Logistics system automation test method and device, computer device and storage medium

Abstract

Embodiments of the present application disclose a logistics system automatic test method and device, computer equipment and a storage medium. The method comprises: obtaining historical test data and business data of the logistics industry, classifying data of each function module including order entry, inventory query and transportation route planning according to different types or different working modes by using a machine learning algorithm, training to obtain test cases of different working types or different working modes in different function modules, the test cases including expected test results; according to the test scene of different working types or different working modes in different function modules, the corresponding test cases are called and the test is executed, the running state of the test scene and the abnormal alarm are displayed through a trend big board real-time monitoring panel, and the test results are analyzed to generate a visual test report of a graphical interface structure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics system software testing, and particularly relates to a logistics system automatic testing method and device, computer equipment and a storage medium. BACKGROUND

[0002] In the logistics industry, with the rapid growth of business and the comprehensive opening of multi-platform, full-channel and integrated logistics solutions to the outside world, the logistics system is facing the challenges of frequent changes and rapid iteration. The traditional manual testing method cannot meet the needs of such rapid development, and automation testing must be relied on to reduce repetitive labor, improve testing efficiency and ensure the quality and stability of the logistics system. Self-developed logistics systems, as the core support of logistics business, are directly related to the efficiency of logistics services and user experience.

[0003] However, at present, for the test tools of self-developed logistics systems, enterprises find that these tools cannot fully adapt to their specific business needs after introducing the test tools, resulting in poor test results. For example: as business needs change, test scripts and test cases need to be frequently updated and maintained manually, increasing the cost and difficulty of testing work. The existing test tools do not have a stable and reliable test environment in complex logistics system testing, and various factors result in high cost, poor stability, low accuracy and low efficiency of the current logistics system testing. SUMMARY

[0004] The embodiments of the present application provide a logistics system automatic testing method, device, computer equipment and storage medium, which aims to improve the testing efficiency of the logistics system, shorten the testing cycle, reduce the testing cost and improve the quality and stability of the system through automatic testing means.

[0005] The technical solutions are as follows:

[0006] In a first aspect, the embodiments of the present application provide a logistics system automatic testing method, comprising:

[0007] Obtain historical test data and business data of the logistics industry, classify the data of each functional module including order entry, inventory query and transportation route planning according to different types or different working modes by using a machine learning algorithm, train to obtain test cases of different working types or different working modes in different functional modules, and the test cases include expected test results;

[0008] According to the test scene of different working types or different working modes in different function modules, the corresponding test case is called and the test is performed, including separately testing the test scene of different working types or different working modes in different function modules in the first test stage; if the test result of a working mode in the first test stage does not meet the expectation, the test in the second test stage is entered, and the test scene whose test result in the first stage does not meet the expectation is repeatedly tested by increasing the test case and the test frequency;

[0009] In each stage of the test, the running state of the test scene and the abnormal alarm are displayed through the trend big board real-time monitoring panel, and the test result is analyzed to generate a visual test report of a graphical interface structure.

[0010] Further, the method further comprises performing suite test according to the actual test scene, comprising:

[0011] Obtaining the function modules associated in business, encapsulating the function modules with the association relationship according to the operation steps and the logical relationship of each business link to form a suite combination;

[0012] Calling the test cases of the associated function modules and putting them into the same container, selecting and configuring the test suite according to the business logic, and configuring the execution order of the test cases;

[0013] Performing the test according to the execution order of the test cases configured in the test suite and recording the execution result.

[0014] Further, the calling of the test cases of the associated function modules and the putting into the same container, the selection and configuration of the test suite according to the business logic, and the configuration of the execution order of the test cases further comprise: according to the change of the application scene, recombining and encapsulating the suite according to the business logic or adjusting the test cases by adding, removing or changing the order, performing the test and recording the execution result.

[0015] Further, the historical test data and the business data of the logistics industry are obtained, the machine learning algorithm is used to classify the data of each function module including order entry, inventory query and transportation route planning according to different types or different working modes, the test cases of different working types or different working modes in different function modules are trained, and the test cases include the expected test result; comprising:

[0016] Obtaining the historical test data and the business data of the logistics industry;

[0017] Obtaining the user operation test script of each function module;

[0018] The acquired logistics industry historical test data and business data are loaded into the user operation test scripts of the logistics system functional modules recorded by the recording tool according to different business types or working modes in the logistics system functional modules, to form test cases under different functional modules, different business types or different working modes.

[0019] Further, the acquired logistics industry historical test data and business data are loaded into the user operation test scripts of the logistics system functional modules recorded by the recording tool according to different business types or working modes in the logistics system functional modules, to form test cases under different functional modules, different business types or different working modes.

[0020] According to the test logic, the keywords of the functional modules to be tested are extracted;

[0021] The test scripts of the functional modules are called through the keywords, and the logistics industry historical test data and business data are combined to generate test cases under different functional modules, different business types or different working modes;

[0022] When a single functional module needs to be tested for one or several business types, the test cases of the business type or working mode are directly called through the keywords for separate testing;

[0023] When a suite function needs to be tested, the corresponding functional modules and the specified business types or working modes under the functional modules are called according to the logical sequence of the keywords, the execution order of the test cases is sorted according to the logical sequence of the keywords, the suite product function test is performed according to the execution order of the arranged test cases, and the test cases with priority are executed according to the priority order.

[0024] Further, the method further includes a third test stage after the test results of the second test stage reach the expectation, and the test of the third test stage includes:

[0025] Different frequency and type of business data during order peak period and emergency are generated by a data simulator; and / or real-time interaction data between functional modules including between logistics equipment and monitoring system, between transportation vehicle and dispatch center are generated; and / or interaction data including between logistics system and warehouse management system, transportation management system, order management system are generated; and / or an integrated test environment is generated to connect each subsystem to generate interaction data under actual business scenarios; machine learning algorithm is used to classify the data of each functional module including order entry, inventory query, transportation route planning according to different types or different working modes, to train test cases of different working types or different working modes in different functional modules, to call corresponding test cases according to test scenarios of different working types or different working modes in different functional modules and perform testing.

