Electronic product automatic test system

Abstract

The invention discloses an automatic test system for an electronic product. The automatic test system for the electronic product comprises a test case generation unit, a test case management unit, a test execution unit, a test data analysis unit, a defect management unit, a test report generation unit and an automatic test tool integration unit, wherein the test case generation unit is used for generating a test case according to a product specification and a test requirement, and importing the test case into the test execution unit. The test cases are preprocessed, so that repeated test cases can be found out, time waste and unnecessary resource waste caused by repeated testing are avoided, the features of the test cases can be automatically recognized through training of the neural network model, the test cases are automatically classified according to the features, and the test efficiency is improved. Therefore, the test efficiency and the test accuracy are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automation testing, in particular to an electronic product automatic testing system. BACKGROUND

[0002] With the continuous development and popularization of electronic products, people's requirements for the quality and reliability of electronic products are also getting higher and higher. In order to ensure the quality and reliability of electronic products, it is necessary to comprehensively test and verify electronic products. The traditional electronic product testing method usually needs a lot of manpower, material resources and time, and the test result is not accurate and reliable enough, and the electronic product testing system may not be able to fully automate the execution of various test tasks, and manual intervention is required, thereby improving the test efficiency and test accuracy, and the test data cannot be effectively counted and analyzed, and valuable test reports and feedback cannot be provided.

[0003] The automatic testing system refers to the automatic control of equipment together to test the functions and indicators of electronic products under various conditions without or with little human participation. At present, the automatic testing system has been widely used in important fields of national economy such as mobile communication, substation measurement and control, electric vehicle motor adjustment, etc.

[0004] In view of the problems in the related art, no effective solution has been proposed so far. SUMMARY

[0005] In view of the problems in the related art, the present application proposes an electronic product automatic testing system to overcome the above technical problems existing in the prior art.

[0006] To this end, the specific technical solutions adopted by the present application are as follows: An electronic product automatic testing system, comprising: a test case generation unit, a test case management unit, a test execution unit, a test data analysis unit, a defect management unit, a test report generation unit and an automatic testing tool integration unit; The test case generation unit is configured to generate test cases according to product specifications and test requirements, and import the test cases into the test execution unit; The test case management unit is configured to save the generated test cases into electronic files for use when executing; The test execution unit is configured to execute the test cases and record the test results generated during the test process; The test data analysis unit is configured to analyze the test results and determine the causes of the problems; The defect management unit is configured to manage the defects and problems found during the test process; The test report generating unit is configured to automatically generate a test report according to the test result and save the test report in the form of an electronic file. The automatic test tool integrating unit is configured to integrate multiple automatic test tools together and realize seamless connection and data exchange between the test tools.

[0007] Preferably, the test case management unit comprises a test case review module, a test case maintenance module, a test case classification module and a test case optimization module. The test case review module is configured to review the test case to ensure that the test case is consistent with the requirement. The test case maintenance module is configured to preprocess the reviewed test case. The test case classification module is configured to classify the preprocessed test case by using a neural network model. The test case optimization module is configured to optimize the test case and the test scene.

[0008] Preferably, the preprocessing of the reviewed test case comprises: Obtaining repeated data corresponding to the product specification and the test case, and performing denoising, filtering and smoothing processing on the repeated data; Joining the unprocessed data rows in the product specification and the test case failure reason to generate a new data table, associating different data tables through external key values to generate a complete data table, and obtaining an accurate data set; Extracting features from the accurate data set, and obtaining feature parameters of the product specification and the test case.

[0009] Preferably, the classification of the preprocessed test case by using the neural network model comprises: Dividing the accurate data set into a training set and a test set according to a proportion by using a back propagation algorithm; Calculating the output value of the forward propagation for the samples in the training set in sequence; Updating the weight value and the bias according to the error back propagation rule, and obtaining a smaller training error value through iteration; Testing the trained accurate data set on the test set, and calculating the error between the prediction result and the real label.

