List system testing method, device, equipment and medium based on big data construction

Abstract

The present invention relates to the field of big data and provides a list system testing method, apparatus, device, and medium based on big data construction. The method receives a first data source fed back by a middleware system connected to a list system to be tested and a target system, obtains a second data source from a target interface, and constructs a third data source based on a file structure model and historical test data. The method automatically generates data based on big data for a specified task scenario, thereby reusing test data, making testing more targeted, improving test efficiency, and shortening release time. All data sources are stored in a cache data pool. Due to the large capacity of the cache data pool, system performance is guaranteed during subsequent verification processes. Comparative verification is performed in the cache data pool based on the data structure of the data to be verified, thereby achieving automated testing of message queues, offline files, and interface files. Furthermore, the present invention also relates to blockchain technology, and test results can be stored in blockchain nodes.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a list system testing method, device, equipment and medium based on big data construction. Background Art

[0002] The credit card list system is a platform used in sales and marketing to precisely target customer groups that meet certain criteria (such as those at online application breakpoints, appointment activations, and other task nodes). It also provides a precise, unified, efficient, and intelligent credit card customer list management platform. Due to the numerous data sources and the use of batch processing tasks for list collection, list screening, and final output, the following issues may be encountered during testing:

[0003] 1. Test data mainly relies on upstream systems, which takes too long, affects overall testing efficiency, and is not conducive to the sustainable development of batch scenario verification;

[0004] 2. Lack of preparation of batch data files for business scenarios and user scenarios can easily lead to incomplete test coverage;

[0005] 3. Due to the dependence on upstream external system data, it is difficult to implement automated testing, resulting in low efficiency of system testing and regression testing. Summary of the Invention

[0006] In view of the above, it is necessary to provide a list system testing method, device, equipment and medium based on big data construction, aiming to solve the problems of incomplete coverage and low efficiency of list system testing.

[0007] A method for testing a list system based on big data construction, comprising:

[0008] When a to-be-tested list system is obtained, a smoke test and a system test are performed on the to-be-tested list system to obtain historical test data;

[0009] When a regression test instruction for the system on the to-be-tested list is received, determining a target system that communicates with the system on the to-be-tested list according to the regression test instruction;

[0010] Acquire a middleware system connected to the to-be-tested list system and the target system, send a message request to the middleware system, and receive data fed back by the middleware system in the message request as a first data source;

[0011] calling a target interface of the target system, and acquiring data from the target interface as a second data source based on a configuration protocol;

[0012] Obtaining a pre-configured file structure model, and constructing a third data source according to the file structure model and the historical test data;

[0013] Storing the first data source, the second data source, and the third data source in a cache data pool to obtain data to be verified;

[0014] The data structure of the data to be verified is parsed, and based on the data structure of the data to be verified, a comparison and verification is performed in the cache data pool to obtain a test result.

[0015] According to a preferred embodiment of the present invention, determining the target system communicating with the to-be-tested list system according to the regression test instruction includes:

[0016] Parsing the regression test instruction to obtain information carried by the regression test instruction;

[0017] Obtaining a preset tag, wherein the preset tag corresponds to an address identified by the storage system;

[0018] Establishing a regular expression according to the preset tag;

[0019] traversing the information carried by the regression test instruction using the regular expression, and determining the traversed information that matches the regular expression as the target address;

[0020] Connecting to the target address and obtaining data from the target address to obtain a system identifier;

[0021] The system corresponding to each of the system identifiers is determined as the target system.

[0022] According to a preferred embodiment of the present invention, constructing a third data source according to the file structure model and the historical test data includes:

[0023] Parsing the regression test instructions to obtain a target task scenario;

[0024] Identifying the data structure of the target task scenario using the file structure model to obtain a target structure;

[0025] Create a data reading function according to the target structure;

[0026] Using the data reading function to read at least one target field from the historical test data;

[0027] The target fields are assembled to obtain the third data source.

[0028] According to a preferred embodiment of the present invention, assembling the target field to obtain the third data source includes:

[0029] Determine the position of each target field in the intermediate file according to the target structure;

[0030] splicing the target fields according to their positions in the intermediate files to obtain the intermediate files;

[0031] When the target structure corresponds to a fixed-length field, identifying empty fields in the intermediate file, filling the empty fields, and performing table entry on the intermediate file after the filling process to obtain the third data source; or

[0032] When the target structure corresponds to a non-fixed-length field, the intermediate file is divided by a delimiter, and the divided intermediate file is put into a table to obtain the third data source.

