A multi-agent automated test construction method based on natural ecology

Abstract

The application particularly relates to a kind of natural ecology-based multi-agent automated testing system construction methods, comprising: obtaining the system change data corresponding to the measured system, and configuring the corresponding agent according to the system change data Intelligent body Agent;Determine the association between each Agent based on the function of the measured system;According to the function type of the measured system corresponding to each Agent, the association between the related Agents, the Agent of the target task type is constructed Corresponding agent cluster;Based on the agent cluster of the target task type and the Agent of other task types, an automated testing system is constructed.The method associates the growth process of natural ecological system with the structure of automatic testing system, simulates the structure of natural ecological system to construct intelligent automated testing software system according to the self-evolution mechanism of nature, so as to achieve the purpose of intelligently testing the measured system.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to a method for constructing multi-agent automated testing based on natural ecosystems. Background Technology

[0002] In related technologies, there is relatively little research on the construction methods of intelligent automated testing systems in the testing field. There is no unified standard in the industry, and the system construction is largely blind and arbitrary. This often results in the inability to quickly construct a testing system according to the actual situation of the project, causing delays in the construction of the testing system, low testing effectiveness and efficiency, and an inability to quickly construct a system according to the actual situation of the project.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a multi-agent automated testing construction method based on natural ecology, a computer program product, and an electronic device, which can effectively overcome the defects existing in the prior art.

[0005] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0006] According to a first aspect of the present invention, a method for constructing a multi-agent automated testing system based on natural ecology is provided, the method comprising: Acquire system change data corresponding to the system under test, and configure the corresponding intelligent agent based on the system change data; wherein, the intelligent agent is used to configure the functional testing of the system under test; Determine the relationships between each Agent based on the functions of the system under test; Based on the functional type of the system under test corresponding to each Agent and the relationship between related Agents, construct corresponding intelligent agent clusters for Agents of the target task type; An automated testing system is constructed based on a cluster of intelligent agents of the target task type and agents of other task types; wherein, the automated testing system is used to test the system under test.

[0007] In some exemplary embodiments, system change data between different versions of the system under test is acquired, and a corresponding intelligent agent is configured based on the system change data, including: Obtain system data corresponding to the current version of the system under test and the historical versions of the target system, and compare the system data; The system change data is determined based on the system change information in the system data comparison results; wherein, the system change data includes at least one type of change data in system structure, graphical user interface, and interface protocol; Based on the system change data of each type of system, determine the system function change information of the system under test, and configure the corresponding Agent according to the system function change information.

[0008] In some exemplary implementations, configuring a corresponding Agent based on system function change information includes: The Agent management module obtains the system function change information and processes the Agent corresponding to the system function according to the type of system function change. The Agent processing includes: adding a new Agent for newly added system functions, and deleting the existing Agent for deleted system functions.

[0009] In some exemplary embodiments, determining the association between agents based on the functionality of the system under test includes: The system under test is decomposed into tasks to obtain the sub-tasks that implement the preset functions of the system under test, as well as the relationships between the related sub-tasks. Based on the relationships between each subtask and related subtasks, configure the relationships between the Agents corresponding to each subtask; wherein, any function of the system under test corresponds to at least one Agent.

[0010] In some exemplary embodiments, the method further includes: Configure the corresponding Agent according to the preset test items of the automated testing system, and establish the association between the Agent and the system under test.

[0011] In some exemplary embodiments, the method further includes: Configure the corresponding test cases and test rule data for the system under test; The test cases and test rule data are executed using Agents of various test types in the automated testing system to obtain the system test data corresponding to the system under test. The test report agent is used to parse the system test data and generate corresponding test result analysis data.

[0012] In some exemplary embodiments, the method further includes: Based on the relationship between each Agent and the central control node, as well as the relationship between different Agents, a communication topology network is constructed. Based on the communication topology network, the first agent uploads the first negotiation information to the central control node; wherein, the first negotiation information is used to process the first test task; The central control node parses the first negotiation information and sends it to the second agent; wherein the second agent is used to jointly process the first test task with the first agent.