[0010] Preferably, the dividing of the accurate data set into the training set and the test set according to the proportion by using the back propagation algorithm comprises: Randomly shuffling the accurate data set to avoid the influence of the arrangement order of the data on the division result; Dividing the data set into the training set and the test set according to a pre-set proportion; The data of the training set is input into the neural network for training through a back propagation algorithm; During the training process, the data of the test set is used periodically to verify the model to ensure the generalization ability and accuracy of the model.

[0011] Preferably, the method for analyzing the test results to determine the cause of the problem comprises: a preset clustering parameter combination list; The current feature data of the object to be extracted is used as the data set for clustering initialization; The data set is clustered by a clustering algorithm to obtain a set of all clusters; The set obtained according to the statistical data is subjected to deletion processing to eliminate clusters in the set that do not belong to the feature area; The feature area is updated using the clusters in the set after deletion processing to determine the feature data.

[0012] Preferably, the preset clustering parameter combination list comprises: K data are randomly selected from the sample as initial cluster center points; The data objects are clustered by a division algorithm until the size of the centroid no longer changes; The error sum of squares is calculated, and the value of SK is calculated according to the size of K; The smallest SK value is selected, and the K value corresponding to the smallest SK value is used as the optimal number of clusters.

[0013] Preferably, the defect management unit comprises a defect record module, a defect statistics module, a defect repair module, and a defect report module; The defect record module is configured to record detailed information of defects in the test process; The defect statistics module is configured to statistically analyze the recorded defects; The defect repair module is configured to allocate defects for repair tasks after statistical analysis and complete the repair tasks; The defect report module is configured to generate a defect report and report the defect situation and processing results.

[0014] Preferably, the test report generation unit comprises a test result collection module, a report format definition module, a report content generation module, and a storage module; The test result collection module is configured to collect data and information generated during all test runs; The report format definition module is configured to define the format of the generated test report; The report content generation module is configured to convert the test results into readable report content according to the specified format using the rules defined by the report format definition module; The storage module is configured to store the generated test report in an electronic file for later access and reference.

[0015] Preferably, the automatic test tool integration unit comprises a tool selection and configuration module, a test script development module, a test framework integration module, an execution scheduling module, and a result collection and analysis module. The tool selection and configuration module is configured to determine the required automatic test tools and configure and set the automatic test tools. The test script development module is configured to write, maintain, and extend the automatic test scripts, and use them as the main code implementation of the test tasks through the automatic test tools. The test framework integration module is configured to integrate the test scripts with various automatic test tools and test frameworks, and manage the data and information generated during the running process. The execution scheduling module is configured to automatically start, run, and stop the test scripts and the automatic test tools, and automatically distribute the test tasks to multiple test nodes. The result collection and analysis module is configured to collect and save the data and information generated during the test running, and present the results through a visual way.

[0016] The present application has the following advantages: 1. The present application can find repeated test cases by preprocessing the test cases, avoid time waste and unnecessary resource waste caused by repeated testing, and automatically identify the characteristics of the test cases through the training of the neural network model, and automatically classify them according to the characteristics, thereby improving the test efficiency and test accuracy.

[0017] 2. The present application can improve the test efficiency and quality by preprocessing the test cases, avoid unnecessary waste in the test process, thereby saving time and cost, and also help the test personnel to find and fix problems faster, promote the rapid iteration of development and testing.

[0018] 3. The present application can complete the execution of test cases more quickly through the neural network model, reduce the number of useless tests and redundant tests, which can significantly save test time and cost, and the test cases classified based on the neural network model can fully discover potential problems in the code, ensure the quality and stability of the software product, thereby improving the efficiency, quality and reliability of the entire test process, shortening the test period, and improving the work efficiency of the development team and the test team.

[0019] 4. By the clustering analysis algorithm, similar test cases can be clustered together, which helps the test team to find the correlation between test cases, and also helps the test team to find the regularity and abnormality of test results, optimizes the design and execution order of test cases, and improves the test efficiency and coverage. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 is a principle block diagram of an electronic product automatic test system according to an embodiment of the present application.