[0033] According to a preferred embodiment of the present invention, the data structure based on the data to be verified is compared and verified in the cache data pool to obtain a test result including:

[0034] Obtaining a message body of a first data source in the data to be verified as a message body to be verified;

[0035] Obtaining a message body from the middleware system as an actual message body;

[0036] Verifying whether the message body to be verified and the actual message body are identical;

[0037] When the message body to be verified is different from the actual message body, determining that the test result is that the first data source in the data to be verified fails the verification; or

[0038] When the message body to be verified is the same as the actual message body, the message body to be verified is sent to the target system. When a response message from the target system is monitored, it is determined that the test result is that the first data source in the data to be verified has passed the verification; or when no response message from the target system is monitored, it is determined that the test result is that the first data source in the data to be verified has failed the verification.

[0039] According to a preferred embodiment of the present invention, performing comparative verification on the data structure based on the data to be verified in the cache data pool to obtain a test result further includes:

[0040] calling the target interface to obtain target data corresponding to the second data source in the data to be verified, and determining that the test result is that the second data source in the data to be verified passes verification when the second data source in the data to be verified is the same as the target data; or determining that the test result is that the second data source in the data to be verified fails verification when the second data source in the data to be verified is different from the target data;

[0041] For the third data source in the data to be verified, obtain a file logic corresponding to the third data source as the target file logic; when the file logic of the third data source is the same as the target file logic, determine the test result as that the third data source passes verification; or when the file logic of the third data source is different from the target file logic, determine the test result as that the third data source fails verification.

[0042] According to a preferred embodiment of the present invention, after obtaining the test results, the method further includes:

[0043] When the test result shows that the to-be-tested list system passes the verification, obtaining user data in the to-be-tested list system;

[0044] Acquiring user behavior information from the target system based on the user data;

[0045] Extracting a text summary of the user behavior information based on the BM25 algorithm;

[0046] Perform word segmentation on the text summary based on the shortest path word segmentation algorithm to obtain an intermediate text;

[0047] Identify entities in the intermediate text based on a hidden Markov algorithm to obtain candidate entities;

[0048] Obtain a pre-built dictionary, and use the dictionary to clean the candidate entities to obtain user labels;

[0049] The user tag is sent to the target system.

[0050] A list system testing device based on big data construction, the list system testing device based on big data construction comprising:

[0051] The testing unit is configured to perform a smoke test and a system test on the system to be tested when the system to be tested is obtained, and obtain historical test data;

[0052] a determining unit configured to, upon receiving a regression test instruction for the system on the to-be-tested list, determine a target system communicating with the system on the to-be-tested list according to the regression test instruction;

[0053] an acquiring unit, configured to acquire a middleware system connected to the to-be-tested list system and the target system, send a message request to the middleware system, and receive data fed back by the middleware system in the message request as a first data source;

[0054] The acquisition unit is further configured to call a target interface of the target system and acquire data from the target interface as a second data source based on a configuration protocol;

[0055] a construction unit, configured to obtain a pre-configured file structure model and construct a third data source according to the file structure model and the historical test data;

[0056] a storage unit, configured to store the first data source, the second data source, and the third data source into a cache data pool to obtain data to be verified;

[0057] The verification unit is used to parse the data structure of the data to be verified, and perform comparative verification in the cache data pool based on the data structure of the data to be verified to obtain a test result.

[0058] A computer device, comprising:

[0059] a memory storing at least one instruction; and

[0060] The processor executes the instructions stored in the memory to implement the list system testing method based on big data construction.

[0061] A computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the list system testing method based on big data construction.

