process of the intelligent monitoring model is described below. The method for training the intelligent monitoring model includes the following steps A1 to A3.

[0107] Step A1: Obtain the sample test case stimulus and the sample response results of the sample test case stimulus; the sample response results include test-specific response results and test-related response results.

[0108] There are multiple ways to implement step A1, and the embodiments of this application provide, but are not limited to, the following two.

[0109] The first method of implementing step A1 includes the following steps A11 to A12.

[0110] Step A11: Determine the target software function name from multiple software function names, where the target software function name is the name of the software function module.

[0111] Step A12: From the preset correspondence between the software function name, test case incentives and expected response results, find the sample test case incentives corresponding to the target software function name and the sample response results corresponding to the sample test case incentives.

[0112] It is understandable that the sample test case stimuli and sample response results obtained through step A12 must match. For example, after obtaining the sample test case stimuli and sample response results through step A12, test developers can modify them to obtain mismatched sample test cases and sample response results.

[0113] In the first step A1 implementation, the "correspondence between software function names, test case stimuli, and expected response results" can be stored on the device running the in-vehicle software intelligent testing method or in a database communicating with that device. This eliminates the need to retrieve it remotely from a server, saving network bandwidth resources.

[0114] The second method of implementing step A1 includes the following steps A21 to A23.

[0115] Step A21: Determine the target software function name from multiple software function names, where the target software function name is the name of the software function module.

[0116] Step A22: Send a sample acquisition instruction including the target software function name to the server; the target software function name is the basis for the server to find the sample test case incentive corresponding to the target software function name and the sample response result corresponding to the sample test case incentive from the preset correspondence between the software function name, test case incentive and expected response result;

[0117] Step A23: Receive the sample test case incentives and the corresponding sample response results from the server.

[0118] It is understood that the sample test case stimuli and sample response results obtained through step A23 must match. For example, after obtaining the sample test case stimuli and sample response results through step A23, test developers can modify them to obtain mismatched sample test cases and sample response results.

[0119] In the second implementation of step A1, the "correspondence between software function names, test case stimuli, and expected response results" can be stored on the server. This saves storage space on the equipment running the intelligent testing method for in-vehicle software.

[0120] Step A2: Input the sample test case stimulus and the sample response result into the intelligent monitoring model, and output the prediction result through the intelligent monitoring model. The prediction result represents whether the sample response result matches the sample test case stimulus.

[0121] Step A3: Train the intelligent monitoring model with the goal of minimizing the difference between the predicted result and the labeled result corresponding to the sample test case stimulus.

[0122] Understandably, the trained intelligent monitoring model has learned the correlation between sample test case incentives and sample response results, that is, it has learned the correlation between test case incentives and specific response results, as well as the correlation between test case incentives and associated response results.

[0123] For example, the detection results include: whether the test specific response result matches the test case stimulus, and whether the test associated response result matches the test case stimulus.

[0124] The following example illustrates the matching principle.

[0125] If the test case stimulus is pressing the brake pedal and the specific test response result is a decrease in vehicle speed, then the two match; if the test case stimulus is pressing the brake pedal and the specific test response result is an increase in vehicle speed, then the two do not match.

[0126] For example, the intelligent monitoring model can obtain predicted specific response results, predicted test case incentives, and predicted associated response results; then the detection results include: whether the predicted specific response result matches the test specific response result, whether the predicted test case incentive matches the test case incentive, and whether the test associated response result matches the predicted associated response result.

[0127] For example, the training of an intelligent monitoring model involves at least one of the following machine learning techniques: artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and instructional learning.

[0128] For example, the intelligent monitoring model can be any of the following models: neural network model, logistic regression model, linear regression model, support vector machine (SVM), Adaboost, XGboost, Transformer-Encoder model, or large language model.

[0129] For example, the neural network model can be any of the following: a model based on a recurrent neural network, a model based on a convolutional neural network, or a classification model based on a Transformer-encoder.

[0130] For example, the intelligent monitoring model can be a deep hybrid model of a recurrent neural network-based model, a convolutional neural network-based model, and a classification model based on a Transformer-encoder.

[0131] For example, the intelligent monitoring model can be any of the following: attention-based deep model, memory network-based deep model, or deep learning-based short text classification model.