[0013] In some exemplary embodiments, the method further includes: Establish an association between the agent node and the agent management module, and at least one target agent, for the purpose of managing the at least one target agent through the agent node.

[0014] According to a second aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the above-described method for constructing a multi-agent automated testing system based on natural ecology.

[0015] According to a third aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described method for constructing a multi-agent automated testing system based on natural ecology.

[0016] According to a fourth aspect of the present invention, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to implement the above-described method for constructing a multi-agent automated testing system based on natural ecology by executing the executable instructions.

[0017] The present invention provides a method for constructing a multi-agent automated testing system based on natural ecosystems. This method acquires system change data of the system under test and configures corresponding intelligent agents based on this data. It then automatically constructs an automated testing system based on the agent cluster and the agents, and uses this system to perform system testing on the system under test. This achieves the goal of constructing an intelligent automated testing software system based on a self-evolutionary mechanism, and intelligently performing various tests on the system under test. This method links the growth and change process of a natural ecosystem with the structure of the automated testing system. Based on the self-evolutionary mechanism of nature, it simulates the structure of a natural ecosystem to construct an intelligent automated testing software system, thereby achieving the goal of intelligently performing various tests on the system under test. This results in the efficient and rapid construction of a testing system.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] Figure 1 The illustration shows a schematic diagram of an exemplary embodiment of the present invention, which describes a method for constructing a multi-agent automated testing system based on natural ecology. Figure 2 The diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0022] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0023] To address the shortcomings and deficiencies of existing technologies, this example implementation provides a method for constructing a multi-agent automated testing system based on a natural ecosystem. This method can be applied to automatically create software testing systems for the software system under test. (Reference) Figure 1 As shown, the method includes: Step S11: Obtain system change data corresponding to the system under test, and configure the corresponding intelligent agent according to the system change data; wherein, the intelligent agent is used to configure the functional test of the system under test; Step S12: Determine the relationships between each Agent based on the functions of the system under test; Step S13: Based on the functional type of the system under test corresponding to each Agent and the relationship between related Agents, construct the corresponding intelligent agent cluster for the Agent of the target task type; Step S14: Construct an automated testing system based on the intelligent agent cluster of the target task type and agents of other task types; wherein, the automated testing system is used to test the system under test.

[0024] The following will describe in more detail each step of the construction method of the multi-agent automated testing system based on natural ecology in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0025] In step S11, system change data corresponding to the system under test is obtained, and the corresponding intelligent agent is configured according to the system change data; wherein, the intelligent agent is used to configure the functional test of the system under test.

[0026] For example, the above method can be applied to smart terminal devices. On the user-side smart terminal device, the user can first select the software system to be tested in the interactive interface, configure it as the software under test, and automatically create a corresponding automated test system build task for the software under test. The user-side smart terminal device can upload the automated test system build task to the server, where the server executes the task to generate the corresponding automated test system and then sends the automated test system back to the user-side smart terminal device. Alternatively, the task can be executed locally on the user-side smart terminal device to generate the corresponding automated test system.

[0027] For example, step S11 described above may specifically include: Step S21: Obtain system data corresponding to the current version of the system under test and the target historical version, and compare the system data; Step S22: Determine system change data based on system change information in the system data comparison results; wherein, system change data includes at least one type of change data among system structure, graphical user interface, and interface protocol; Step S23: Determine the system function change information of the tested system based on the system change data of each type, and configure the corresponding Agent according to the system function change information.

[0028] Specifically, when executing the automated test system creation task, the current version of the software system under test and at least one historical version can be determined based on user input information, and used as the target historical version. Alternatively, when executing the automated test system creation task, the corresponding current version and historical version can be queried from the database based on the system identification information of the system under test. For example, the system identification information can be the system name, system code, system version identifier, etc.