[0062] It can be seen from the above technical solutions that the present invention can, when obtaining a list system to be tested, perform a smoke test and a system test on the list system to be tested to obtain historical test data; when receiving a regression test instruction for the list system to be tested, determine the target system communicating with the list system to be tested according to the regression test instruction, obtain a middleware system connected to the list system to be tested and the target system, send a message request to the middleware system, and receive the data fed back by the middleware system to the message request as a first data source, call the target interface of the target system, and obtain data from the target interface as a second data source based on a configuration protocol, obtain a pre-configured file structure model, and according to the file structure model and the historical data. The third data source is constructed based on the historical test data, and big data is automatically generated for the specified task scenario, thereby realizing the reuse of test data, making the test more targeted, simplifying the test process, improving the test efficiency, and thus shortening the release time. The first data source, the second data source, and the third data source are stored in the cache data pool to obtain the data to be verified, which can make full use of the large capacity of the cache data pool, thereby ensuring the performance of the system in the subsequent verification process and avoiding affecting the normal operation of the system. The data structure of the data to be verified is parsed, and based on the data structure of the data to be verified, comparative verification is performed in the cache data pool to obtain the test results, thereby realizing automated testing of message queues, offline files, and interface files. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of a preferred embodiment of the list system testing method based on big data construction of the present invention.

[0064] Figure 2 It is a functional module diagram of a preferred embodiment of the list system testing device based on big data construction of the present invention.

[0065] Figure 3 It is a structural diagram of a computer device of a preferred embodiment of the present invention for implementing a list system testing method based on big data construction. DETAILED DESCRIPTION

[0066] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] like Figure 1 FIG. 1 is a flow chart of a preferred embodiment of a list system testing method based on big data construction according to the present invention. The order of the steps in the flow chart can be changed and some steps can be omitted according to different requirements.

[0068] The list system testing method based on big data construction is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0069] The computer device may be any electronic product that can interact with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an interactive network television (IPTV), a smart wearable device, etc.

[0070] The computer device may also include a network device and / or a user device, wherein the network device includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0071] The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0072] Among them, Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0073] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0074] The network where the computer device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0075] S10, when the system on the to-be-tested list is obtained, a smoke test and a system test are performed on the system on the to-be-tested list to obtain historical test data.

[0076] In this embodiment, the overall automated verification can be based on the TestNG framework, connecting various test processes in series. At the same time, various subtasks in the test process (such as big data construction, etc.) can be encapsulated in program blocks for easy calling, thereby improving test efficiency.

[0077] In this embodiment, the to-be-tested list system refers to a list system of customers that need to be tested.

[0078] In this embodiment, the smoke test refers to a process of verifying the changed code before embedding the changed code into the to-be-tested list system.

[0079] In this embodiment, the system test refers to a test of the entire system of the to-be-tested list system to check whether there is any abnormality in the to-be-tested list system.

[0080] It should be noted that when the smoke test and the system test are performed on the to-be-tested list system, all pre-configured test processes need to be executed. Therefore, this embodiment retains the complete test data during the test process as the historical test data.

[0081] S11 , when a regression test instruction for the system on the to-be-tested list is received, determining a target system that communicates with the system on the to-be-tested list according to the regression test instruction.

[0082] In this embodiment, the regression test instruction may be triggered by relevant staff, such as testers, developers, etc.

[0083] In at least one embodiment of the present invention, determining the target system communicating with the to-be-tested list system according to the regression test instruction includes:

[0084] Parsing the regression test instruction to obtain information carried by the regression test instruction;

[0085] Obtaining a preset tag, wherein the preset tag corresponds to an address identified by the storage system;

[0086] Establishing a regular expression according to the preset tag;

[0087] traversing the information carried by the regression test instruction using the regular expression, and determining the traversed information that matches the regular expression as the target address;

[0088] Connecting to the target address and obtaining data from the target address to obtain a system identifier;

[0089] The system corresponding to each of the system identifiers is determined as the target system.

[0090] The preset tags can be customized.

[0091] For example, the preset tag may be configured as ad.

[0092] Furthermore, the regular expression established according to the preset tag may be in the form of ad(), indicating obtaining data with the ad tag.

[0093] Furthermore, the regular expression ad() is used to traverse the information carried by the regression test instruction, and the traversed information matching the regular expression ad() is determined as the target address, and connected to the target address to obtain data from the target address as the system identification list.

[0094] In this embodiment, the system identification list stores the system identifications of target systems that communicate with the to-be-tested list system.

[0095] Each target system corresponds to a system identifier, so the target system can be accurately acquired according to the system identifier.

[0096] Through the above implementation, required data can be quickly acquired based on tags and regular expressions, and due to the uniqueness of tags, the acquired data is more accurate.

[0097] In this embodiment, the target system refers to a downstream system that communicates with the to-be-tested list system.