[0026] Further, the analyzing the test result and generating the visual test report of the graphical interface structure includes an operable window configured with a search and filtering function, and further includes a multi-factor visual list including a report name, a task progress, an execution type, an execution case condition, a running result, an execution result or error information, a successful case, a system corresponding time, an execution time consumption, and an execution time, and further includes a test case execution pass rate of different function modules displayed using a column chart, a system response time change trend displayed using a different color line chart, and detailed error information listed in a table form.

[0027] In a second aspect, the embodiment of the present application further provides a logistics system automatic test device, comprising:

[0028] A test case generation unit is configured to obtain historical test data and business data of the logistics industry, classify data of each function module including order entry, inventory query, and transportation route planning according to different types or different working modes, and train test cases of different working types or different working modes in different function modules, wherein the test cases include expected test results.

[0029] A test execution unit is configured to call and execute the test cases according to test scenarios of different working types or different working modes in different function modules, including separately testing the test scenarios of different working types or different working modes in different function modules in a first test stage; if the test result of a certain working mode in the first test stage does not meet the expectation, entering a second test stage, and repeatedly testing the test scenarios of the first stage whose test result does not meet the expectation by increasing test cases and test frequency.

[0030] A test result output unit is configured to display a test scenario running state and an abnormal alarm through a trend dashboard real-time monitoring panel in each test stage, analyze the test result, and generate a visual test report of a graphical interface structure.

[0031] In a third aspect, the embodiment further provides a computer storage medium, which stores a plurality of instructions suitable for being loaded and executed by a processor.

[0032] In a fourth aspect, the embodiment further provides a computer device, which comprises a processor and a memory, wherein the memory stores a computer program suitable for being loaded and executed by the processor.

[0033] The technical scheme provided by some embodiments of the present application has at least the following beneficial effects:

[0034] The historical test data and business data of the logistics industry are acquired, machine learning algorithms are used to classify the data of each functional module including order entry, inventory query, and transportation route planning according to different types or different working modes, test cases of different working types or different working modes in different functional modules are trained, and the test cases include expected test results; the corresponding test cases are called and executed according to the test scenarios of different working types or different working modes in different functional modules, including separately testing the test scenarios of different working types or different working modes in different functional modules in a first test stage; if the test result of a working mode in the first test stage does not meet the expectation, the test enters a second test stage, in which the test scenarios whose test results in the first stage do not meet the expectation are repeatedly tested by increasing test cases and test frequency; in each stage of the test, the running state of the test scenario and abnormal alarm are displayed on a trend big board real-time monitoring panel, and the test result is analyzed to generate a visual test report with a graphical interface structure, which can improve the test efficiency of the logistics system, shorten the test cycle, reduce the test cost, and improve the quality and stability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. 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 effort on the basis of these drawings.

[0036] Figure 1 is a method flowchart provided by the embodiments of the present application;

[0037] Figure 2 is a trend big board real-time monitoring structure schematic diagram provided by the embodiments of the present application;

[0038] Figure 3 is a device schematic diagram provided by the embodiments of the present application;

[0039] Figure 4 is a computer device structure schematic diagram provided by the embodiments of the present application;

[0040] Figure 5 is a schematic diagram of a computer storage medium provided by the embodiments of the present application. DETAILED DESCRIPTION

[0041] With reference to the drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0042] The flowcharts shown in the drawings are only illustrative, and do not necessarily include all the contents and operations / steps, nor are they necessarily executed in the order described. For example, some operations / steps can be further decomposed, combined or partially merged, so the actual execution order may be changed according to the actual situation.

[0043] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the terms "first", "second", etc. are used to distinguish the same or similar items with basically the same function and role. Those skilled in the art can understand that the terms "first", "second", etc. do not limit the quantity and execution order, and the terms "first", "second", etc. also do not necessarily mean different.

[0044] It should be understood that the terms used in the present application are only for the purpose of describing specific embodiments and do not intend to limit the present application. As used in the present application specification and the appended claims, unless otherwise clear from the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0045] It should also be understood that the term "and / or" used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0046] In the logistics industry, with the rapid growth of business and the comprehensive opening of multi-platform, full-channel and integrated logistics solutions to the outside world, the logistics system is facing the challenges of frequent changes and rapid iteration. The traditional manual testing method has been unable to meet the needs of such rapid development, and must rely on automated testing to reduce repetitive labor, improve testing efficiency and ensure the quality and stability of the logistics system. As the core support of logistics business, the correctness of the function and the optimization of the performance of the self-developed logistics system are directly related to the efficiency of logistics services and user experience.

[0047] However, at present, for self-developed logistics systems, enterprises find that after introducing test tools, these tools cannot fully adapt to their specific business needs, resulting in poor test results. For example: as business needs change, test scripts and use cases need to be updated and maintained frequently by manual work, increasing the cost and difficulty of testing work. The existing test tools do not have a stable and reliable test environment in complex logistics system testing. Various factors result in high cost, poor stability, low accuracy, and low efficiency of the current logistics system testing. Therefore, under such circumstances, logistics enterprises urgently need an efficient, intelligent, and easy-to-use automated testing platform to improve the testing efficiency and quality of logistics systems, reduce testing costs and maintenance difficulty, and make enterprises more competitive in the international market.