[0022] In the drawings: 1, test case generation unit; 2, test case management unit; 201, case review module; 202, case maintenance module; 203, case classification module; 204, case optimization module; 3, test execution unit; 4, test data analysis unit; 5, defect management unit; 501, defect record module; 502, defect statistics module; 503, defect repair module; 504, defect report module; 6, test report generation unit; 601, test result collection module; 602, report format definition module; 603, report content generation module; 604, storage module; 7, automation test tool integration unit; 701, tool selection and configuration module; 702, test script development module; 703, test framework integration module; 704, execution scheduling module; 705, result collection and analysis module. DETAILED DESCRIPTION

[0023] To further illustrate the embodiments, the present application provides drawings, which are part of the disclosure of the present application, mainly used to illustrate the embodiments, and can be explained in conjunction with the related description of the specification to explain the operating principle of the embodiments. With reference to these contents, those skilled in the art should understand other possible embodiments and advantages of the present application. The components in the drawings are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0024] According to an embodiment of the present application, an electronic product automatic test system is provided.

[0025] The present application will be further described in conjunction with the drawings and specific embodiments, such as Figure 1As shown, according to the electronic product automatic testing system of the embodiment of the present application, the electronic product automatic testing system comprises: a test case generation unit 1, a test case management unit 2, a test execution unit 3, a test data analysis unit 4, a defect management unit 5, a test report generation unit 6 and an automatic test tool integration unit 7. The test case generation unit 1 is configured to generate test cases according to product specifications and test requirements, and import the test cases to the test execution unit 3. Specifically, the test case generation unit 1 includes determining the purpose, scope, content, method and standard of the test.

[0026] The test case management unit 2 is configured to save the generated test cases in electronic files for use when executing. The test execution unit 3 is configured to execute the test cases and record the test results generated in the test process. Specifically, the test execution unit refers to a unit that encapsulates and manages the combination of test cases, test data and environment to be executed in software testing.

[0027] The test data analysis unit 4 is configured to analyze the test results and determine the causes of problems. The defect management unit 5 is configured to manage the defects and problems found in the test process. Specifically, the defects and problems found in the test process are recorded and managed, including the type, severity, discovery time and repair situation of the defects.

[0028] The test report generation unit 6 is configured to automatically generate a test report according to the test results, and save the test report in the form of an electronic file. The automatic test tool integration unit 7 is configured to integrate multiple automatic test tools together, and realize seamless connection and data exchange between the test tools.

[0029] Specifically, the automatic test script includes: design and writing of test cases, building of automatic test framework, positioning and operation of UI elements, data driving and parameterization setting, assertion and report generation, and script optimization and maintenance.

[0030] In order to facilitate the understanding of the above technical solutions of the present application, the electronic product automatic testing in the actual process will be described in detail.

[0031] For example, in an electronic terminal equipment manufacturing company, in order to improve the test efficiency and product quality, the electronic product automatic test system of the application is introduced. Before the start of the electronic product automatic test, the test case generation unit 1 automatically defines the test strategies and cases of functions, performances and the like according to the product specification and test standards, and transmits them to the test execution unit 3; the test case management unit 2 is responsible for the version saving and calling management of the test cases. In the product online test stage, the test execution unit 3 cooperates with the automatic script, combines the simulation data and the real environment to perform multiple rounds, and simultaneously collects the execution logs in real time. The execution results are handed over to the test data analysis unit 4 for problem tracing, and if an abnormality is found, the defect management unit 5 registers the defect type, level and repair progress. After the test is completed, the test report generation unit 6 automatically generates the PDF report in a visual manner according to the data analysis results. In the whole process, the automatic test tool integration unit 7 ensures the seamless connection of various test tools, supports script execution, data parameterization and UI automatic identification and the like. Thus, the system reduces manual operation, improves test efficiency, supports multi-tool linkage, and realizes intelligent and closed-loop test management.

[0032] In one embodiment, the test case management unit 2 comprises a test case review module 201, a test case maintenance module 202, a test case classification module 203 and a test case optimization module 204. The test case review module 201 is configured to review the test cases to ensure that the test cases are consistent with the requirements.

