Automatic test method and device for whole vehicle rack and computer program product

Abstract

The invention discloses an automatic test method and device for a whole vehicle rack and a computer program product, and the method comprises the steps: obtaining a user behavior feature vector which is used for representing the use interest of a user on electronic and electric parts in various pre-divided vehicle use scenes according to the collected vehicle use behavior data of the user; calculating the correlation among the electronic and electric devices used by the user in various vehicle use scenes; and according to the user behavior feature vector and the correlation, generating an automatic test case for simulating the car using behavior of the user by using a genetic algorithm. The behavior characteristics of the user in different vehicle using scenes can be accurately extracted based on the actual user data, and the automatic test case close to the use habits of the real user is efficiently generated by calculating the correlation between the electronic and electric devices and combining the genetic algorithm, so that the pertinence and comprehensiveness of the test are improved, and the test efficiency is improved. And the test efficiency and accuracy are greatly optimized.

Claims

1. A vehicle bench automated testing method, characterized in that: The following steps are involved: Based on the collected user vehicle use behavior data, a user behavior feature vector is obtained for characterizing the user's interest in using electronic and electrical devices in various pre-divided vehicle use scenarios; Calculate the correlation between the electronic and electrical components used by users in various vehicle usage scenarios; According to the user behavior feature vector and the correlation, a genetic algorithm is used to generate an automatic test case simulating the user's car-using behavior.

2. The method according to claim 1, characterized in that The method of obtaining a user behavior feature vector for characterizing the user's interest in using electronic devices in various pre-divided vehicle use scenarios based on the collected user vehicle use behavior data specifically includes: Extract the usage sequence and duration of each electronic device from the user's car usage behavior data; Calculate the full operation time and single operation time of each electronic and electrical component; Calculate the total operating time of all electronic components and the sum of the single operating time; Calculate the proportion of the full operation time and the proportion of the single operation time of each electronic and electrical device; wherein, the proportion of the full operation time of an electronic and electrical device is the proportion of the full operation time of the electronic and electrical device to the total working time of all electronic and electrical devices, and the proportion of the single operation time of an electronic and electrical device is the proportion of the single operation time of the electronic and electrical device to the sum of the single operation time of all electronic and electrical devices; Multiply the total operation time ratio by the single operation time ratio to obtain the user's interest value for each electronic device; A user behavior feature vector sequence is constructed, where each user behavior feature vector in the sequence represents the user's interest value in using the corresponding electronic device.

3. The method according to claim 1, characterized in that: The vehicle usage scenarios include large scenarios divided according to the external environmental conditions and usage modes of the vehicle, and small scenes divided according to the functional domains of the vehicle under each large scenario; the calculation of the correlation between the use of various electronic and electrical components by users in various vehicle usage scenarios includes calculating the correlation between the electronic and electrical components in the same large scene but different small scenes, and / or the correlation between the electronic and electrical components in the same large scene and small scenes.

4. The method according to claim 3, characterized in that The correlation of each electronic component is calculated as follows: Where I(x,y) is the correlation between continuous random electronic and electrical devices x and y; P(x,y) is the probability distribution of two electronic and electrical devices x and y working at the same time; P(x) is the probability distribution of a single electronic and electrical device x working, and P(y) is the probability distribution of a single electronic and electrical device y working.

5. The method according to claim 1, characterized in that The step of using a genetic algorithm to generate an automatic test case simulating the user's car-using behavior according to the user behavior feature vector and the correlation specifically includes: Using a binary coding method, multi-parameter cascade coding is performed on the user behavior feature vector to form a gene representation of the individual; Construct a fitness function, whose value is defined as the ratio of the number of chromosome traversed paths to the number of all paths, to evaluate the test coverage ability of an individual; According to the set selection probability and strategy, use the roulette wheel method or random sampling method to select individuals with higher fitness from the current population; Performing a single-point crossover operation on the screened individuals, with the crossover frequency being determined according to the correlation between the electronic and electrical components, to generate new individuals; Perform mutation operations on new individuals; According to the fitness, the best individuals are selected from the current population and the newly generated offspring to form a new generation of population. The population size is the sum of the number of vehicle usage scenarios. Repeat the selection, crossover and mutation operations until the preset number of iterations is reached or the optimal solution is found; The final individuals are genetically decoded and converted into a test case set.

6. The method according to claim 5, characterized in that The value of the fitness function is between 0 and 1. The higher the value of the fitness function, the stronger the chromosome coverage ability after encoding.

7. The method according to claim 1, characterized in that Also includes: In the environmental simulation chamber test bench, each node is automatically tested according to the timing in the automatic test case and a test report is generated.

8. A vehicle bench automatic testing device, characterized in that: include: An acquisition module, used to acquire a user behavior feature vector for characterizing the user's interest in using electronic and electrical devices in various pre-divided vehicle use scenarios based on the collected user vehicle use behavior data; A calculation module is used to calculate the correlation between the electronic and electrical components used by users in various vehicle use scenarios; A generation module is used to generate automatic test cases simulating user vehicle use behavior using a genetic algorithm based on the user behavior feature vector and the correlation.

9. A vehicle bench automatic testing device, characterized in that: include: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the vehicle bench automated testing method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions instruct a computer device to execute operations corresponding to the method according to any one of claims 1 to 7.

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