Automatic test method and system for information system, electronic equipment and medium

Abstract

The invention provides an automatic test method and system for an information system, electronic equipment and a medium. The method comprises the following steps: acquiring a menu list based on a user menu permission; traversing pages corresponding to all menus in the menu list, and identifying control information in the pages; adding a page containing the to-be-measured control into a to-be-traversed page queue; traversing the to-be-traversed page queue, identifying a to-be-tested control of the to-be-traversed page queue, executing a corresponding test operation, and obtaining tested operation data; and performing multi-dimensional anomaly judgment on the test result based on a preset rule base and the operation information. According to the invention, the efficiency and quality of conventional page regression testing can be improved.

Description

Technical Field

[0001] This invention relates to the field of system testing technology, and in particular to an automated testing method, system, electronic device, and medium for information systems. Background Technology

[0002] With the accelerated digitalization of water conservancy infrastructure, water conservancy construction and management web systems need to manage massive amounts of engineering data, of which basic data operations (CRUD operations) account for over 60%, and their accuracy directly affects the quality of engineering management. Currently, the industry faces three major challenges: 1. Frequent changes in requirements and high maintenance costs of VS scripts: Dynamic adjustments to the menu structure cause traditional automation scripts to fail; 2. Tight delivery schedule vs. strict quality standards: Manual testing is difficult to cover all menu paths; 3. Cross-project reuse requirements vs. test asset silos: Test solutions cannot be reused between different systems.

[0003] Existing solutions (such as fixed script recording and manually constructed test data) have shortcomings such as poor dynamic adaptability, single anomaly detection dimensions, and lack of business chain verification, which cannot meet the quality assurance requirements of continuous delivery scenarios. Summary of the Invention This invention provides an automated testing method, system, electronic device, and medium for information systems to improve the efficiency and quality of conventional page regression testing.

[0004] According to one aspect of the present invention, an automated testing method for an information system is provided, comprising: Retrieve the menu list based on user menu permissions; Iterate through all pages corresponding to the menus in the menu list and identify the control information on the pages; Add the page containing the control to be tested to the queue of pages to be traversed; Traverse the queue of pages to be traversed, identify the controls to be tested in the queue of pages to be traversed, execute the corresponding test operations, and obtain the running data after the test; The test results are analyzed using a multi-dimensional anomaly assessment based on a pre-defined rule base and runtime information.

[0005] Optionally, obtaining the menu list based on user menu permissions includes: All menu data is retrieved based on user menu permissions, and a list of menus that conform to the preset menu hierarchy is selected from the menu data.

[0006] Optionally, before adding the page containing the control to be tested to the queue of pages to be traversed, the method further includes: The name of the control to be tested is expanded using keywords, and synonyms of the name of the control to be tested are categorized into the same type of control.

[0007] Optionally, traversing the queue of pages to be traversed and identifying the controls to be tested in the queue of pages to be traversed includes: A large model is used to identify the controls in the queue of pages to be traversed, and the controls are cleaned, classified, and sorted.

[0008] Optionally, the step of using a large model to identify the controls in the queue of pages to be traversed, and cleaning, classifying, and sorting the controls, includes: Open the pages in the queue of pages to be traversed one by one, and take screenshots of the pages; The large model is used to identify control information on the page, obtain the information of the control to be tested, remove duplicate control to be tested, and classify the obtained control to be tested according to the control type. The controls to be tested are sorted according to a preset order.

[0009] Optionally, performing the corresponding test operations and obtaining the test data includes: The controls to be tested in the queue of pages to be traversed are clicked in a preset order, and the running data of the execution process is obtained and saved. The running data includes at least control operation running information, page screenshots, page interface return information, and console information.

[0010] Optionally, the step of performing multi-dimensional anomaly determination on the test results based on a preset rule base and operational information includes: Based on a preset rule base and the control operation information, page screenshots, page interface return information, and console information obtained after testing each control under test, multi-dimensional anomaly determination is performed on each control under test; the multi-dimensional anomaly determination includes data change verification, interface error checking, and pop-up error checking.

[0011] According to another aspect of the present invention, an automated testing system for information systems is provided, comprising: The menu retrieval module is used to retrieve the menu list based on the user's menu permissions. The page traversal module is used to traverse all pages corresponding to all menus in the menu list and identify control information on the page; The queue generation module is used to add pages containing controls to be tested to the queue of pages to be traversed. The testing module is used to traverse the queue of pages to be traversed, identify the controls to be tested in the queue of pages to be traversed, execute the corresponding test operations, and obtain the running data after the test. The anomaly detection module is used to perform multi-dimensional anomaly detection on test results based on a preset rule base and runtime information. According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the information system automated testing method according to any embodiment of the present invention.