[0029] The database can store system data for different versions of the system under test. This system data includes system topology data (e.g., topology diagram); graphical user interfaces for each function and page level, along with corresponding descriptions; descriptions of interfaces and protocols; system function update documentation; system user manuals, etc.

[0030] When building an automated test system for the system under test, you can select the latest version and one or more historical versions for system data comparison. When selecting historical versions, you can choose a specific historical version based on the current testing requirements.

[0031] By comparing the data of each type of system data mentioned above, it is possible to determine the changes in the system topology, added or deleted system functions, function implementation methods, and changes in the graphical user interface, interfaces and / or protocols of the tested system, etc. Among them, the interface can be the data interaction interface between different functional modules within the tested system, as well as the communication interface between the system and the external environment.

[0032] After determining the system change data, corresponding system function change information can be generated. This information may include the specific function that has changed, along with its description. Furthermore, based on the specific changed system function, a corresponding agent is configured for each changed function.

[0033] The Agent, implemented using the LangChain framework, is an AI Agent capable of autonomously using tools, calling APIs, planning steps, and executing tasks. Generally, an Agent comprises several core components: perception, decision-making, execution, goals or system tasks, feedback, and learning. Perception is the core function, indicating the Agent's ability to acquire information from the environment, such as reading sensor data, receiving user input, and acquiring API data. Decision-making (or Planning) allows the Agent to assess the current situation based on perceived information and decide on the next step. Execution involves carrying out the actions made in the decision, such as calling APIs or other Agents, or sending messages. Goals or tasks are configured with specific objectives, such as performing test tasks. Feedback and learning indicate the Agent possesses a self-feedback mechanism, enabling it to continuously optimize its behavioral strategies through reinforcement learning.

[0034] For example, the above-mentioned configuration of the corresponding Agent based on system function change information includes: the Agent management module obtains the system function change information and processes the Agent corresponding to the system function according to the system function change type; wherein, processing the Agent includes: adding a new Agent for the newly added system function and deleting the existing Agent corresponding to the deleted system function.

[0035] Specifically, an Agent management module can be provided to read and process system change data. When it is determined from the system change data that the system function of the system under test has changed, if it is determined that a new system function has been added, a corresponding Agent can be configured for the new function; or, if it is determined that a deleted system function has been added to the system under test, the Agent corresponding to that function can be deleted.

[0036] In step S12, the relationships between each Agent are determined based on the functions of the system under test.

[0037] For example, step S12 described above may specifically include: Step S31: Perform task decomposition processing on the system under test to obtain each sub-task corresponding to the implementation of the preset function under test, as well as the relationship between the related sub-tasks. Step S32: Based on the relationships between each subtask and related subtasks, configure the relationships between the Agents corresponding to each subtask; wherein, any function of the system under test corresponds to at least one Agent.

[0038] Specifically, a task decomposition module can be provided to perform task decomposition processing on the system under test. Specifically, for the current version of the system under test, based on the specific functions that the business system can currently implement, the implementation of each function can be decomposed into tasks, determining the sub-tasks corresponding to each function implementation, and the relationships between these sub-tasks. These relationships can include calling relationships, execution order relationships, etc.

[0039] Based on the Agents corresponding to each system function of the system under test, the subtasks corresponding to each function, and the relationships between the subtasks, the mapping relationship between Agents and subtasks, as well as the relationship between Agents corresponding to different subtasks, can be configured. Each function of the system under test corresponds to one or more Agents.

[0040] For example, when a business function of the system under test contains only one subtask, a corresponding Agent is configured, and this Agent can be used to test that function of the system under test. Alternatively, if a business function of the system under test contains n subtasks, each subtask can be configured with its own corresponding Agent. Furthermore, other Agents can be configured for this business function to test other business functions between the subtasks; for example, configuring a call capability testing Agent. Therefore, each business function can be configured with more than n Agents.

[0041] For example, in the Agent management module, when it detects that the system under test has added new system functions and identifies the corresponding sub-tasks, it can configure a corresponding Agent for each sub-task to complete the Agent's creation, registration, and dispatch. When it detects that the system under test has deleted business functions, it can deregister and delete the corresponding Agent.