[0098] For example, the target system may include, but is not limited to, an AI (Artificial Intelligence) outbound call system, a business opportunity platform, and an artificial agent system.

[0099] S12, obtaining a middleware system connected to the to-be-tested list system and the target system, sending a message request to the middleware system, and receiving data fed back by the middleware system in the message request as a first data source.

[0100] It is understandable that, since different business scenarios have different requirements for data, the data interaction between the to-be-tested list system and the target system is diverse.

[0101] For example, the customer list in the appointment activation task scenario is usually a message queue data type. Therefore, a middleware system will be connected between the test list system and the target system to assist in data storage and transmission.

[0102] In this embodiment, the message request is used to request data from the middleware system.

[0103] The middleware system may be an MQ (Message Queue) system.

[0104] Correspondingly, the first data source fed back from the middleware system may be a message queue.

[0105] S13: calling a target interface of the target system, and acquiring data from the target interface as a second data source based on a configuration protocol.

[0106] In this embodiment, data may be obtained from the target interface via Hypertext Transfer Protocol (HTTP) or the like as the second data source in the data to be verified.

[0107] Furthermore, the second data source in the acquired data to be verified is in the form of an interface file.

[0108] S14: Obtain a pre-configured file structure model, and construct a third data source according to the file structure model and the historical test data.

[0109] In this embodiment, the file structure model is equivalent to a pre-configured file template. The file structure model can be used to disassemble the structure of each file and thus understand the data structure of the file.

[0110] In at least one embodiment of the present invention, constructing a third data source according to the file structure model and the historical test data includes:

[0111] Parsing the regression test instructions to obtain a target task scenario;

[0112] Identifying the data structure of the target task scenario using the file structure model to obtain a target structure;

[0113] Create a data reading function according to the target structure;

[0114] Using the data reading function to read at least one target field from the historical test data;

[0115] The at least one target field is assembled to obtain the third data source.

[0116] For example, the target task scenarios may include, but are not limited to: online application breakpoints, appointment activation, etc.

[0117] In the file structure model, different types of data structures are configured according to different target task scenarios.

[0118] For example, in a target structure, you can configure fields 0-19 to represent customer numbers, fields 20-X to represent ID cards, and so on.

[0119] That is to say, in the file structure model, the meaning represented by each field is clearly defined.

[0120] In this embodiment, the data reading function may include, but is not limited to, one or a combination of the following: a variable filling function, a regular expression, and a constant obtaining function.

[0121] It should be noted that in previous regression testing, due to the dependence on upstream data, it is usually necessary to execute the entire system test process in sequence before further regression testing can be performed, and it is impossible to directly verify the tasks in the specified task scenario.

[0122] For example: When you want to verify a certain function, you need to perform a test from the old function to the new function, so that there is data when verifying the function.

[0123] In the above implementation, big data can be automatically generated for the specified task scenario, which enables the reuse of test data, makes the test more targeted, simplifies the test process, improves test efficiency, and shortens the release time.

[0124] Specifically, assembling the target field to obtain the third data source includes:

[0125] Determine the position of each target field in the intermediate file according to the target structure;

[0126] splicing the target fields according to their positions in the intermediate files to obtain the intermediate files;

[0127] When the target structure corresponds to a fixed-length field, identifying empty fields in the intermediate file, filling the empty fields, and performing table entry on the intermediate file after the filling process to obtain the third data source; or

[0128] When the target structure corresponds to a non-fixed-length field, the intermediate file is divided by a delimiter, and the divided intermediate file is put into a table to obtain the third data source.

[0129] The filling process of the empty field includes:

[0130] Fill the empty fields with zeros; or

[0131] The empty field is padded with spaces on the left.

[0132] Through the above implementation, a customer list depending on various task scenarios can be automatically constructed, thereby improving testing efficiency.

[0133] S15: Store the first data source, the second data source, and the third data source into a cache data pool to obtain data to be verified.

[0134] Specifically, the cache data pool may be an ES (Elasticsearch) data pool.

[0135] In this embodiment, by storing the first data source, the second data source and the third data source in the cache data pool, the large capacity of the cache data pool can be fully utilized, thereby ensuring the performance of the system in the subsequent verification process and avoiding affecting the normal operation of the system.

[0136] S16, parsing the data structure of the data to be verified, and performing comparative verification in the cache data pool based on the data structure of the data to be verified to obtain a test result.