[0012] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the information system automated testing method according to any embodiment of the present invention.

[0013] The technical solution of this invention obtains a menu list based on user menu permissions, traverses all pages corresponding to menus in the menu list, identifies control information on the pages, and adds pages containing controls to be tested to a queue of pages to be traversed, thereby reducing the number of test pages and improving testing efficiency. By traversing the queue of pages to be traversed, identifying the controls to be tested in the queue, executing corresponding test operations, and obtaining post-test running data, the test results are subjected to multi-dimensional anomaly judgment based on a preset rule base and running information. Thus, the program executes operations automatically and then uses AI to perform multi-dimensional anomaly judgment, thereby improving the efficiency and quality of conventional page regression testing.

[0014] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of an automated testing method for an information system provided according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of an automated testing method for an information system according to Embodiment 2 of the present invention; Figure 3 This is a flowchart of an automated testing system for an information system provided according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements an automated testing method for an information system according to an embodiment of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] Example 1 Figure 1 This is a flowchart illustrating an automated testing method for an information system, as provided in Embodiment 1 of the present invention. Figure 1 As shown, the method includes: S101. Obtain the menu list based on user menu permissions.

[0020] The information system can be accessed through a headless browser. After logging into the information system by entering user information, users can access the menus and interfaces within the information system according to their permissions. The menu list can be obtained from the information returned by the interfaces by capturing the list of page interfaces.

[0021] S102. Traverse all pages corresponding to all menus in the menu list and identify the control information on the pages.

[0022] Given the menu list, we can iterate through all the pages corresponding to the menus, parse the control information on the pages, and identify the controls contained within the pages. These controls include types such as button, div, a, and span.

[0023] In addition, AI can be used to help identify control information on the page to ensure the speed and accuracy of control recognition.

[0024] S103. Add the page containing the control to be tested to the queue of pages to be traversed.

[0025] The controls to be tested include query / add / edit / delete controls. In this embodiment, pages containing query / add / edit / delete controls can be added to the queue of pages to be traversed for subsequent testing.

[0026] S104. Traverse the queue of pages to be traversed, identify the controls to be tested in the queue of pages to be traversed, perform the corresponding test operations, and obtain the running data after the test.

[0027] In this embodiment, the queue of pages to be traversed can be sequentially iterated. The query / add / edit / delete controls on each page can be identified, and the controls to be tested can be clicked in a preset order. System operation data after clicking the controls can be obtained. This operation data can be saved for later use in identifying anomalies and obtaining test results. The operation data may include page screenshots, page API return information, and page form change information.

[0028] S105. Based on the preset rule base and operation information, perform multi-dimensional anomaly judgment on the test results. The preset rule base in this embodiment may include control recognition rules, operation verification rules, etc. For example, adding a control will add a record, deleting a control will remove a record, the result of editing a control's edit operation field will be compared with the content of viewing a control's view field, and the number of query results for a control will be greater than 1. The runtime information includes the number of data records before and after the operation, page screenshots, interface responses, and console information. The severity level is defined according to the order in which the anomalies occur; for example, a discrepancy in the number of data records after the operation is a serious anomaly, an anomaly pop-up in the page screenshot is a high-level anomaly, and only an anomaly in the console is a minor anomaly.

[0029] The technical solution of this invention obtains a menu list based on user menu permissions, traverses all pages corresponding to menus in the menu list, identifies control information on the pages, and adds pages containing controls to be tested to a queue of pages to be traversed, thereby reducing the number of test pages and improving testing efficiency. By traversing the queue of pages to be traversed, identifying the controls to be tested in the queue, executing corresponding test operations, and obtaining post-test running data, the test results are subjected to multi-dimensional anomaly judgment based on a preset rule base and running information. Thus, the program executes operations automatically and then uses AI to perform multi-dimensional anomaly judgment, thereby improving the efficiency and quality of conventional page regression testing.

[0030] Example 2 Figure 2 This is a flowchart illustrating an automated testing method for an information system provided in Embodiment 2 of the present invention. Figure 1 As shown, the method includes: S201. Obtain all menu data based on user menu permissions, and select a menu list that conforms to the preset menu level range from the menu data.

[0031] A headless browser can be launched using WebDriver to access the system. After successful login, the program will automatically enter the user's username / password and click the login button, simulating the human login process and ensuring subsequent access to the system's menus and interfaces. SeleniumWire can be used to capture the page's interface list and retrieve the menu list from the information returned by the ` / api / menus` interface. All system menus (such as "User Management," "Order List," and "Product Configuration") are typically returned through the ` / api / menus` interface (e.g., the returned data is `[{name: "User Management", path: " / user"}, {name: "Order List", path: " / order"}]`). The program will find this menu interface from the monitored interfaces and extract the "name" and "access address" of all menus.