[0048] Please refer to Figure 1 , Figure 1 is a schematic flowchart of a logistics system automated testing method provided by an embodiment of the present application. The logistics system automated testing method can be implemented by a computer device, which can be deployed on a single server or a server cluster. It can also be deployed on a handheld terminal, a notebook computer, a wearable device, or a robot, etc.

[0049] It should be noted that the acquisition of any information involved in the provided method is in accordance with relevant regulations and with the consent of the user, and does not infringe on the user's privacy or violate relevant laws and regulations.

[0050] The method of this embodiment builds a layered micro-service architecture platform according to test management, test execution, and result analysis integrated multiple models. The RBAC model is a kind of access control model with standardization, scalability, and manageability. It is responsible for test case management, task scheduling, and permission control through the RBAC model. It is driven by multiple frameworks through Selenium (Appium, Postman, JMeter can also be selected), and integrates Allure / ExtentReports to analyze test results and generate visual reports.

[0051] Selenium is a widely used open-source tool for web automation testing. It is a browser automation tool that can simulate user interactions with web applications, such as clicking buttons, filling out forms, and submitting data. Selenium supports multiple programming languages, such as Java, Python, and C#, and can be used with various browsers, such as Chrome, Firefox, and Safari. It is widely used for web application automation testing and web data scraping tasks. Selenium mainly consists of three parts: Selenium IDE, a Firefox browser plugin that can record and replay user interactions and export them as test cases in multiple languages; Selenium WebDriver, which provides APIs for web automation, mainly for browser control, page element selection, and debugging; and Selenium Grid, which provides the ability to run selenium tests on different browsers on different machines.

[0052] Appium is a tool for mobile application testing. It supports both iOS and Android platforms and can simulate user interactions on mobile devices, such as touch and swipe, to verify the functionality and performance of mobile applications. For example, to test a logistics delivery app, Appium can simulate user interactions on the app, such as viewing order status and scheduling pickup time. Appium can connect to mobile devices, control device operations, and obtain feedback information, enabling automated testing of mobile logistics applications.

[0053] Jenkins is a continuous integration service for software development. It runs in a servlet container and supports software configuration management tools. It can execute projects based on Apache Ant and Apache Maven, as well as arbitrary Shell scripts and Windows batch commands.

[0054] GitLab-CI is a continuous integration system that works with GitLab (of course, there are other continuous integration systems that can also work with GitLab, such as Jenkins). And the version of GitLab 8.0 is integrated with GitLab-CI by default and enabled by default. gitlab-ci-server gitlab-ci-runner gitlab-ci-server is responsible for scheduling, triggering Runner, and obtaining return results.

[0055] Specifically, as shown in Figure 1 The logistics system automatic test method provided by the embodiment can include the following steps:

[0056] S101, obtain logistics industry historical test data and business data, use machine learning algorithm to classify the data of each function module including order entry, inventory query, transportation route planning according to different types or different working modes, train to obtain test cases of different working types or different working modes in different function modules, and the test cases include expected test results;

[0057] Obtaining logistics industry historical test data and business data is more in line with the data test characteristics of the logistics industry, which can improve the authenticity and accuracy of the test. In terms of specific implementation, different test data can be called through a unified interface based on the RBAC model, such as historical data and business data stored in various servers or databases and the like. For example, use clustering algorithm to classify logistics order data and find different types of order modes, such as ordinary orders, urgent orders, bulk orders, etc. Then generate corresponding test cases according to these modes and test different types of orders in detail. The difference between ordinary orders, urgent orders, and bulk orders may be in the time requirements, the number of specified time requirements, etc. However, for different types or different working modes of order entry, the name, quantity, weight, destination, etc. of the goods are used as input features, and the assigned vehicle and / or whether the order can be correctly processed are used as output labels. In logistics business, a large number of user operations are involved, such as order entry, inventory query, transportation route planning, etc. The logistics industry historical data and business data are huge, and the machine learning algorithm is used for comprehensive analysis, classification, learning, and training to obtain test cases, which can improve the generation efficiency and quality of test cases, cover more test scenarios, and find more potential problems. At the same time, as the test data continues to accumulate, the machine learning model is continuously optimized, and the generated test cases are more accurate and effective; the machine learning algorithm includes but is not limited to random forest, gradient boosting tree, or neural network algorithm.

[0058] S102, according to the test scene of different working types or different working modes in different function modules, the corresponding test case is called and the test is executed, including separately testing the test scene of different working types or different working modes in different function modules in the first test stage; if the test result of a working mode in the first test stage does not meet the expectation, the test in the second test stage is entered, and the test scene whose test result in the first stage does not meet the expectation is repeatedly tested by increasing the test case and the test frequency.

[0059] In the test process of the logistics system, different test stages and business scenarios have different requirements for the test strategy. The adaptive test strategy adjustment is adopted in the embodiment, and the test strategy can be dynamically adjusted according to the test result and the system state. For example, in the initial stage of the test, a comprehensive test strategy is adopted to test each function module of the logistics system in detail. When it is found that there are many problems in a certain function module or a certain working mode or working type under a certain module, the test case and the test frequency of the module or the working mode or working type under the module are automatically increased for focused testing. At the same time, the test load or quantity is dynamically adjusted according to the performance indexes such as response time and throughput fed back during the test, so that the overall situation of the test can be comprehensively reflected, and the accuracy of the test is improved. Whether the test result meets the expectation is determined by using an assertion method with a clear message parameter, comparing with the expectation or whether the expectation is met.

[0060] S103, in each stage of the test, the running state of the test scene and the abnormal alarm are displayed through a trend big board real-time monitoring panel, and the test result is analyzed to generate a visual test report with a graphical interface structure.