[0137] In at least one embodiment of the present invention, performing comparative verification on the data structure based on the data to be verified in the cache data pool to obtain a test result includes:

[0138] Obtaining a message body of a first data source in the data to be verified as a message body to be verified;

[0139] Obtaining a message body from the middleware system as an actual message body;

[0140] Verifying whether the message body to be verified and the actual message body are identical;

[0141] When the message body to be verified is different from the actual message body, determining that the test result is that the first data source in the data to be verified fails the verification; or

[0142] When the message body to be verified is the same as the actual message body, the message body to be verified is sent to the target system. When a response message from the target system is monitored, it is determined that the test result is that the first data source in the data to be verified has passed the verification; or when no response message from the target system is monitored, it is determined that the test result is that the first data source in the data to be verified has failed the verification.

[0143] Through the above implementation, the message queue in the data to be verified can be tested.

[0144] In at least one embodiment of the present invention, performing comparative verification based on the data structure of the data to be verified in the cache data pool to obtain a test result further includes:

[0145] calling the target interface to obtain target data corresponding to the second data source in the data to be verified, and determining that the test result is that the second data source in the data to be verified passes verification when the second data source in the data to be verified is the same as the target data; or determining that the test result is that the second data source in the data to be verified fails verification when the second data source in the data to be verified is different from the target data;

[0146] For the third data source in the data to be verified, obtain a file logic corresponding to the third data source as the target file logic; when the file logic of the third data source is the same as the target file logic, determine the test result as that the third data source passes verification; or when the file logic of the third data source is different from the target file logic, determine the test result as that the third data source fails verification.

[0147] Through the above implementation, the offline files and interface files in the data to be verified can be tested.

[0148] It can be understood that when the first data source, the second data source in the data to be verified, and the third data source all pass the verification, it can be determined that the to-be-tested list system passes the verification.

[0149] In at least one embodiment of the present invention, after obtaining the test result, the method further includes:

[0150] When the test result shows that the to-be-tested list system passes the verification, obtaining user data in the to-be-tested list system;

[0151] Acquiring user behavior information from the target system based on the user data;

[0152] Extracting a text summary of the user behavior information based on the BM25 algorithm;

[0153] Perform word segmentation on the text summary based on the shortest path word segmentation algorithm to obtain an intermediate text;

[0154] Identify entities in the intermediate text based on a hidden Markov algorithm to obtain candidate entities;

[0155] Obtain a pre-built dictionary, and use the dictionary to clean the candidate entities to obtain user labels;

[0156] The user tag is sent to the target system.

[0157] In this embodiment, the target dictionary may be constructed according to actual usage scenarios to meet actual needs.

[0158] In the above embodiment, the Text Rank algorithm based on the BM25 similarity algorithm is used to optimize the extraction of text summaries. The secondary extraction of entities is performed by combining the hidden Markov algorithm and the dictionary constructed according to the actual scenario, which can make the extracted entities more accurate.

[0159] Furthermore, the user tag is sent to the target system. For example, when the target system is a human agent system or an AI outbound call system, the agent can be assisted in communicating with the customer better based on the user tag, or the agent can be assisted in selecting a more appropriate speech method based on different user tags to improve the user experience.

[0160] It should be noted that in order to further improve data security and prevent data from being maliciously tampered with, the test results can be stored in the blockchain node.

[0161] It can be seen from the above technical solutions that the present invention can, when obtaining a list system to be tested, perform a smoke test and a system test on the list system to be tested to obtain historical test data; when receiving a regression test instruction for the list system to be tested, determine the target system communicating with the list system to be tested according to the regression test instruction, obtain a middleware system connected to the list system to be tested and the target system, send a message request to the middleware system, and receive the data fed back by the middleware system to the message request as a first data source, call the target interface of the target system, and obtain data from the target interface as a second data source based on a configuration protocol, obtain a pre-configured file structure model, and according to the file structure model and the historical data. The third data source is constructed based on the historical test data, and big data is automatically generated for the specified task scenario, thereby realizing the reuse of test data, making the test more targeted, simplifying the test process, improving the test efficiency, and thus shortening the release time. The first data source, the second data source, and the third data source are stored in the cache data pool to obtain the data to be verified, which can make full use of the large capacity of the cache data pool, thereby ensuring the performance of the system in the subsequent verification process and avoiding affecting the normal operation of the system. The data structure of the data to be verified is parsed, and based on the data structure of the data to be verified, comparative verification is performed in the cache data pool to obtain the test results, thereby realizing automated testing of message queues, offline files, and interface files.