[0033] The test case maintenance module 202 is configured to preprocess the reviewed test cases. Specifically, the test case maintenance module 202 comprises test case updating, test case abandonment, test case tracing and test case version control. Test case updating: timely updating of test cases to ensure the executability and correctness of test cases. In particular, in the iterative updating process of the product, the test cases are updated in time to adapt to new functions and changes; Test case abandonment: classification and recording of abandoned test cases, and deletion or archiving of the test cases to clean up the test case library and avoid unnecessary interference of the abandoned test cases to the test process; Test case tracing: timely tracing of the source and modification history of the test cases to facilitate the checking and tracing of the quality and correctness of the test cases, and to facilitate the evaluation of the rationality of the test case design; Test case version control: timely saving and management of the historical versions of the test cases through version control means to ensure the integrity and consistency of the test cases.

[0034] It needs to be explained that firstly, through the use case review module 201, the requirement alignment review is carried out to ensure comprehensive coverage; then the use case maintenance module 202 performs updating, discarding, tracing and version control operations, and responds to product iteration in a timely manner; the use case classification module 203 classifies use cases according to function, type, priority and other dimensions, which facilitates screening and calling; finally, the use case optimization module 204 continuously optimizes the test set based on historical execution effect and defect frequency, and improves the use case hit rate and maintainability. Therefore, the use case reuse rate and maintenance efficiency are improved, and efficient and accurate test execution and management are supported.

[0035] In one embodiment, the pre-processing of the reviewed test cases comprises: Obtaining repeated data corresponding to the product specification and the test case, and performing denoising, filtering and smoothing processing on the repeated data; Joining the unprocessed data rows in the product specification and the test case failure reason, generating a new data table, associating different data tables through external key values, generating a complete data table, and obtaining an accurate data set; Specifically, associating different data tables through external key values to generate a complete data table includes the following steps: Design the data table structure: design each data table, determine the table name, table fields, field data types and field constraints, etc.

[0036] Create a data table: create a corresponding data table in the database according to the designed data table structure, and insert data.

[0037] Create foreign key constraints: create foreign key constraints between associated data tables to ensure data integrity and consistency between data tables. Foreign key constraints can be created through the ALTER TABLE statement.

[0038] Perform an association query: perform an association query operation through an SQL statement, and associate different data tables according to external key values. For example, the JOIN keyword can be used to implement the association query operation of the data table.

[0039] Feature extraction is performed on the accurate data set, and feature parameters of the product specification and the test case are obtained.

[0040] Specifically, the selected feature parameters are removed to improve the expression ability and accuracy of the feature parameters.

[0041] The use case classification module 203 is configured to classify the pre-processed test cases through a neural network model.

[0042] In one embodiment, the classifying the preprocessed test case by the neural network model comprises: The accurate data set is divided into a training set and a test set according to a proportion by using a back propagation algorithm; The output value of the forward propagation is calculated for the samples in the training set in turn; The weight and bias are updated according to the error back propagation rule, and the training error is obtained by iteration; The trained accurate data set is tested on the test set, and the error between the prediction result and the true label is calculated.

[0043] In one embodiment, the dividing the accurate data set into a training set and a test set according to a proportion by using a back propagation algorithm comprises: The accurate data set is randomly shuffled to avoid the influence of the arrangement order of the data on the division result; The data set is divided into a training set and a test set according to a pre-set proportion; The data of the training set is input into the neural network for training by using the back propagation algorithm; During the training process, the data of the test set is regularly used to verify the model to ensure the generalization ability and accuracy of the model; After the training is completed, the generalization performance of the model is evaluated by the test result of the test set.

[0044] The neural network model can automatically find the characteristics of key features, which is a great liberation for solving machine learning problems; the test case classified based on the neural network model can fully discover potential problems in the code, ensure the quality and stability of the software product, and thus improve the efficiency, quality and reliability of the entire test process, shorten the test period, and improve the work efficiency of the development team and the test team; The neural network is trained by using the back propagation algorithm, and the classification with high accuracy can be performed.

[0045] It should be explained that first, the system obtains the repeated data in the product specification and the use case, and performs denoising, filtering and smoothing operations. Then, the unprocessed data rows are joined, the data table structure is designed, the table and foreign key constraint are created, and the JOIN query is performed to generate a complete associated data table to obtain an accurate data set. After that, the data features are extracted and feature selection is performed to improve the expression ability. The use case classification module 203 classifies the test case based on the neural network model, verifies the prediction accuracy on the test set after the model is iteratively optimized by using the back propagation algorithm. Thus, the data consistency is enhanced, the use case classification efficiency is optimized, and the overall test data processing capability is improved.