[0061] In the embodiment, the related factors causing system errors are found out through correlation analysis, such as a certain specific operation step, input data or system configuration that may cause system errors. The errors are classified by using clustering analysis to find out different types of error modes, such as interface display error, data calculation error and interface calling error. At the same time, a detailed visual test report with a graphical interface structure is generated, including error distribution, problem severity, improvement suggestions and the like, which provides strong support for the optimization and repair of the system.

[0062] The depth analysis of the test result can help the tester to more comprehensively understand the quality status of the system and to make a more effective test plan and repair scheme. At the same time, data support can be provided for the continuous improvement of the system to improve the overall quality and stability of the system.

[0063] The machine learning-based test case generation method can improve the generation efficiency and quality of test cases, cover more test scenarios, and discover more potential problems. At the same time, as the test data continues to accumulate, the machine learning model can be continuously optimized to generate more accurate and effective test cases.

[0064] The logistics system automated testing method can test a certain functional module separately, such as testing the order receiving link, the driver order receiving link, and the transportation scheduling link. In the order receiving link, the system needs to receive order information from the outside and perform preliminary audit; in the driver order receiving link, the system needs to generate an order according to the order information and submit it to the system for audit; in the transportation scheduling link, the system needs to select appropriate transportation mode and transportation vehicle according to the destination and transportation requirements of the goods; in the final delivery link, the system needs to confirm whether the goods are accurately delivered to the customer.

[0065] However, the business process of the logistics system is complex and interrelated, such as the entire process from order receiving, warehouse management, transportation scheduling to final delivery. Therefore, in some embodiments, the logistics system automated testing method further includes suite testing according to actual test scenarios, which can test the entire logistics business process or a certain stage of logistics business according to requirements, and can continuously and uninterruptedly test each link node in the entire process testing link or the regulated stage of logistics business testing link. The implementation method is mainly to build a business process model or a suite, bind and encapsulate the operation steps and logical relationships of each business link, and then use an automated testing tool to perform testing according to the model or the suite. For example, testing an e-commerce logistics system simulates the entire process of receiving orders from the warehouse, generating orders, arranging domestic transportation, and finally delivering the goods to the customer. The input, output, and operation steps of each link are determined, and then the business model or suite is built to continuously and uninterruptedly perform testing, including in the order receiving link, the system needs to receive order information from the outside and perform preliminary audit; in the driver order receiving link, the system needs to generate an order according to the order information and submit it to the system for audit; in the transportation scheduling link, the system needs to select appropriate transportation mode and transportation vehicle according to the destination and transportation requirements of the goods; in the final delivery link, the system needs to confirm whether the goods are accurately delivered to the customer. This automated testing method checks whether the system correctly processes data, updates status, and executes business rules in each link. For example, in the customs declaration link, it checks whether the system can correctly generate a declaration form and interact with the customs system; in the transportation scheduling link, it checks whether the system can reasonably arrange transportation vehicles according to real-time traffic conditions and transportation requirements. Through business process automated testing technology, the coherence and accuracy of the entire logistics business process can be ensured, and the overall operation efficiency of the logistics system can be improved.

[0066] The implementation method of suite testing according to actual test scenarios includes:

[0067] Obtain function modules associated in business, and combine the function modules with association according to operation steps and logical relations of each business link.

[0068] Retrieve test cases of the associated function modules and put them into the same container, select and configure test suites according to business logic, and configure the execution order of the test cases.

[0069] Execute the test according to the execution order of the test cases configured in the test suite and record the execution result.

[0070] By constructing a business process suite model, the operation steps and logical relations of the associated business links are combined and bound, and finally the test is executed according to the constructed business process suite model, which can ensure the coherence and accuracy of the entire logistics business process and improve the overall operation efficiency of the logistics system.

[0071] Obtain function modules associated in business, and combine the function modules with association according to operation steps and logical relations of each business link. For example, associate the two functions of transport vehicle arrangement and transport route planning to form a suite, associate the two function modules of order input and freight calculation to form a suite, and associate the four function modules of order input, freight calculation, transport vehicle and transport route planning to form a suite. A specific example is to configure the payment process as a suite containing four function modules of login, selecting goods, payment and refund, and configure it to be executed every morning. In this way, when the payment process is started, the system will select test cases in sequence according to the execution order of the four function modules and execute them according to the execution order. If there are multiple business types or working mode scenarios in a certain module, the system can provide the selected combined scenarios for screening or selection, or perform the test one by one in a permutation and combination manner.

[0072] In terms of retrieving test cases, test cases of the associated function modules are retrieved and put into the same container, test suites are selected and configured according to business logic, and the execution order of the test cases is configured. Test cases can be selected by dragging from the test case library, and each test case of the function modules associated in business is associated with the CI / CD pipeline in logical order, and the execution order is configured. Finally, the test cases configured in the test suite are triggered for automatic execution according to the execution order, and the execution result is recorded.

[0073] During the execution of the suite and the test cases, the execution result of each suite or case is recorded, including the pass rate, time consumption and error log, which supports historical execution comparison and trend analysis.

[0074] In some embodiments, the test cases of the associated function modules are called and put into the same container, the test suite is selected and configured according to the business logic, and the execution order of the test cases is configured, including recombining and packaging the suite according to the business logic according to the change of the application scene, or adjusting the test cases by adding, removing, or changing the order, executing the test and recording the execution result. For example, after entering the second test stage, the key test is repeated by increasing the test cases and the test frequency. After removing a certain function module in the suite, only the test case of the module is removed, and the other test cases remain unchanged. Similarly, after changing the order of the suite, the order of the test cases is adjusted accordingly. In this way, the time required for full reassembly can be saved, and the test efficiency can be improved.

[0075] In some embodiments, historical test data and business data of the logistics industry are obtained, and machine learning algorithms are used to classify data of various function modules including order entry, inventory query, and transportation route planning according to different types or different working modes, to train test cases of different working types or different working modes in different function modules, and the test cases include expected test results.