[0162] like Figure 2 , which is a functional module diagram of a preferred embodiment of the big data-based list system testing device of the present invention. The big data-based list system testing device 11 comprises a testing unit 110, a determination unit 111, an acquisition unit 112, a construction unit 113, a storage unit 114, and a verification unit 115. The modules / units referred to herein are a series of computer program segments that can be executed by the processor 13 and perform fixed functions, and are stored in the memory 12. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0163] When a system on the to-be-tested list is obtained, the testing unit 110 performs a smoke test and a system test on the system on the to-be-tested list to obtain historical test data.

[0164] In this embodiment, the overall automated verification can be based on the TestNG framework, connecting various test processes in series. At the same time, various subtasks in the test process (such as big data construction, etc.) can be encapsulated in program blocks for easy calling, thereby improving test efficiency.

[0165] In this embodiment, the to-be-tested list system refers to a list system of customers that need to be tested.

[0166] In this embodiment, the smoke test refers to a process of verifying the changed code before embedding the changed code into the to-be-tested list system.

[0167] In this embodiment, the system test refers to a test of the entire system of the to-be-tested list system to check whether there is any abnormality in the to-be-tested list system.

[0168] It should be noted that when the smoke test and the system test are performed on the to-be-tested list system, all pre-configured test processes need to be executed. Therefore, this embodiment retains the complete test data during the test process as the historical test data.

[0169] When receiving a regression test instruction for the system on the to-be-tested list, the determining unit 111 determines a target system that communicates with the system on the to-be-tested list according to the regression test instruction.

[0170] In this embodiment, the regression test instruction may be triggered by relevant staff, such as testers, developers, etc.

[0171] In at least one embodiment of the present invention, the determining unit 111 determines, according to the regression test instruction, the target system that communicates with the to-be-tested list system, including:

[0172] Parsing the regression test instruction to obtain information carried by the regression test instruction;

[0173] Obtaining a preset tag, wherein the preset tag corresponds to an address identified by the storage system;

[0174] Establishing a regular expression according to the preset tag;

[0175] traversing the information carried by the regression test instruction using the regular expression, and determining the traversed information that matches the regular expression as the target address;

[0176] Connecting to the target address and obtaining data from the target address to obtain a system identifier;

[0177] The system corresponding to each of the system identifiers is determined as the target system.

[0178] The preset tags can be customized.

[0179] For example, the preset tag may be configured as ad.

[0180] Furthermore, the regular expression established according to the preset tag may be in the form of ad(), indicating obtaining data with the ad tag.

[0181] Furthermore, the regular expression ad() is used to traverse the information carried by the regression test instruction, and the traversed information matching the regular expression ad() is determined as the target address, and connected to the target address to obtain data from the target address as the system identification list.

[0182] In this embodiment, the system identification list stores the system identifications of target systems that communicate with the to-be-tested list system.

[0183] Each target system corresponds to a system identifier, so the target system can be accurately acquired according to the system identifier.

[0184] Through the above implementation, required data can be quickly acquired based on tags and regular expressions, and due to the uniqueness of tags, the acquired data is more accurate.

[0185] In this embodiment, the target system refers to a downstream system that communicates with the to-be-tested list system.

[0186] For example, the target system may include, but is not limited to, an AI (Artificial Intelligence) outbound call system, a business opportunity platform, and an artificial agent system.

[0187] The acquisition unit 112 acquires a middleware system connected to the to-be-tested list system and the target system, sends a message request to the middleware system, and receives data fed back by the middleware system in response to the message request as a first data source.

[0188] It is understandable that, since different business scenarios have different requirements for data, the data interaction between the to-be-tested list system and the target system is diverse.

[0189] For example, the customer list in the appointment activation task scenario is usually a message queue data type. Therefore, a middleware system will be connected between the test list system and the target system to assist in data storage and transmission.

[0190] In this embodiment, the message request is used to request data from the middleware system.

[0191] The middleware system may be an MQ (Message Queue) system.

[0192] Correspondingly, the first data source fed back from the middleware system may be a message queue.