[0046] In one embodiment, the analyzing the test result to determine the cause of the problem comprises: a preset clustering parameter combination list; performing clustering initialization with the current feature data of the object to be extracted as the data set for clustering; performing clustering on the data set by a clustering algorithm to obtain a set of all clusters; performing deletion processing on the set obtained according to the statistical data to eliminate clusters in the set that do not belong to the feature region; updating the feature region by using the clusters in the set after the deletion processing, and determining the feature data.

[0047] In one embodiment, the preset clustering parameter combination list includes: Step 1: randomly select K data from the sample as initial cluster center points; Step 2: perform clustering on the data objects by a division algorithm until the size of the centroid no longer changes; Step 3: calculate the sum of squared errors, and calculate the value of SK by the size of K; Step 4: repeat steps 1 to 3 until the K value is calculated; Step 5: repeat steps 1 to 4 for a preset number of times to obtain the average value of SK; Step 6: select the smallest SK value, and take the corresponding K value as the optimal number of clusters.

[0048] Through the clustering analysis algorithm, similar test cases can be clustered together, which helps the test team to find the correlation between test cases, and also helps the test team to find the rules and abnormalities of test results, optimize the design and execution order of test cases, and improve the test efficiency and coverage.

[0049] The use case optimization module 204 is configured to optimize the writing of the test cases and test scenarios.

[0050] Specifically, the optimization of the writing of the test cases and test scenarios includes test design techniques, data-driven testing techniques, test priority techniques, and test reuse techniques, etc. Specifically, the test design technique is a systematic test design method that can help the test team to find the redundancy and repetition of test cases, reduce the number of test cases, and improve the test coverage. Specifically, the data-driven testing technique is a method of separating test data and test cases, which can greatly reduce the number of test cases and improve the test efficiency and coverage by using external data sources and reading and executing test data through automated test tools. Specifically, the test priority technique is a method of executing test cases in priority order. By setting the priority of test cases, the most important test cases can be executed first, reducing test time and improving test efficiency. Specifically, the test reuse technique is a method of reusing existing test cases and test scenarios. By modifying existing test cases and test scenarios, new test cases and scenarios can be quickly generated, improving test efficiency and accuracy.

[0051] It should be explained that the system first presets the clustering parameter combination, randomly selects K data as the initial center point, and determines the optimal clustering number through iterative calculation. Then, the test data is clustered, the feature area is updated after removing the abnormal cluster, and the problem mode is accurately identified. The test data is separated from the test case by the test case optimization module 204, the execution order is optimized by combining the priority technique, and new scenarios are quickly generated by the reuse technique, thereby improving the utilization rate of test resources.

[0052] In one embodiment, the defect management unit 5 includes a defect recording module 501, a defect statistics module 502, a defect repair module 503, and a defect report module 504. The defect recording module 501 is configured to record detailed information of defects found during testing. The defect statistics module 502 is configured to statistically analyze the recorded defects. The defect repair module 503 is configured to allocate defect repair tasks to the analyzed defects and complete the repair tasks. The defect report module 504 is configured to generate a defect report and report the defect situation and processing results.

[0053] It should be explained that the problems found in testing are first recorded by the defect recording module 501, including type, environment, and reproduction steps. Then, the defect statistics module 502 analyzes the historical defect data and classifies common problem patterns. The system assigns tasks to the relevant responsible persons according to the statistical results by the defect repair module 503, and tracks the repair progress. After processing, the defect report module 504 automatically generates a defect report, summarizes the defect processing situation and repair status, and provides quality audit and improvement reference.

[0054] In one embodiment, the test report generation unit 6 includes a test result collection module 601, a report format definition module 602, a report content generation module 603, and a storage module 604. The test result collection module 601 is configured to collect all data and information generated during testing. The report format definition module 602 is configured to define the format of the test report; The report content generation module 603 is configured to convert the test results into readable report content according to the format defined by the report format definition module 602. The storage module 604 is configured to store the generated test report in an electronic file for later access and reference.