[0076] Obtain historical test data and business data of the logistics industry;

[0077] Obtain user operation test scripts for each function module;

[0078] Load the obtained historical test data and business data of the logistics industry according to different business types or working modes in each function module of the logistics system into the user operation test scripts of each function module of the logistics system recorded by the recording tool, to form test cases under different business types or different working modes of different function modules.

[0079] By loading the obtained historical test data and business data of the logistics industry according to different business types or working modes in each function module of the logistics system into the user operation test scripts of each function module of the logistics system recorded by the recording tool, test cases under different business types or different working modes of different function modules are formed, which not only improves the test efficiency, but also realizes full coverage of complex scenarios in the logistics industry.

[0080] In some embodiments, the test cases under different business types or different working modes of different function modules are formed by loading the obtained historical test data and business data of the logistics industry according to different business types or working modes in each function module of the logistics system into the user operation test scripts of each function module of the logistics system recorded by the recording tool, including:

[0081] Extract the keywords of the function modules that need to be tested according to the test logic;

[0082] The test scripts of each functional module are called by keywords, combined with historical test data and business data of the logistics industry, to generate test cases under different business types or different working modes of different functional modules;

[0083] When one or several business types of a single functional module need to be tested, the test cases of the business type or working mode are directly called by keywords for separate testing;

[0084] When the suite function needs to be tested, the corresponding functional modules and the specified business types or working modes under each functional module are called according to the logical sequence of the keywords, the execution order of the test cases is sorted according to the logical sequence of the keywords, and the suite product function test is performed according to the execution order of the arranged test cases. For test cases marked with priority, execute according to the priority order.

[0085] The logistics system often involves a large amount of data processing, such as the weight, volume, destination, and transportation time of goods. The formation of test cases includes applying machine learning algorithms to historical test data and business data of the logistics industry to generate test cases under different working types or different working modes of different functional modules.

[0086] In the early stage of testing, a series of keywords related to logistics business are defined, such as "create order", "query inventory", "arrange transportation", etc. Test scenarios are described by writing test scripts containing these keywords. For example, one test script contains the "fee payment" keyword, and another test script may contain the "create order - query inventory - arrange transportation" keyword sequence. The test system can call the corresponding business logic module according to these keywords to execute the test operation. When the "create order" keyword is executed, the framework calls the business logic module or business function module of creating an order to simulate the user's operation of creating an order; when the "query inventory" keyword is executed, the test script of the inventory query module is called to check whether the inventory meets the order requirements; when the "arrange transportation" keyword is executed, the transportation arrangement module test script is called to allocate transportation vehicles for the order.

[0087] When testing, the test scripts of each function module are called by keywords, combined with historical test data and business data of the logistics industry, to generate test cases under different business types or different working modes of different function modules. When testing a single function module of one or several business types, directly call the test cases of the business type or working mode through the keyword for separate testing; when testing the suite function, call the corresponding function module and the specified business type or working mode under each function module according to the logical sequence of the keyword, and sort the execution order of the test cases according to the logical sequence of the keyword, and perform the suite product function test according to the execution order of the arranged test cases. For test cases marked with priority, execute according to the priority order.

[0088] By calling keywords, test scripts can be written by understanding the logistics business process and the meaning of the keyword. At the same time, when the business logic of the logistics system changes, only the business logic module corresponding to the keyword needs to be modified, without the need to modify a large number of test scripts, thereby reducing the cost of test maintenance.

[0089] In some embodiments, the script configuration process is as follows: the tester simulates the user's operation on the interface of the logistics management system through the recording tool function in the Selenium model. Taking a logistics information query system as an example, a test script is written using Selenium. The script can simulate the user opening the browser, entering the URL of the logistics system, logging into the system, and then inputting the order number for query. Selenium will automatically operate the browser to complete these steps and check whether the query result is correct. At the same time, Selenium also supports multiple browsers such as Chrome and Firefox, and can test on different browsers to ensure the compatibility of the system on various browsers. Specifically, for example, when entering an order, the tester opens the order entry page of the logistics management system, and successively inputs the name, quantity, weight, destination, etc. of the goods, and then clicks the submit button. The recording tool automatically records each step in the operation process, including the position of the mouse click, the content of the keyboard input, the page jump time, etc. and generates the corresponding test script, which is finally formed into a test case through playback. During the subsequent playback process, the test script automatically executes these recorded operations to simulate the user's behavior, thereby verifying whether the functions of the logistics system under different operation scenarios are normal. For example, when testing the order processing function of the logistics system, the entire process from order creation to order allocation of transportation vehicles is recorded, and when the test script is played back, the system will execute the steps in sequence to check whether the system can correctly process the order and allocate vehicles to ensure the smoothness of the business process. If the system has errors at a certain step, such as incorrect saving of order information or unreasonable allocation of vehicles, the tester can discover and repair it in time.

[0090] The embodiment separates the different external data such as historical test data and business data of the logistics industry from the test script, and provides different test data for the test script through an external data source such as an Excel table, a database, and the like. For example, to test the freight calculation function of the logistics system, an Excel data table containing different freight weights, volumes, and destinations is provided. Each row in the data table represents a set of test data, including the weight, volume, destination, and expected freight of the goods. The test script reads the data from the table during runtime, sequentially performs freight calculation, and verifies whether the calculation result meets the expectation. For example, when the script reads a set of data with a freight weight of 100 kg, a volume of 2 cubic meters, and a destination of City A, the test system will calculate the freight according to the preset freight calculation rules, and then compare the calculation result with the expected freight in the table. If they are consistent, it means that the freight calculation function under this set of data is normal; if they are inconsistent, it means that the system may have a problem and needs to be further investigated. By separating the test data from the test script, data management and maintenance can be facilitated, and when the business rules of the logistics system change, only the data in the external data source needs to be modified, without modifying the test script, thereby improving the flexibility and maintainability of the test.