[0032] In this embodiment, based on a preset menu hierarchy range, menus within that range are further filtered from the previously saved menu list to form an optimized menu list, avoiding blindly traversing all menus. The preset menu hierarchy range can include: Level 1 menus (top-level menus): only menus with numbers 1 to 2 are selected (numbers start from 1, for example, "Project Preliminary Management" is number 1, and "Project Construction Management" is number 2); Level 2 menus: only sub-menus with numbers 3 to 5 are selected; Default rule: if it is a Level 3 menu (the system generally does not exceed 3 levels), all are parsed (no additional configuration required). The final menu list to be traversed is then obtained.

[0033] S202. Traverse all pages corresponding to the menus in the menu list and identify the control information on the pages.

[0034] WebDriver can be used to automatically find and click the corresponding menu control based on the information in the menu list, until the bottom-level "leaf menu" is opened. The supported menu control types are: div, span, and a. The operation logic is as follows: For example, to open "Safety Supervision and Inspection", the program will first find the first-level menu "Project Construction Management" and click it → then find the second-level menu "Engineering Safety Supervision" below it and click it → finally find the third-level menu "Safety Supervision and Inspection" and click it, until the target page is entered.

[0035] The page contains control information, which is then parsed to identify all control information.

[0036] S203. Expand the name of the control to be tested using keywords, and classify the synonyms of the name of the control to be tested into the same type of control.

[0037] In this embodiment, the text of controls may differ for different projects (for example, some projects may be called "Add" instead of "Add New," or "Remove" instead of "Delete"). The program reserves a "keyword list" for expanding the control names with keywords, and new keywords can be added (for example, adding "Add" to the matching list of "Add New") to adapt to different projects.

[0038] S204. Add the page containing the control to be tested to the queue of pages to be traversed.

[0039] Finally, all menu information (such as menu name, access address, and control details) for query, add, edit, and delete controls will be saved as JSON / Excel files. Subsequent automated testing will then eliminate the need to manually identify which pages to test; instead, the saved files will be read directly, and the corresponding pages will be tested automatically.

[0040] S205. Use a large model to identify the controls in the queue of pages to be traversed, and clean, classify and sort the controls.

[0041] In one specific implementation, the method includes: sequentially opening the pages in the queue of pages to be traversed, and taking screenshots of the pages; The large model is used to identify control information on the page, obtain the information of the control to be tested, remove duplicate control to be tested, and classify the obtained control to be tested according to the control type. The controls to be tested are sorted according to a preset order.

[0042] In this embodiment, pages in the queue to be traversed can be opened sequentially. Screenshots can be taken first, then AI models can be used to analyze the page content, while OCR text recognition assists in correction. Finally, key page information can be extracted. AI models qwen-vl-max (responsible for analyzing page layout, controls, and data) and qwen-vl-ocr (responsible for recognizing text in images, such as "Add" on buttons or "Name" input boxes in forms) can be used. The recognized content includes forms, list feature data, and operation controls. Forms can include input boxes, dropdown lists, and radio buttons on the page (e.g., "Project Name" input box, "Status" dropdown list); list feature data can include column names ("Serial Number," "Project Name," "Creation Time") and table data; operation controls can include "Query," "Add," "Edit," and "Delete" buttons on the page.

[0043] The recognized information (such as control position, name, and form fields) can be saved to a local file (such as page_info.json), and it supports "periodic updates". If the page does not change, there is no need to call AI recognition again, reducing AI token consumption.

[0044] Additionally, BeautifulSoup can be used to parse and identify control lists. However, in cases where controls are duplicated or feature rules are unclear, the AI ​​large-scale model (qwen-vl-max) is used to output JSON data for validation. Duplicate controls are removed, and controls are categorized and sorted according to their operation type. For example, multiple "add" operations can be combined into one category (adding "new", "reset", "add", etc.) and sorted according to a preset order, such as query, reset, add, edit, view, and delete operations. The preset order can be modified to suit specific project requirements.

[0045] S206. Perform click operations on the controls to be tested in the queue of pages to be traversed in a preset order, and obtain and save the running data of the execution process. The running data includes at least control operation running information, page screenshots, page interface return information, and console information.