[0055] It should be explained that first, the test result collection module 601 is responsible for collecting the running data and log information of all test stages; then, the report format definition module 602 defines the report template according to the enterprise standard, such as the structure including summary, test item, execution status, defect summary, etc.; next, the report content generation module 603 arranges the test data according to the preset format and generates readable report text; finally, the storage module 604 archives the generated electronic report to the test platform database, supports subsequent query and audit tracking, and ensures the consistency and traceability of the report.

[0056] In one embodiment, the automatic test tool integration unit 7 includes a tool selection and configuration module 701, a test script development module 702, a test framework integration module 703, an execution scheduling module 704, and a result collection and analysis module 705. The tool selection and configuration module 701 is configured to determine the required automatic test tools and configure and set the automatic test tools. The test script development module 702 is configured to write, maintain and extend the automatic test scripts, and use them as the main code implementation of the test tasks through the automatic test tools. The test framework integration module 703 is configured to integrate the test scripts with various automatic test tools and test frameworks, and manage the data and information generated during the running process. The execution scheduling module 704 is configured to automatically start, run and stop the test scripts and the automatic test tools, and automatically distribute the test tasks to multiple test nodes. The result collection and analysis module 705 is configured to collect and save the data and information generated during the test running, and present the results through a visual way.

[0057] It needs to be explained that first, the tool selection and configuration module 701 selects tools such as Selenium and JMeter according to product characteristics and performs parameter configuration; the test script development module 702 writes Python test scripts to realize UI and performance testing; the test framework integration module 703 integrates scripts with the Pytest framework and uniformly manages test data; the execution scheduling module 704 automatically triggers test tasks through Jenkins and distributes them to multiple test devices; and the result collection and analysis module 705 aggregates test logs, generates visual reports to show pass rates and performance indicators. Thus, unified management and analysis of cross-platform automated testing are realized.

[0058] In summary, by means of the above technical solutions of the present application, by preprocessing the test cases, the test efficiency and quality can be improved, unnecessary waste in the test process can be avoided, thereby saving time and cost, and the tester can also help to find and fix problems faster, promoting rapid iteration of development and testing. Through the neural network model, the execution of test cases can be completed more quickly, reducing the number of useless tests and redundant tests, which can significantly save test time and cost, and the test cases classified based on the neural network model can fully discover potential problems in the code, ensuring the quality and stability of the software product, and thus improving the entire test process. Through the clustering analysis algorithm, similar test cases can be clustered together, which helps the test team to discover the relevance between test cases, and also helps the test team to discover the rules and abnormalities of test results, optimize the design and execution order of test cases, and improve test efficiency and coverage. By integrating multiple units, including a test case generation unit, a test case management unit, a test execution unit, a test data analysis unit, a defect management unit, a test report generation unit, and an automated test tool integration unit, the test-related electronic files can be automatically managed in a full-process and systematic manner without human intervention, and finally the results are presented in a visual manner.

[0059] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An automatic testing system for electronic products, characterized in that, The automated testing system for electronic products includes: a test case generation unit, a test case management unit, a test execution unit, a test data analysis unit, a defect management unit, a test report generation unit, and an automated testing tool integration unit; The test case generation unit is used to generate test cases according to the product specifications and test requirements, and import the test cases into the test execution unit. The test case management unit is used to save the generated test cases to electronic files for later use during execution; The test execution unit is used to execute test cases and record the test results generated during the test process; The test data analysis unit is used to analyze the test results and determine the cause of the problem. The defect management unit is used to manage defects and problems discovered during the testing process; The test report generation unit is used to automatically generate a test report based on the test results and save the test report in the form of an electronic file. The automated testing tool integration unit is used to integrate multiple automated testing tools together and achieve seamless connection and data exchange between the testing tools.

2. The automatic testing system for electronic products according to claim 1, characterized in that, The test case management unit includes a test case review module, a test case maintenance module, a test case classification module, and a test case optimization module; The test case review module is used to review the test cases to ensure that the test cases are consistent with the requirements. The test case maintenance module is used to preprocess the reviewed test cases; The test case classification module is used to classify the preprocessed test cases using a neural network model; The test case optimization module is used to optimize the writing of the test cases and test scenarios.