[0042] In step S13, based on the functional type of the system under test corresponding to each Agent and the association between related Agents, a corresponding intelligent agent cluster is constructed for the Agents of the target task type.

[0043] For example, for each current Agent, including newly added Agents and existing Agents, an intelligent agent cluster (Multi-Agent-Based Micro AI System, AI MAS) can be created for Agents with the same tested system function type, based on the type of system function they correspond to and the type of test task that the Agent should perform. A small cluster AI MAS can consist of multiple intelligent Agents. Alternatively, the cluster can be divided according to the type of test task corresponding to each Agent.

[0044] Specifically, the intelligent agent cluster can include a System Under Test (SUT) monitoring AI MAS, where each agent can be used to monitor the execution process of various functions of the SUT, collect corresponding detection data, and use it for system testing. The distributed control node (AI MAS) can include multiple agents for testing the execution actions of distributed nodes within the SUT; each distributed node can be configured with a corresponding agent. The UI automation AI Agent can be used to test UI interfaces, such as functional testing of virtual controls, input windows, and audio / video playback windows. The interface testing AI Agent can be used to generate corresponding test cases for data interfaces and perform testing. The element library AI Agent can be configured with a corresponding relationship with the UI automation AI Agent to provide elements used in the UI interface and test each element.

[0045] Specifically, a small cluster of AI MAS composed of multiple intelligent agents constitutes an autonomous intelligent AI system. The AI ​​MAS system can be differentiated, replicated, and nested. The automated testing system can be completed collaboratively by various small MAS clusters and multiple agents. The cooperation of multiple AI MAS or AI Agents provides the system with significant redundancy; if one AI MAS or AI Agent malfunctions, the overall reliability of the system is minimally affected, thus improving system maintainability.

[0046] For example, the method further includes: configuring a corresponding Agent according to the preset test items of the automated testing system, and establishing an association between the Agent and the system under test.

[0047] Specifically, users can pre-configure testing requirements and, based on these requirements, configure the testing functions that the testing system needs to implement, as well as other business functions. Corresponding agents can then be configured for each of these functions. These agents can be used to build an automated testing system.

[0048] For example, this could include a test data construction AI Agent, which can communicate with the agents corresponding to each function of the system under test, obtain the corresponding functional data, and automatically generate corresponding test cases. For instance, different types of test case templates can be pre-configured, and the appropriate test case template can be selected and generated based on the functional data read from other agents. A performance testing AI Agent can be used to configure corresponding performance test data for the system under test and associate it with the test data construction AI Agent to generate corresponding test cases. A test report AI Agent can be used to collect test data corresponding to each function, parse the test data, and generate corresponding test reports.

[0049] In step S14, an automated testing system is constructed based on the intelligent agent cluster of the target functional type and other agents of other functional types; wherein, the automated testing system is used to test the system under test.

[0050] For example, based on the intelligent agent clusters and agents with independent functions constructed in the above steps, an automated testing system for the system under test is constructed, and the automated testing system is used to test the various business functions of the system under test.

[0051] For example, a plant growth simulation system, after business decomposition, can be divided into several subsystems, such as leaf system, branch system, and root system. Corresponding to an automated testing system, these subsystems can be configured as a multi-Agent subsystem, a communication subsystem, and a control subsystem. Each leaf (Agent) is used to sense various sensor information in the testing environment, control various actuators to adapt to environmental changes, process information from its own system environment, collect various test information, record the content and results of each test, and maintain a micro-loop of recording parameters, logs, and screenshots. Communication is maintained through the branch system and the underground root system; that is, the control agent and communication agent complete the control and communication of the testing system. It simulates the automatic adaptation process of a plant to its environment; each agent software in the leaf system automatically learns and grows based on environmental information.