[0132] Deep learning-based short text classification models are recurrent neural networks (RNNs) or convolutional neural networks (CNNs), or variations of RNNs or CNNs.

[0133] For example, some simple domain-adaptive modifications can be made to a pre-trained model to obtain an intelligent monitoring model.

[0134] For example, “simple domain-adaptive modification” includes, but is not limited to, retraining a pre-trained model using a large-scale unsupervised domain corpus, and / or compressing a pre-trained model by model distillation.

[0135] Steps A1 to A3 constitute supervised learning. For example, semi-supervised or unsupervised learning can also be applied to the intelligent monitoring model. Semi-supervised learning is a learning method that combines supervised and unsupervised learning. Semi-supervised learning uses a large amount of unlabeled data, as well as labeled data simultaneously, to perform pattern recognition.

[0136] Step S404: If the detection result is yes and the test-specific response result matches the expected specific response result, determine that the software functional module has passed the test.

[0137] It is understandable that if the test result in the related technology is "pass," there may be a situation as mentioned above in the related technology: the test response result contains a problem with the test-related response result, or the test-specific response result contained in the test response result is the same as the expected specific response result. If it is situation one, then the test result in the related technology is inaccurate. The embodiments of this application can determine whether there is a problem with the test-related response result contained in the test response result through an intelligent monitoring model, thereby solving the problem caused by situation one.

[0138] For example, if the test result is negative or the test result for a specific response does not match the expected result for a specific response, it is determined that the software functional module has failed the test.

[0139] For example, if a software functional module fails a test, an alert can be issued. A warning label can be added to the target test case stimulus that failed the test, and the test steps can be recorded in the test report. Test engineers can then use the warnings, relevant test case stimuli, test steps, and test response results in the test report to troubleshoot bugs.

[0140] This application provides an intelligent testing method for in-vehicle software, which obtains target test case stimuli and expected specific response results of the target test case stimuli; and obtains the test response results of the target test case stimuli through a vehicle model. The test results include two parts: a test-specific response result and a test-related response result. In related technologies, only the test-specific response result is considered to be present; if the test-specific response result meets the requirements, the test is considered passed, without considering the test-related response result. However, the test-related response result may affect the test-specific response result. Therefore, if the test-specific response result meets the requirements, but the test-related response result has problems, the inaccurate testing problem in related technologies will occur. To solve the above situation, the embodiments of this application obtain detection results through an intelligent monitoring model. This detection result characterizes whether the test response result matches the test case stimuli, that is, the detection result includes whether there are problems with the test-related response result. If the detection result is positive and the test-specific response result matches the expected specific response result, the software functional module is determined to have passed the test. The intelligent testing of in-vehicle software provided by the embodiments of this application, if the test result is passed, will not have the problems existing in related technologies, thus improving the accuracy of the test results.

[0141] It is understood that there are multiple ways to implement step S401, and the embodiments of this application provide, but are not limited to, the following two.

[0142] The first method of implementing step S401 includes the following steps B11 to B12.

[0143] Step B11: Determine the target software function name from multiple software function names, where the target software function name is the name of the software function module.

[0144] For example, terminal 100 may display a user interface that shows multiple software function names so that test engineers can select one or more target software function names.

[0145] Step B12: From the preset correspondence between the software function name, test case incentive, and expected specific response result, find the target test case incentive corresponding to the target software function name and the expected specific response result corresponding to the target test case incentive.

[0146] For example, the correspondence between software function names, test case stimuli, and expected specific response results can be stored in the terminal or on the server.

[0147] The second method for implementing step S401 includes the following steps B21 to B23.

[0148] Step B21: Determine the target software function name from multiple software function names, where the target software function name is the name of the software function module.

[0149] Step B22: Send an acquisition instruction including the target software function name to the server; the target software function name is the basis for the server to find the target test case incentive corresponding to the target software function name and the expected specific response result corresponding to the target test case incentive from the preset correspondence between the software function name, test case incentive and expected specific response result.

[0150] Step B23: Receive the target test case incentives corresponding to the target software function name and the expected specific response results corresponding to the target test case incentives from the server.

[0151] The following describes a method for obtaining the correspondence between the target software function name, the target test case stimulus, and the expected specific response result. This method may include the following steps C1 to C5.

[0152] Step C1: Obtain input parameters and response parameters belonging to the same set time period; the input parameters include input signals generated by the user in response to vehicle operations, and the response parameters include response signals of the vehicle in response to the input signals.