[0091] Due to the complex and variable application scenarios of the logistics industry, the same function often needs to be tested multiple times to ensure that it works normally under different environments and conditions. Using the script recording and playback technology, the tester only needs to record the script once, which can be played back multiple times when needed, increasing the test frequency of the test script, or using different test data on the same script to form different test cases, which can greatly improve the test efficiency.

[0092] In some embodiments, the method further comprises a third test stage after the test result of the second test stage reaches the expectation, and the test of the third test stage comprises:

[0093] generating business data of different frequencies and types during order peak periods and emergencies through the data simulator; and / or generating data for real-time interaction between functional modules including the logistics equipment and the monitoring system, the transportation vehicle and the dispatch center; and / or generating interaction data between the logistics system and the warehouse management system, the transportation management system, and the order management system; and / or generating an integrated test environment to connect various subsystems to generate interaction data under actual business scenarios; and / or using a machine learning algorithm to classify the data of each functional module such as order entry, inventory query, and transportation route planning according to different types or different working modes, train test cases for different working types or different working modes in different functional modules, and retrieve and execute the test cases according to the test scenarios of different working types or different working modes in different functional modules.

[0094] The data simulator generates business data of different frequencies and types during order peak periods and emergencies; by simulating peak data interaction scenarios, the frequency and quantity of data transmission can be increased to check whether the system can handle these data in a timely manner, ensuring the real-time and accuracy of logistics operations.

[0095] There is a large amount of real-time data interaction in the logistics system, such as data transmission between logistics equipment and monitoring systems, information exchange between transportation vehicles and dispatch centers, etc. The data simulator generates real-time interaction data between each functional module, including between logistics equipment and monitoring systems, between transportation vehicles and dispatch centers, simulates these real-time data interaction scenarios, and verifies the performance and stability of the system in a real-time data environment.

[0096] When testing the automated shelves of the logistics warehouse system, simulate the real-time data communication between the shelves and the warehouse management system. Automated shelves need to feed back information such as storage location and quantity of goods to the warehouse management system in real time, while receiving instructions from the warehouse management system, such as storing and taking out goods. During testing, by simulating real-time data interaction scenarios, the response time and operation accuracy of the shelves when receiving storage and removal instructions are checked.

[0097] Logistics systems often involve the integration of multiple subsystems, such as warehouse management systems, transportation management systems, order management systems, etc. In some embodiments, by establishing an integrated test environment, each subsystem is connected to simulate data interaction and business processes in actual business scenarios. For example, when testing a comprehensive logistics system of a large logistics enterprise, the warehouse management system needs to transfer order information with the transportation management system, and the transportation management system needs to feed back transportation status to the order management system. In the integrated test environment, simulate business processes such as order creation, warehouse allocation, and transportation arrangement to check whether the data transmission between each subsystem is accurate and timely. At the same time, compatibility problems between subsystems can be found. Different subsystems may use different technical architectures and data formats, and may encounter data format incompatibility, interface call failure, etc. during integration. Through integrated testing, these problems can be found and fixed and optimized in a timely manner to ensure that each subsystem can work together to achieve integrated management of logistics business.

[0098] In some embodiments, to facilitate testers' timely understanding of test progress and system status, real-time visualization of the testing process can be implemented. At each stage of testing, a real-time monitoring panel displays the test scenario's running status and abnormal alarms, and analyzes the test results to generate a graphically-based visual test report. This graphical interface displays the execution status of test cases, system performance metrics, error messages, etc. In some embodiments, an HTML / PDF report is automatically generated after execution, displaying the pass rate, failed test cases, and error stack traces. It also supports filtering results by module, priority, execution time, and other dimensions. In some embodiments, trend charts (such as daily pass rate) and distribution charts (such as defect type percentages) can also be output. Furthermore, it supports test case execution time statistics to identify performance bottlenecks. Reports can also be exported to Excel, email, or integrated into tools such as Jira and Slack. Specifically, for example... Figure 2 As shown, the project's operational status is displayed in real-time through a trend dashboard monitoring panel, including displays such as the current number of tasks, recent execution results, number of projects running in real-time, top rankings of test cases by usage, total number of test cases, number of suites, and number of execution scenarios for the day. It also includes product data statistics tables, operational trend charts, and execution trend charts, displayed in bar and line graphs. In this embodiment, bar graphs are used to display the pass rate of test cases for different functional modules, line graphs show the trend of system response time changes, and tables list detailed error information. Testers can intuitively see various situations during the testing process, promptly identify problems, and handle them. The visualization interface also includes search and filter options to facilitate testers quickly locating specific test cases or error messages. By monitoring key performance indicators, the stability of the testing environment can be ensured, and automatic alarms are triggered in case of anomalies, notifying relevant personnel for handling. This embodiment supports historical data backtracking to analyze the trend of test effect changes.

[0099] At each stage of testing, a real-time monitoring panel displays the test scenario's operational status and anomaly alarms, analyzes the test results, and generates a graphically-based visual test report. This allows for real-time monitoring of the logistics system's performance data, helping logistics companies optimize resource allocation, improve testing efficiency, and reduce the time spent troubleshooting. Furthermore, it provides a more intuitive understanding of the system's performance and stability, offering strong support for system optimization and repair.