[0193] The acquiring unit 112 calls a target interface of the target system and acquires data from the target interface as a second data source based on a configuration protocol.

[0194] In this embodiment, data may be obtained from the target interface via Hypertext Transfer Protocol (HTTP) or the like as the second data source in the data to be verified.

[0195] Furthermore, the second data source in the acquired data to be verified is in the form of an interface file.

[0196] The construction unit 113 obtains a pre-configured file structure model, and constructs a third data source according to the file structure model and the historical test data.

[0197] In this embodiment, the file structure model is equivalent to a pre-configured file template. The file structure model can be used to disassemble the structure of each file and thus understand the data structure of the file.

[0198] In at least one embodiment of the present invention, the construction unit 113 constructs the third data source according to the file structure model and the historical test data, including:

[0199] Parsing the regression test instructions to obtain a target task scenario;

[0200] Identifying the data structure of the target task scenario using the file structure model to obtain a target structure;

[0201] Create a data reading function according to the target structure;

[0202] Using the data reading function to read at least one target field from the historical test data;

[0203] The target fields are assembled to obtain the third data source.

[0204] For example, the target task scenarios may include, but are not limited to: online application breakpoints, appointment activation, etc.

[0205] In the file structure model, different types of corresponding data structures are configured according to different target task scenarios.

[0206] For example, in a target structure, you can configure fields 0-19 to represent customer numbers, fields 20-X to represent ID cards, and so on.

[0207] That is to say, in the file structure model, the meaning represented by each field is clearly defined.

[0208] In this embodiment, the data reading function may include, but is not limited to, one or a combination of the following: a variable filling function, a regular expression, and a constant obtaining function.

[0209] It should be noted that in previous regression testing, due to the dependence on upstream data, it is usually necessary to execute the entire system test process in sequence before further regression testing can be performed, and it is impossible to directly verify the tasks in the specified task scenario.

[0210] For example: When you want to verify a certain function, you need to perform a test from the old function to the new function, so that there is data when verifying the function.

[0211] In the above implementation, big data can be automatically generated for the specified task scenario, which enables the reuse of test data, makes the test more targeted, simplifies the test process, improves test efficiency, and shortens the release time.

[0212] Specifically, assembling the target field to obtain the third data source includes:

[0213] Determine the position of each target field in the intermediate file according to the target structure;

[0214] splicing the target fields according to their positions in the intermediate files to obtain the intermediate files;

[0215] When the target structure corresponds to a fixed-length field, identifying empty fields in the intermediate file, filling the empty fields, and performing table entry on the intermediate file after the filling process to obtain the third data source; or

[0216] When the target structure corresponds to a non-fixed-length field, the intermediate file is divided by a delimiter, and the divided intermediate file is put into a table to obtain the third data source.

[0217] The filling process of the empty field includes:

[0218] Fill the empty fields with zeros; or

[0219] The empty field is padded with spaces on the left.

[0220] Through the above implementation, a customer list depending on various task scenarios can be automatically constructed, thereby improving testing efficiency.

[0221] The storage unit 114 stores the first data source, the second data source, and the third data source into a cache data pool to obtain data to be verified.

[0222] Specifically, the cache data pool may be an ES (Elasticsearch) data pool.

[0223] In this embodiment, by storing the first data source, the second data source and the third data source in the cache data pool, the large capacity of the cache data pool can be fully utilized, thereby ensuring the performance of the system in the subsequent verification process and avoiding affecting the normal operation of the system.

[0224] The verification unit 115 analyzes the data structure of the data to be verified, and performs comparative verification in the cache data pool based on the data structure of the data to be verified to obtain a test result.

[0225] In at least one embodiment of the present invention, the verification unit 115 performs comparative verification in the cache data pool based on the data structure of the data to be verified, and obtains a test result including:

[0226] Obtaining a message body of a first data source in the data to be verified as a message body to be verified;

[0227] Obtaining a message body from the middleware system as an actual message body;

[0228] Verifying whether the message body to be verified and the actual message body are identical;

[0229] When the message body to be verified is different from the actual message body, determining that the test result is that the first data source in the data to be verified fails the verification; or

[0230] When the message body to be verified is the same as the actual message body, the message body to be verified is sent to the target system. When a response message from the target system is monitored, it is determined that the test result is that the first data source in the data to be verified has passed the verification; or when no response message from the target system is monitored, it is determined that the test result is that the first data source in the data to be verified has failed the verification.