[0052] For example, the method further includes: Step S41: Configure the corresponding test cases and test rule data for the system under test; Step S42: Execute the test cases and test rule data using the Agents of each test type in the automated testing system to obtain the system test data corresponding to the system under test; Step S43: Use the test report agent to parse the system test data and generate corresponding test result analysis data.

[0053] Specifically, the AutoTest automated test model can be configured as follows: AutoTest (D, R, P, A) Where D represents the configuration test data, R represents the test rules, P represents the test execution, and A represents the test result analysis.

[0054] The automated test configuration executed by the Agent program is a process in which the above four elements are executed sequentially. If multiple automated test agents are included, then multiple processes are executed concurrently. The analysis is based on the four elements of the model, mainly including test execution and test result analysis. Test execution mainly involves the three elements of D, R, and P in the model.

[0055] Specifically, when an automated testing system performs system testing on the system under test based on the aforementioned automated testing model, it can first create test data and then perform test execution (Perform) according to test rules. For example, P can be defined as a binary function P(Object, Script) that performs different test behaviors on different test objects, where Object represents different test objects and Script represents different test scripts. For example: querying certain data is DataQuery(Data1, Query), uploading an attachment file is FileUpload(File, Upload), changing an attachment file is FileUpdate(File, Update), deleting an attachment file is FileDelete(File, Delete); writing to disk is DiskWrite(Disk, Write), deleting from disk is DiskDelete(Disk, Delete), outputting data from the network port is NicOut(Nic, Out), and inputting data from the network port is NicIn(NIC, In), etc. Similarly, the test result analysis agent is (Test Analyse). Analyse is defined as a binary function Analyse(Result, Report) that performs analysis on different test results. Result is the different test results, and Report is the test report.

[0056] For example, the method further includes: Step S51: Construct a communication topology network based on the association between each Agent and the central control node, as well as the association between different Agents; Step S52: Based on the communication topology network, the first Agent uploads the first negotiation information to the central control node; wherein, the first negotiation information is used to process the first test task; Step S53: The central control node parses the first negotiation information and sends the first negotiation information to the second agent; wherein the second agent is used to jointly process the first test task with the first agent.

[0057] Specifically, a central control node can be provided, which can establish communication links with each Agent. The implementation of multi-Agent collaboration relies on a flexible communication mechanism between agents. Communication between agents alone is insufficient; other agents in the system are unaware that two agents have negotiated, and their behavioral changes affect the group. Therefore, each agent still needs to communicate with the central control node, which then notifies relevant agents to coordinate.

[0058] Agents with a direct relationship can interact directly with each other, or they can interact through agents. Simultaneously, the interacting data can be sent to the central control node, which then notifies other agents within the architecture. Alternatively, agents without a direct relationship can communicate through the central control node.

[0059] For example, the method further includes: establishing an association between the proxy node and the Agent management module, and at least one target Agent, for managing the at least one target Agent through the proxy node.

[0060] Specifically, to facilitate user management of Agents on different smart terminal devices, proxy nodes can be created and deployed on other smart terminal devices. The association and communication link between the proxy nodes, the Agent management module, and the target Agent can be established, allowing users to control the Agent management module through the proxy nodes on other terminal devices besides the current terminal device, and thus manage and control the specified target Agent.

[0061] For example, a communication module can be provided. Through the interaction between this communication module and the central control node, negotiation and management between the agent control node and different agents can be completed. The central control node can be used to configure node communication between different nodes, and the communication module can be used to implement communication and data exchange between different nodes.

[0062] For example, an Agent management model architecture can be provided, including a task decomposition module, an Agent task scheduling module, a communication module, and an Agent management module, to realize the production, dispatch, replication, registration, and deregistration of Agents.

[0063] The method provided in this invention implements a method for constructing a multi-agent automated testing system based on natural ecosystems. It configures corresponding agents for real-time changes in the system under test and utilizes multiple different agents to perform different testing tasks, thereby constructing a multi-agent automated testing system. Changes in the testing system's functionality are triggered based on changes in the system under test.