[0153] It is understood that the test case incentives provided in the embodiments of this application include manually written test case incentives and test case incentives obtained from the cloud server of the actual vehicle.

[0154] Understandably, with the continuous development of vehicle technology, vehicles are now connected to the internet, and during normal driving, they generate a large number of input and response parameters, which can be stored in the vehicle's cloud server.

[0155] In the field of software engineering, testing software functional modules is a crucial step in ensuring software quality. Related testing methods rely on manually written test case stimuli, which simulate user operations to verify the correctness of the software functional modules. However, this method suffers from inefficiency and difficulty in covering all possible scenarios. An example is provided below.

[0156] Understandably, the normal process of starting a vehicle is: press the brake, shift gears, release the brake, and press the accelerator. Therefore, the test cases written by the test engineer would include these four actions in sequence: pressing the brake, shifting gears, releasing the brake, and pressing the accelerator. However, in actual vehicle startup, users might start the vehicle using the following sequence: shift gears, press the brake, release the brake, and press the accelerator. Test engineers may not consider certain actual user actions, such as user errors. Therefore, manually written test cases cannot cover all possible scenarios.

[0157] The test case incentives obtained from the cloud server of the actual vehicle are generated during the user's driving process. Therefore, the test case incentives fully cover the actual operating scenarios of the vehicle and do not deviate from the actual scenarios. Thus, compared with related technologies, the test results obtained by this application have higher accuracy.

[0158] Understandably, during actual vehicle operation, the user is constantly operating the vehicle. This continuous operation generates corresponding input signals, which the software modules then respond to. For example, during actual vehicle operation, the user's sequence of operations is as follows: operating the brake pedal (generating a braking signal), and operating the accelerator pedal (generating an acceleration signal). The vehicle continuously responds to these input signals, receiving signals such as: a speed reduction signal (responding to the braking signal) and a speed increase signal (responding to the acceleration signal).

[0159] It is understandable that the time interval between the input parameter and its corresponding response parameter is very small, such as 0.01 seconds, so the input parameter and its corresponding response parameter can be considered to belong to the same set time period. In this application, a correspondence is established between input parameters and response parameters that belong to the same set time period.

[0160] For example, the time period can be set based on the actual situation, and there is no limitation here.

[0161] Step C2: From the preset correspondence between the software function name, the first field, and the second field, find the target first field and the target second field corresponding to the target software function name; the first field is the field included in the test case incentive, and the second field is the field included in the expected specific response result.

[0162] For example, the target first field can be one or more.

[0163] For example, the target second field can be one or more.

[0164] It is understandable that different software function names correspond to different software function modules. The first field corresponding to different software function names may be different or the same; the second field corresponding to different software function names may be different or the same.

[0165] Step C3: Determine that the field value of the first target field included in the input parameters is the target test case stimulus corresponding to the target software function name.

[0166] Step C4: Determine that the field value of the target second field included in the response parameter is the expected specific response result of the target test case stimulus.

[0167] Step C5: Construct the correspondence between the target software function name, the target test case incentive, and the expected specific response result corresponding to the target test case incentive.

[0168] It is understandable that after constructing the correspondence between the target software function name, the target test case stimulus, and the expected specific response result according to the above steps C1 to C5, the target test case stimulus and the expected specific response result can be obtained from the correspondence when executing step S401.

[0169] For example, the above method may also include steps C6 to C8.

[0170] Step C6: From the preset correspondence between the software function name and the third field, find the target third field corresponding to the target software function name; the target third field is the field included in the expected associated response result.

[0171] For example, the number of target third fields can be one or more.

[0172] Step C7: Determine the field value of the target third field included in the response parameter as the expected associated response result of the target test case stimulus.

[0173] Step C8: Construct the correspondence between the target software function name, the target test case incentive, and the expected response result of the target test case incentive. The expected response result includes the expected associated response result and the expected characteristic response result.

[0174] It is understandable that the sample test case incentives and sample response results for training the intelligent monitoring model can be obtained from the correspondence constructed in step C8.