[0100] The automatic test method of the present application can greatly improve test efficiency, reduce human errors, ensure the stability and reliability of the logistics system, and quickly find and fix problems, and can quickly complete regression testing to ensure that each update does not damage the original function, thereby accelerating the iteration process of the logistics system. It can also monitor the performance data of the logistics system in real time to help logistics enterprises optimize resource allocation and improve operational efficiency. For example, through performance testing, bottlenecks in the logistics system can be found and appropriate optimization measures can be taken. At the same time, the successful development of the present application helps to promote the sustainable development of the logistics industry, and by improving the efficiency and reliability of the logistics system, resource waste and environmental pollution can be reduced to achieve green logistics. It can also promote the standardization and standardization development of the logistics industry and improve the overall logistics level of society. It can also ensure the stability and reliability of the self-developed logistics system and reduce business interruptions and losses caused by system failures. In addition, it can also help companies better understand the performance characteristics and optimization direction of the self-developed logistics system, providing strong support for subsequent iterations and updates.

[0101] The following is an embodiment of the device of the present application, which can be used to execute the method embodiment of the present application. For details not disclosed in the device embodiment of the present application, please refer to the method embodiment of the present application.

[0102] Please refer to Figure 3 which shows a logistics system automatic test device provided by an exemplary embodiment of the present application, comprising:

[0103] The test case generation unit 301 is used to obtain historical test data and business data of the logistics industry, classify the data of each functional module including order entry, inventory query, and transportation route planning according to different types or different working modes using a machine learning algorithm, and train to obtain test cases for different working types or different working modes in different functional modules. The test cases include expected test results;

[0104] The test execution unit 302 is used to retrieve and execute the test cases according to the test scenarios of different working types or different working modes in different functional modules, including separately testing the test scenarios of different working types or different working modes in different functional modules in the first test stage. If the test result of a certain working mode in the first test stage does not meet the expectation, the test enters the second test stage, and the test scenario whose test result in the first stage does not meet the expectation is repeatedly tested by increasing the test cases and test frequency in the second test stage;

[0105] The test result output unit 303 is used to display the test scenario operation status and abnormal alarms in real time through the trend panel at each stage of the test, and to parse the test results to generate a visual test report with a graphical interface structure.

[0106] Based on the above-mentioned automated testing methods for logistics systems, such as Figure 4 As shown in the diagram, this embodiment of the invention also provides a structural schematic of an automated testing device for a logistics system. This device includes a processor 41 and a memory 42 coupled to the processor 41. The memory 42 stores a computer program, which, when executed by the processor 41, causes the processor 41 to perform the automated testing method for the logistics system described in the above embodiment.

[0107] For other details regarding the implementation of the above technical solution by the processor 41 in the above-mentioned automated testing equipment for logistics systems, please refer to the description in the automated testing method for logistics systems provided in the above-mentioned embodiments of the invention, which will not be repeated here.

[0108] The processor 41 can also be called a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip with signal processing capabilities. The processor 41 may also be a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor may be a microprocessor, or the processor 41 may be any conventional processor.

[0109] like Figure 5 As shown in the diagram, this embodiment of the invention also provides a schematic diagram of a computer-readable storage medium, on which a readable computer program 51 is stored. The computer program 51 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in various embodiments of the invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks or optical disks, ROM (Read-Only Memory), RAM (Random Access Memory), or terminal devices such as computers, servers, mobile phones, and tablets.

[0110] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiment is merely a logical function division, and there can be another division manner for actual implementation, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different modules can be indirect couplings or communication connections through some interfaces, devices or modules, and can be electrical, mechanical or in other forms.

[0111] The modules described as separated components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place, or can be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purposes of the embodiments.

[0112] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can be physically present alone, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can be stored in a computer readable storage medium.

[0113] In the above embodiments, all or part of the embodiments can be realized by software, hardware, firmware or any combination thereof. When realized by software, all or part of the embodiments can be realized in the form of a computer program product.

[0114] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be stored by the computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

[0115] The above describes the technical solutions provided by the present application in detail. The principles and implementation manners of the present application are described by applying specific examples. The above examples are only used to help understand the method and core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed; therefore, the content of the specification should not be understood as a limitation of the present application.

[0116] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0117] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices, and computer program products of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions described in the flowcharts and / or block diagrams. The computer program instructions can also be stored in a computer readable storage medium that can guide the computer to work, so that the computer can read and execute the computer program instructions stored in the computer readable storage medium, and generate a means for implementing the functions described in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 an apparatus for performing the functions specified in the flowchart or multiple flows and / or blocks.

[0118] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart or multiple flows and / or blocks. Figure 1 one or more processes and / or blocks Figure 1 an apparatus for performing the functions specified in the flowchart or multiple flows and / or blocks.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the flowchart or multiple flows and / or blocks. Figure 1 one or more processes and / or blocks Figure 1 an apparatus for performing the functions specified in the flowchart or multiple flows and / or blocks.

[0120] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the application, the application can be practiced otherwise than as specifically set forth herein. With this in mind, the application includes all modifications and variations as come within the scope of the following claims and their equivalents.