[0064] After adding test data to the system under test, the test results are analyzed to obtain data characteristics and distribution features. Based on this information, the proportions of the test results data characteristics are integrated, and a test report of the target system is constructed accordingly.

[0065] This method uses multiple agents for data injection, system test execution, test result data analysis, and test report generation. It is applicable to most software automation testing processes, reducing the burden on testers in data and environment preparation while ensuring the correctness and completeness of the tests, thus effectively improving testing efficiency.

[0066] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0067] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0068] Figure 2 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0069] It should be noted that, Figure 2 The electronic device 1000 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0070] like Figure 2As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage section 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004. Furthermore, the electronic device 1000 also includes an FPGA device and a System-on-a-Chip (SoC) device.

[0071] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0072] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0073] Specifically, the aforementioned electronic devices can be airborne intelligent electronic devices, such as airborne video processing equipment.

[0074] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0076] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0077] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0078] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0079] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0080] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0081] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for constructing a multi-agent automated testing system based on natural ecology, characterized in that, The method includes: Acquire system change data corresponding to the system under test, and configure the corresponding intelligent agent based on the system change data; wherein, the intelligent agent is used to configure the functional testing of the system under test; Determine the relationships between each Agent based on the functions of the system under test; Based on the functional type of the system under test corresponding to each Agent and the relationship between related Agents, construct corresponding intelligent agent clusters for Agents of the target task type; An automated testing system is constructed based on a cluster of intelligent agents of the target task type and agents of other task types; wherein, the automated testing system is used to test the system under test.

2. The method according to claim 1, characterized in that, Acquire system change data corresponding to the system under test, and configure the corresponding intelligent agent based on the system change data, including: Obtain system data corresponding to the current version of the system under test and the historical versions of the target system, and compare the system data; The system change data is determined based on the system change information in the system data comparison results; wherein, the system change data includes at least one type of data from: system structure, graphical user interface, and interface protocol; Based on the system change data of each type of system, determine the system function change information of the system under test, and configure the corresponding Agent according to the system function change information.

3. The method according to claim 2, characterized in that, Configure the corresponding Agent based on the system function change information, including: The Agent management module obtains the system function change information and processes the Agent corresponding to the system function according to the type of system function change. The Agent processing includes: adding a new Agent for newly added system functions, and deleting the existing Agent for deleted system functions.

4. The method according to claim 1, characterized in that, The determination of the relationships between agents based on the functions of the system under test includes: The system under test is decomposed into tasks to obtain the sub-tasks that implement the preset functions of the system under test, as well as the relationships between the related sub-tasks. Based on the relationships between each subtask and related subtasks, configure the relationships between the Agents corresponding to each subtask; wherein, any function of the system under test corresponds to at least one Agent.

5. The method according to claim 1, characterized in that, The method further includes: Configure the corresponding Agent according to the preset test items of the automated testing system, and establish the association between the Agent and the system under test.

6. The method according to claim 1, characterized in that, The method further includes: Configure the corresponding test cases and test rule data for the system under test; The test cases and test rule data are executed using Agents of various test types in the automated testing system to obtain the system test data corresponding to the system under test. The test report agent is used to parse the system test data and generate corresponding test result analysis data.

7. The method according to claim 1, characterized in that, The method further includes: Based on the relationship between each Agent and the central control node, as well as the relationship between different Agents, a communication topology network is constructed. Based on the communication topology network, the first agent uploads the first negotiation information to the central control node; wherein, the first negotiation information is used to process the first test task; The central control node parses the first negotiation information and sends it to the second agent; wherein the second agent is used to jointly process the first test task with the first agent.

8. The method according to claim 1, characterized in that, The method further includes: Establish an association between the agent node and the agent management module, and at least one target agent, for the purpose of managing the at least one target agent through the agent node.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for constructing a multi-agent automated testing system based on natural ecology as described in any one of claims 1 to 8.

10. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method for constructing a multi-agent automated testing system based on natural ecology, as described in any one of claims 1 to 8, by executing the executable instructions.

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