[0046] The program can automatically perform test operations on each control in the ordered sequence: For example, the process might be as follows: Testing "Query": Automatically input query criteria (e.g., enter the project name "Test Project"), click the query button, and check if the corresponding data is retrieved; Testing "Add": Automatically click the add button, fill in the form (e.g., enter the project name and responsible person), submit the form, and check if the addition was successful; Testing "Edit": Locate the newly added data, click the edit button, modify a field (e.g., change the responsible person), submit, and check if the update was successful; Testing "Delete": Locate the test data, click the delete button (if there is a secondary confirmation, automatically click "Confirm"), and check if the data has been deleted. During the testing process, all key information will be saved for later troubleshooting.

[0047] The data it stores includes control operation information, page screenshots, page API return information, and console information, specifically including: Page screenshots: Screenshots before, during (e.g., the form page after clicking "Add"), and after the test, providing a clear view of the page status; API return information: Results (success / failure, returned data) returned by the APIs called by the page during the test (e.g., the / api / add API when adding data); Form change information: For example, the original data entered when adding data and the data modified after editing, comparing whether they meet expectations; S207. Based on the preset rule base and the control operation running information, page screenshots, page interface return information and console information obtained after testing each control under test, perform multi-dimensional anomaly judgment on each control under test; the multi-dimensional anomaly judgment includes data change verification, interface error checking and pop-up error checking. The preset rule base in this embodiment may include control recognition rules, operation verification rules, etc. For example, adding a control will add a record, deleting a control will remove a record, the result of editing a control's edit operation field will be compared with the content of viewing a control's view field, and the number of query results for a control will be greater than 1. The runtime information includes the number of data records before and after the operation, page screenshots, interface responses, and console information. The severity level is defined according to the order in which the anomalies occur; for example, a discrepancy in the number of data records after the operation is a serious anomaly, an anomaly pop-up in the page screenshot is a high-level anomaly, and only an anomaly in the console is a minor anomaly.

[0048] Specifically, the first step is to collect the result data corresponding to the control operations to determine if the control operations are correct. For example, for a query operation, you can enter the query information "Project Name" into the query box (e.g., "Project Name = Test Project A"). Then, the page query result list must contain the data "Test Project A"; all fields in the result (e.g., person in charge, creation time) must be consistent with the previously entered query conditions and the original data stored by the system. For a add operation, after clicking add, the total number of data in the page data list must be one more than before (confirming "Add successful"); the latest first data in the list (usually the newly added one) must have all fields (e.g., name, date, check status) consistent with the submitted test data. For an edit operation, you can select an existing data from the list (e.g., the newly added "Test Project A"), click the edit button, randomly select 1-2 fields (e.g., change "Person in Charge" to another random name), and save the changes. After saving, the "Modified Fields" of this data must be consistent with the newly entered content (e.g., the person in charge has indeed changed), while other unmodified fields remain unchanged. For deletion, you can select a data item from the list (such as the recently edited "Test Project A"), click the delete button (if there is a secondary confirmation, it will be automatically confirmed), and if the total number of items in the list is 1 less than before deletion (confirmation "deletion successful"); then search again using the ID or name of this data item. If the result does not contain this record, it means the verification was successful.

[0049] In this embodiment, the control operation information, page screenshots, page interface return information, and console information can be fed into an AI big data model for comprehensive analysis to determine whether an anomaly has occurred. The operation information includes the conditions entered during the query, newly added test data, a comparison of fields before and after editing, and changes in quantity before and after deletion; page screenshots include screenshots before, during, and after the operation (e.g., screenshots of queries with no results, screenshots of forms where adding an item failed); console information includes browser console errors (e.g., JS errors, resource loading failures); and interface return information includes the results (success / failure, error code, error message) returned by the interfaces called during the operation (e.g., adding via / api / add, deleting via / api / delete).

[0050] If the AI ​​detects an anomaly, it will automatically save all relevant information: a screenshot of the anomaly, console error logs, error messages returned by the API, and the steps performed at the time (such as "what data was entered when adding data"). It will also generate AI analysis results: for example, if the anomaly is identified as a database insertion anomaly, the reason might be that the date field format does not meet the API requirements, providing the development team with a direction for troubleshooting.

[0051] S208. Based on the test results obtained from the multi-dimensional anomaly determination, generate a test report.

[0052] Based on the AI ​​analysis results, a visual test report can be generated. This report can include the path to the abnormal page, the steps taken, and related logs. For example, the abnormal page path could be "Project Construction Management → Engineering Safety Supervision → Safety Inspection"; the steps taken could be: 1. Click the "Add" button → 2. Fill out the form → 3. Submit the form; and the related logs could include interface error messages and console error reports. Furthermore, abnormal areas in the test report can be highlighted to emphasize the location of the anomaly.