Claims

1. A method of automated testing of a logistics system, characterized by, The method comprises the following steps: acquiring historical test data and business data of the logistics industry, classifying data of each functional module including order entry, inventory query, and transportation route planning according to different types or different working modes using a machine learning algorithm, training test cases of different working types or different working modes in different functional modules, and including expected test results in the test cases; calling corresponding test cases according to test scenarios of different working types or different working modes in different functional modules and executing tests, including separately testing test scenarios of different working types or different working modes in different functional modules in a first test stage; if the test result of a certain working mode in the first test stage does not meet the expectation, entering a second test stage, and repeatedly testing the test scenario whose test result in the first stage does not meet the expectation by increasing test cases and test frequency in the second test stage; in each test stage, a trend big board real-time monitoring panel is used to display the running state of the test scenario and an abnormal alarm, and the test result is analyzed to generate a visual test report with a graphical interface structure; the method further comprises a third test stage after the test result in the second test stage meets the expectation, and the test in the third test stage comprises the following steps: generating business data of different frequencies and types including order peak period and emergency through a data simulator; and / or generating data for real-time interaction between each functional module including the logistics equipment and the monitoring system, the transportation vehicle and the dispatch center; and / or generating interaction data including the logistics system and the warehouse management system, the transportation management system, and the order management system; and / or generating an integrated test environment, connecting each subsystem to generate interaction data under actual business scenarios; classifying data of each functional module including order entry, inventory query, and transportation route planning according to different types or different working modes using a machine learning algorithm, training test cases of different working types or different working modes in different functional modules, and calling corresponding test cases according to test scenarios of different working types or different working modes in different functional modules and executing tests; the method further comprises suite testing according to actual test scenarios, which comprises the following steps: acquiring function modules associated in business, encapsulating suite combinations according to operation steps and logical relationships of each business link for function modules with an association relationship; calling test cases of the associated function modules and placing them in the same container, selecting and configuring test suites according to business logic, and configuring the execution order of the test cases; executing tests according to the execution order of the test cases configured in the test suites and recording the execution results.

2. The logistics system automation testing method of claim 1, wherein: The method further comprises according to changes in application scenarios, recombining and encapsulating suites according to business logic or adjusting test cases by adding, removing, or changing the order, executing tests, and recording the execution results.

3. The logistics system automation testing method of claim 2, wherein: The historical test data and business data of the logistics industry are acquired, machine learning algorithms are used to classify the data of each functional module including order entry, inventory query, and transportation route planning according to different types or different working modes, and test cases for different working types or different working modes in different functional modules are trained, wherein the test cases include expected test results. The historical test data and business data of the logistics industry are acquired. User operation test scripts of each functional module are acquired. The acquired historical test data and business data of the logistics industry are loaded into the user operation test scripts of each functional module of the logistics system recorded by the recording tool according to different business types or working modes in each functional module of the logistics system, and test cases under different business types or different working modes of different functional modules are formed.

4. The logistics system automation testing method of claim 3, wherein: The acquired historical test data and business data of the logistics industry are loaded into the user operation test scripts of each functional module of the logistics system recorded by the recording tool according to different business types or working modes in each functional module of the logistics system, and test cases under different business types or different working modes of different functional modules are formed. The keywords of the functional modules that need to be tested are extracted according to the test logic. Test cases under different business types or different working modes of different functional modules are generated by calling the test scripts of each functional module through the keywords, combined with the historical test data and business data of the logistics industry. When a single functional module needs to be tested for one or several business types, the test cases for the business type or working mode are directly called through the keywords for separate testing. When a suite function needs to be tested, the corresponding functional modules and the specified business types or working modes under each functional module are called according to the logical sequence of the keywords, the execution order of the test cases is sorted according to the logical sequence of the keywords, the suite product function test is performed according to the execution order of the arranged test cases, and the test cases with priority are executed according to the priority order.

5. The logistics system automation testing method of claim 1, wherein: The test results are analyzed, and a visual test report with a graphical interface structure is generated, which includes an operable window with search and filtering functions, a multi-factor visual list including report name, task progress, execution type, execution case situation, running result, execution result or error information, successful case, system corresponding time, execution time consumption, and execution time, and a column chart showing the test case execution pass rate of different functional modules, a trend chart with different colors showing the change trend of system response time, and detailed error information in table form.

6. A logistics system automation testing apparatus characterized by comprising: The test case generation unit is configured to acquire the historical test data and business data of the logistics industry, use machine learning algorithms to classify the data of each functional module including order entry, inventory query, and transportation route planning according to different types or different working modes, and train test cases for different working types or different working modes in different functional modules, wherein the test cases include expected test results. ​ The test execution unit is configured to call and execute test cases according to test scenarios of different work types or different work modes in different functional modules, including separate testing of test scenarios of different work types or different work modes in different functional modules in a first test stage; if the test result of a work mode in the first test stage does not meet the expectation, the test enters a second test stage, in which the test scenario whose test result in the first stage does not meet the expectation is repeatedly tested by increasing test cases and test frequency; The test execution unit is further configured to perform a third test stage after the test result in the second test stage meets the expectation, and the test in the third test stage includes: generating business data of different frequencies and types including order peak period and emergency by a data simulator; and / or generating data of real-time interaction between each functional module including the logistics equipment and the monitoring system, the transportation vehicle and the dispatch center; and / or generating interaction data of the logistics system and the warehouse management system, the transportation management system and the order management system; and / or generating an integrated test environment to connect each subsystem to generate interaction data in an actual business scenario; using a machine learning algorithm to classify data of each functional module including order entry, inventory query and transportation route planning according to different types or different work modes, training to obtain test cases of different work types or different work modes in different functional modules, and calling and executing the test cases according to test scenarios of different work types or different work modes in different functional modules. The test execution unit is further configured to perform suite testing according to actual test scenarios, and the suite testing includes: obtaining function modules associated in business, encapsulating suite combinations of function modules with association according to operation steps and logical relationships of each business link; calling test cases of associated function modules and putting them into the same container, selecting and configuring test suites according to business logic, and configuring the execution order of test cases; executing tests according to the execution order of test cases configured in the test suite and recording the execution results; The test result output unit is configured to display the test scenario running state and abnormal alarm through a trend dashboard real-time monitoring panel and analyze the test result to generate a visual test report with a graphical interface structure in each test stage.

7. A computer device, comprising: The computer program is adapted to be loaded and executed by the processor to perform the method steps of any one of claims 1-5. The computer storage medium stores a plurality of instructions, and the instructions are adapted to be loaded and executed by the processor to perform the method steps of any one of claims 1-5.

8. A computer storage medium, characterized in that, ​

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