[0175] The intelligent monitoring model in this application learns from a large number of test cases generated during actual vehicle driving, continuously accumulating experience to compensate for the lack of professional skills among test engineers in multiple aspects and enrich test scenarios. For example, the intelligent monitoring model learns the characteristics of different driving behaviors by analyzing a large amount of vehicle driving data, including speed, acceleration, steering angle, and braking force. This data comes from actual driving tests under various road conditions and weather conditions, enabling the model to identify and predict driving behaviors under various complex situations.

[0176] For example, intelligent monitoring models can be applied to SIL (Safety Integrity Level) testing, without adding extra work to SIL testing in related technologies, to discover software bug risks in requirements or more scenarios.

[0177] It is understood that if this application is used in SIL testing, a SIL testing environment needs to be built. The process of building a SIL testing environment is described below. The process of building a SIL testing environment includes the following steps D1 to D3.

[0178] Step D1: Develop the relevant software code mentioned in the embodiments of this application based on the new requirements or functional changes.

[0179] For example, software code includes, but is not limited to: code for software functional modules, code for test case stimuli, and code for expected response results.

[0180] Step D2: Generate VECU from the software code in Step D1 using SIL-related tools.

[0181] In SIL testing, the test case stimulus is ECU (Electronic Control Unit) code. The software engineering that can run in the SIL test environment, compiled from the software code of the actual ECU, is called VECU. For example, in this application, VECU (Vehicle Electronic Control Unit) refers to the vehicle controller (VCU).

[0182] A VECU (Virtual ECU) is software that is compiled and runs independently on a PC, independent of actual hardware. It typically consists of several parts: ASW (Application Software), vBSW (Virtual Base Software), vCDD (Virtual Communication Driver), and RTE (Runtime Environment).

[0183] For example, a SIL-related tool could be the VECU-BUILDER tool, which generates a VECU from the software code in step D1. VECU-BUILDER is a tool for generating virtual ECUs; it constructs a virtual ECU from C code source files or pre-compiled binary library files.

[0184] Step D3: Based on the VECU generated in step D2, build the SIL test environment.

[0185] For example, modeling programming tools (such as Matlab / Simulink) can be used to build models of each component in the vehicle controller to obtain the SIL test environment.

[0186] For example, test case stimuli and test response results obtained through the vehicle model can be stored in the SIL test execution database in real time.

[0187] For example, the intelligent monitoring model can obtain the target test case stimulus and test response results from the SIL test execution database to perform the operation of step S403, and store the obtained detection results in the SIL test execution database.

[0188] The above describes an intelligent testing method for vehicle software provided by embodiments of this application. The following describes the apparatus for performing the above-described intelligent testing method for vehicle software.

[0189] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an in-vehicle software intelligent testing device provided in an embodiment of this application. Figure 5 As shown, the vehicle-mounted software intelligent testing device includes:

[0190] The first acquisition module 501 is used to acquire the target test case incentive and the expected specific response result of the target test case incentive;

[0191] The second acquisition module 502 is used to obtain the test response results of the target test case stimulus through the vehicle model. The test response results include test-specific response results and test-related response results. The vehicle model includes the software functional modules to be tested. The test-related response results include parameters that affect the test-specific response results.

[0192] The third acquisition module 503 is used to input the test response result and the target test case stimulus into a pre-built intelligent monitoring model, and acquire the detection result through the intelligent monitoring model; the detection result characterizes whether the test response result matches the test case stimulus;

[0193] The first determining module 504 is used to determine that the software function module has passed the test if the detection result is yes and the test-specific response result matches the expected specific response result.

[0194] In one optional implementation, the first acquisition module includes:

[0195] The first determining unit is configured to determine a target software function name from a plurality of software function names, wherein the target software function name is the name of the software function module;

[0196] The first search unit is used to search for the target test case incentive corresponding to the target software function name and the expected specific response result corresponding to the target test case incentive from a preset correspondence between the software function name, test case incentive and expected specific response result.

[0197] In one optional implementation, the first acquisition module includes:

[0198] The second determining unit is configured to determine a target software function name from a plurality of software function names, wherein the target software function name is the name of the software function module.

[0199] The first sending unit is used to send an acquisition instruction including the target software function name to the server; the target software function name is the basis for the server to find the target test case incentive corresponding to the target software function name and the expected specific response result corresponding to the target test case incentive from a preset correspondence between the software function name, test case incentive and expected specific response result;

[0200] The first receiving unit is used to receive the target test case incentives corresponding to the target software function name and the expected specific response results corresponding to the target test case incentives from the server.