[0053] The technical solution of this invention reduces the number of test pages and improves testing efficiency by selecting a menu list that conforms to a preset menu hierarchy and expanding the name of the control to be tested with keywords. By using a large model to identify the controls in the queue of pages to be traversed, and cleaning, classifying and sorting the controls, the running information is handed over to AI for multi-dimensional anomaly judgment, thereby improving the efficiency and quality of regular page regression testing.

[0054] Example 3 Figure 3 This is a schematic diagram of the structure of an automated testing system for an information system provided in Embodiment 3 of the present invention. Figure 3 As shown, the system includes: Menu acquisition module 301 is used to acquire a menu list based on user menu permissions; Page traversal module 302 is used to traverse all pages corresponding to all menus in the menu list and identify control information on the page; The queue generation module 303 is used to add the page containing the control to be tested to the queue of pages to be traversed; The test module 304 is used to traverse the queue of pages to be traversed, identify the controls to be tested in the queue of pages to be traversed, perform corresponding test operations, and obtain the running data after the test. The anomaly detection module 305 is used to perform multi-dimensional anomaly detection on test results based on a preset rule base and runtime information. The information system automated testing system provided in this embodiment of the invention can execute the information system automated testing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0055] Example 4 Figure 4A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0056] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0057] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0058] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as an automated testing method for an information system.

[0059] In some embodiments, an automated testing method for an information system may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the automated testing method for an information system described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform an automated testing method for an information system by any other suitable means (e.g., by means of firmware).

[0060] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0061] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0062] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0063] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0064] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0065] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0066] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0067] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An automated testing method for an information system, characterized in that, include: Retrieve the menu list based on user menu permissions; Iterate through all pages corresponding to the menus in the menu list and identify the control information on the pages; Add the page containing the control to be tested to the queue of pages to be traversed; Traverse the queue of pages to be traversed, identify the controls to be tested in the queue of pages to be traversed, perform the corresponding test operations, and obtain the running data after the test; The test results are analyzed using a multi-dimensional anomaly assessment based on a pre-defined rule base and runtime information.

2. The automated testing method for information systems according to claim 1, characterized in that, The process of obtaining the menu list based on user menu permissions includes: All menu data is retrieved based on user menu permissions, and a list of menus that conform to the preset menu hierarchy is selected from the menu data.

3. The automated testing method for information systems according to claim 1, characterized in that, Before adding the page containing the control to be tested to the queue of pages to be traversed, the following steps are also included: The name of the control to be tested is expanded using keywords, and synonyms of the name of the control to be tested are categorized into the same type of control.

4. The automated testing method for information systems according to claim 1, characterized in that, The step of traversing the queue of pages to be traversed and identifying the controls to be tested in the queue of pages to be traversed includes: A large model is used to identify the controls in the queue of pages to be traversed, and the controls are cleaned, classified, and sorted.

5. The automated testing method for information systems according to claim 4, characterized in that, The process of using a large model to identify controls in the queue of pages to be traversed, and then cleaning, classifying, and sorting the controls, includes: Open the pages in the queue of pages to be traversed one by one, and take screenshots of the pages; The large model is used to identify the control information in the page, obtain the control information to be tested, remove duplicate controls to be tested, and classify the obtained controls to be tested according to the control type. The controls to be tested are sorted according to a preset order.

6. The automated testing method for information systems according to claim 5, characterized in that, The execution of the corresponding test operations and the acquisition of post-test running data include: Click operations are performed on the controls to be tested in the queue of pages to be traversed in a preset order, and the running data of the execution process is obtained and saved. The running data includes at least control operation running information, page screenshots, page interface return information, and console information.

7. The automated testing method for information systems according to claim 6, characterized in that, The multi-dimensional anomaly determination of test results based on a preset rule base and operational information includes: Based on a preset rule base and the control operation information, page screenshots, page interface return information, and console information obtained after testing each control under test, multi-dimensional anomaly determination is performed on each control under test; the multi-dimensional anomaly determination includes data change verification, interface error checking, and pop-up error checking.

8. An automated testing system for information systems, characterized in that, include: The menu retrieval module is used to retrieve the menu list based on the user's menu permissions. The page traversal module is used to traverse all pages corresponding to all menus in the menu list and identify control information on the page; The queue generation module is used to add pages containing controls to be tested to the queue of pages to be traversed. The testing module is used to traverse the queue of pages to be traversed, identify the controls to be tested in the queue of pages to be traversed, execute the corresponding test operations, and obtain the running data after the test. The anomaly detection module is used to perform multi-dimensional anomaly detection on test results based on a preset rule base and runtime information.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the information system automated testing method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the information system automated testing method according to any one of claims 1-7.

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