3. The automatic testing system for electronic products according to claim 2, characterized in that, The preprocessing of the reviewed test cases includes: Obtain duplicate data corresponding to product specifications and test cases, and perform noise reduction, filtering and smoothing on the duplicate data; The unprocessed data rows in the product specification and test case failure causes are linked to generate a new data table. Different data tables are then associated through external keys to generate a complete data table and obtain an accurate dataset. Feature extraction is performed on the accurate dataset to obtain feature parameters for the product specifications and test cases.

4. The automatic testing system for electronic products according to claim 3, characterized in that, The method for classifying the preprocessed test cases using a neural network model includes: The backpropagation algorithm is used to divide the accurate dataset into training and test sets according to a certain ratio; The forward propagation output value is calculated sequentially for each sample in the training set; The weights and biases are updated according to the backpropagation rule of error, and the smaller value of the training error is obtained through iteration. The trained accurate dataset is tested on the test set, and the error between the prediction result and the true label is calculated.

5. The automatic testing system for electronic products according to claim 4, characterized in that, The process of dividing the accurate dataset into training and test sets proportionally using the backpropagation algorithm includes: The accurate dataset is randomly shuffled to avoid the influence of the data arrangement order on the partitioning results; The dataset is divided into training and testing sets according to a pre-defined ratio; The training set data is input into the neural network for training using the backpropagation algorithm; During training, the model is periodically validated using test set data to ensure its generalization ability and accuracy. After training, the generalization performance of the model is evaluated using the test results on the test set.

6. The automatic testing system for electronic products according to claim 5, characterized in that, The method for analyzing test results and determining the cause of problems includes: a list of preset clustering parameter combinations; Clustering initialization is performed using the current feature data of the object to be extracted as the clustering dataset; The dataset is clustered using a clustering algorithm to obtain a set of all clusters; The set obtained from statistical data is used for deletion, removing clusters that do not belong to the feature region. The feature regions are updated by deleting the processed clusters from the set to determine the feature data.

7. An automatic testing system for electronic products according to claim 6, characterized in that, The list of preset clustering parameter combinations includes: K data points are randomly selected from the sample as the initial cluster centers; Data objects are clustered using a partitioning algorithm until the centroid size no longer changes; Calculate the sum of squared errors, and then use the value of K to determine the value of SK. Select the smallest SK value and use its corresponding K value as the optimal number of clusters.

8. The automatic testing system for electronic products according to claim 1, characterized in that, The defect management unit includes a defect recording module, a defect statistics module, a defect repair module, and a defect reporting module; The defect recording module is used to record detailed information about defects during the testing process. The defect statistics module is used to perform statistical analysis on the recorded defects; The defect repair module is used to assign defect repair tasks to the statistically analyzed defects and complete the repair tasks. The defect reporting module is used to generate defect reports and report the defect status and processing results.

9. An automatic testing system for electronic products according to claim 1, characterized in that, The test report generation unit includes a test result collection module, a report format definition module, a report content generation module, and a storage module; The test result collection module is used to collect all data and information generated during the test run. The report format definition module is used to define the format for generating test reports; The report content generation module is used to convert the test results into readable report content in a prescribed format according to the rules defined by the report format definition module. The storage module is used to store the generated test report in an electronic file for later access and reference.

10. An automatic testing system for electronic products according to claim 1, characterized in that, The automated testing tool integration unit includes a tool selection and configuration module, a test script development module, a test framework integration module, an execution scheduling module, and a result collection and analysis module. The tool selection and configuration module is used to determine the required automated testing tools and to configure and set the automated testing tools. The test script development module is used to write, maintain, and extend automated test scripts, and to use them as the main code implementation for test tasks through automated testing tools; The test framework integration module is used to integrate test scripts with various automated testing tools and test frameworks, and to manage the data and information generated during the operation. The execution scheduling module is used to automatically start, run, and stop test scripts and automated testing tools, and to automatically distribute test tasks to multiple test nodes; The result collection and analysis module is used to collect and save the data and information generated during test execution, and to present the results in a visual manner.

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