[0201] In one alternative implementation, it also includes:

[0202] The fourth acquisition module is used to acquire input parameters and response parameters belonging to the same set time period; the input parameters include input signals generated by the user in response to vehicle operations, and the response parameters include response signals of the vehicle in response to the input signals;

[0203] The first search module is used to search for the target first field and target second field corresponding to the target software function name from the preset correspondence between the software function name, the first field and the second field; the first field is the field contained in the test case incentive, and the second field is the field contained in the expected specific response result;

[0204] The second determining module is used to determine that the field value of the target first field included in the input parameters is the target test case incentive corresponding to the target software function name;

[0205] The third determining module is used to determine that the field value of the target second field included in the response parameter is the expected specific response result of the target test case stimulus;

[0206] The first construction module is used to construct the correspondence between the target software function name, the target test case incentive, and the expected specific response result corresponding to the target test case incentive.

[0207] In one alternative implementation, it also includes:

[0208] The second search module is used to search for the target third field corresponding to the target software function name from the preset correspondence between the software function name and the third field; the target third field is the field included in the expected associated response result;

[0209] The fourth determining module is used to determine the expected associated response result of the target test case stimulus by determining the field value of the target third field included in the response parameter;

[0210] The second construction module is used to construct the correspondence between the target software function name, the target test case incentive, and the expected response result of the target test case incentive. The expected response result includes the expected associated response result and the expected specific response result.

[0211] In one alternative implementation, it also includes:

[0212] The fifth acquisition module is used to acquire sample test case stimuli and sample response results of the sample test case stimuli; the sample response results include test-specific response results and test-related response results;

[0213] The sixth acquisition module is used to input the sample test case stimulus and the sample response result into the intelligent monitoring model, and output the prediction result through the intelligent monitoring model. The prediction result characterizes whether the sample response result matches the sample test case stimulus.

[0214] The training module is used to train the intelligent monitoring model with the goal of minimizing the difference between the predicted results and the labeled results corresponding to the sample test case stimuli.

[0215] In one optional implementation, the fifth acquisition module includes:

[0216] The fifth determining module is used to determine the target software function name from multiple software function names, wherein the target software function name is the name of the software function module;

[0217] The third search module is used to search for sample test case incentives corresponding to the target software function name and sample response results corresponding to the sample test case incentives from the preset correspondence between the software function name, test case incentives and expected response results.

[0218] This application also provides an electronic device in its embodiments. (See reference...) Figure 6 The diagram illustrates a structural schematic suitable for implementing the electronic device in the embodiments of this application. The electronic device in the embodiments of this application may include, but is not limited to, fixed terminals such as mobile phones, laptops, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0219] like Figure 6 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. When the electronic device is powered on, the RAM 603 also stores various programs and data required for the operation of the electronic device. The processing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0220] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, memory cards, hard drives, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0221] This application also provides a computer program product including computer-readable instructions, which, when executed on an electronic device, cause the electronic device to implement any of the vehicle software intelligent testing methods provided in this application.

[0222] This application also provides a computer-readable storage medium that carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any of the vehicle software intelligent testing methods provided in this application.

[0223] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0224] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0225] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0226] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

Claims

1. A method for intelligent testing of vehicle-mounted software, characterized in that, include: Obtain the target test case stimulus and the expected specific response result of the target test case stimulus; The test response results of the target test case stimulus are obtained through a vehicle model. The test response results include test-specific response results and test-related response results. The vehicle model includes the software functional modules to be tested. The test-related response results include parameters that affect the specific response results of the test; The test response results and the target test case stimuli are input into a pre-built intelligent monitoring model, and the detection results are obtained through the intelligent monitoring model. The detection result indicates whether the test response result matches the test case stimulus; If the detection result is yes and the test-specific response result matches the expected specific response result, the software functional module is determined to have passed the test.

2. The intelligent testing method for vehicle software according to claim 1, characterized in that, The steps of obtaining the target test case stimulus and the expected specific response result of the target test case stimulus include: The target software function name is determined from a plurality of software function names, wherein the target software function name is the name of the software function module; From the preset correspondence between the software function name, test case incentive, and expected specific response result, find the target test case incentive corresponding to the target software function name and the expected specific response result corresponding to the target test case incentive.

3. The intelligent testing method for vehicle-mounted software according to claim 1, characterized in that, The steps of obtaining the target test case stimulus and the expected specific response result of the target test case stimulus include: The target software function name is determined from a plurality of software function names, wherein the target software function name is the name of the software function module; Send an acquisition instruction including the target software function name to the server; the target software function name is the basis for the server to find the target test case incentive corresponding to the target software function name and the expected specific response result corresponding to the target test case incentive from a preset correspondence between the software function name, test case incentive and expected specific response result; Receive the target test case incentives corresponding to the target software function name and the expected specific response results corresponding to the target test case incentives from the server.

4. The intelligent testing method for vehicle-mounted software according to any one of claims 2 or 3, characterized in that, The method for obtaining the correspondence between the target software function name, the target test case stimulus, and the expected specific response result includes: The system acquires input parameters and response parameters belonging to the same set time period; the input parameters include input signals generated by the user in response to vehicle operations, and the response parameters include response signals from the vehicle in response to the input signals. From the preset correspondence between the software function name, the first field, and the second field, find the target first field and the target second field corresponding to the target software function name; the first field is the field included in the test case incentive, and the second field is the field included in the expected specific response result; The value of the target first field included in the input parameters is determined to be the target test case incentive corresponding to the target software function name; The value of the target second field included in the response parameter is determined to be the expected specific response result of the target test case stimulus; Construct a correspondence between the target software function name, the target test case incentive, and the expected specific response result corresponding to the target test case incentive.

5. The intelligent testing method for vehicle-mounted software according to claim 4, characterized in that, After the step of determining that the field value of the target second field included in the response parameter is the expected specific response result of the target test case stimulus, the method further includes: From the preset correspondence between the software function name and the third field, find the target third field corresponding to the target software function name; the target third field is the field expected to be included in the associated response result. The value of the target third field included in the response parameters is determined to be the expected associated response result of the target test case stimulus; Construct a correspondence between the target software function name, the target test case incentive, and the expected response result of the target test case incentive. The expected response result includes the expected associated response result and the expected specific response result.

6. The intelligent testing method for vehicle-mounted software according to claim 1, characterized in that, The method for training the intelligent monitoring model includes: Obtain sample test case stimuli and sample response results of the sample test case stimuli; the sample response results include test-specific response results and test-related response results, and the sample test case stimuli are generated during vehicle driving; The sample test case stimulus and the sample response result are input into the intelligent monitoring model, and the intelligent monitoring model outputs a prediction result, which characterizes whether the sample response result matches the sample test case stimulus. The intelligent monitoring model is trained with the goal of minimizing the difference between the predicted results and the labeled results corresponding to the sample test case stimuli.

7. The intelligent testing method for vehicle software according to claim 6, characterized in that, The method for obtaining sample test case stimuli and sample response results of the sample test case stimuli includes: The target software function name is determined from a plurality of software function names, wherein the target software function name is the name of the software function module; From the preset correspondence between the software function name, test case incentives, and expected response results, find the sample test case incentives corresponding to the target software function name and the sample response results corresponding to the sample test case incentives.

8. An intelligent testing device for vehicle-mounted software, characterized in that, include: The first acquisition module is used to acquire the target test case incentive and the expected specific response result of the target test case incentive; The second acquisition module is used to obtain the test response results of the target test case stimulus through the vehicle model. The test response results include test-specific response results and test-related response results. The vehicle model includes the software functional modules to be tested. The test-related response results include parameters that affect the specific response results of the test; The third acquisition module is used to input the test response results and the target test case incentives into the pre-built intelligent monitoring model, and obtain the detection results through the intelligent monitoring model; The detection result indicates whether the test response result matches the test case stimulus; The first determining module is used to determine that the software functional module has passed the test if the detection result is yes and the test-specific response result matches the expected specific response result.

9. A computer program product, characterized in that, It includes computer-readable instructions that, when executed on an electronic device, cause the electronic device to implement the vehicle software intelligent testing method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program to enable the electronic device to implement the vehicle software intelligent testing method as described in any one of claims 1 to 7.

11. A computer storage medium, characterized in that, The storage medium carries one or more computer programs, which, when executed by an electronic device, enable the electronic device to implement the vehicle software intelligent testing method as described in any one of claims 1 to 7.

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