[0231] Through the above implementation, the message queue in the data to be verified can be tested.

[0232] In at least one embodiment of the present invention, the verification unit 115 performs comparative verification in the cache data pool based on the data structure of the data to be verified, and obtaining the test result further includes:

[0233] calling the target interface to obtain target data corresponding to the second data source in the data to be verified, and determining that the test result is that the second data source in the data to be verified passes verification when the second data source in the data to be verified is the same as the target data; or determining that the test result is that the second data source in the data to be verified fails verification when the second data source in the data to be verified is different from the target data;

[0234] For the third data source in the data to be verified, obtain a file logic corresponding to the third data source as the target file logic; when the file logic of the third data source is the same as the target file logic, determine the test result as that the third data source passes verification; or when the file logic of the third data source is different from the target file logic, determine the test result as that the third data source fails verification.

[0235] Through the above implementation, the offline files and interface files in the data to be verified can be tested.

[0236] It can be understood that when the first data source, the second data source in the data to be verified, and the third data source all pass the verification, it can be determined that the to-be-tested list system passes the verification.

[0237] In at least one embodiment of the present invention, after obtaining the test result, when the test result indicates that the to-be-tested list system passes verification, obtaining user data in the to-be-tested list system;

[0238] Acquiring user behavior information from the target system based on the user data;

[0239] Extracting a text summary of the user behavior information based on the BM25 algorithm;

[0240] Perform word segmentation on the text summary based on the shortest path word segmentation algorithm to obtain an intermediate text;

[0241] Identify entities in the intermediate text based on a hidden Markov algorithm to obtain candidate entities;

[0242] Obtain a pre-built dictionary, and use the dictionary to clean the candidate entities to obtain user labels;

[0243] The user tag is sent to the target system.

[0244] In this embodiment, the target dictionary may be constructed according to actual usage scenarios to meet actual needs.

[0245] In the above embodiment, the Text Rank algorithm based on the BM25 similarity algorithm is used to optimize the extraction of text summaries. The secondary extraction of entities is performed by combining the hidden Markov algorithm and the dictionary constructed according to the actual scenario, which can make the extracted entities more accurate.

[0246] Furthermore, the user tag is sent to the target system. For example, when the target system is a human agent system or an AI outbound call system, the agent can be assisted in communicating with the customer better based on the user tag, or the agent can be assisted in selecting a more appropriate speech method based on different user tags to improve the user experience.

[0247] It should be noted that in order to further improve data security and prevent data from being maliciously tampered with, the test results can be stored in the blockchain node.

[0248] It can be seen from the above technical solutions that the present invention can, when obtaining a list system to be tested, perform a smoke test and a system test on the list system to be tested to obtain historical test data; when receiving a regression test instruction for the list system to be tested, determine the target system communicating with the list system to be tested according to the regression test instruction, obtain a middleware system connected to the list system to be tested and the target system, send a message request to the middleware system, and receive the data fed back by the middleware system to the message request as a first data source, call the target interface of the target system, and obtain data from the target interface as a second data source based on a configuration protocol, obtain a pre-configured file structure model, and according to the file structure model and the historical data. The third data source is constructed based on the historical test data, and big data is automatically generated for the specified task scenario, thereby realizing the reuse of test data, making the test more targeted, simplifying the test process, improving the test efficiency, and thus shortening the release time. The first data source, the second data source, and the third data source are stored in the cache data pool to obtain the data to be verified, which can make full use of the large capacity of the cache data pool, thereby ensuring the performance of the system in the subsequent verification process and avoiding affecting the normal operation of the system. The data structure of the data to be verified is parsed, and based on the data structure of the data to be verified, comparative verification is performed in the cache data pool to obtain the test results, thereby realizing automated testing of message queues, offline files, and interface files.

[0249] like Figure 3 1 is a schematic diagram of the structure of a computer device according to a preferred embodiment of the present invention for implementing a list system testing method based on big data construction.

[0250] The computer device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a list system testing program constructed based on big data.

[0251] Those skilled in the art will understand that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation on the computer device 1. The computer device 1 may have either a bus structure or a star structure. The computer device 1 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components. For example, the computer device 1 may also include input and output devices, network access devices, etc.

[0252] It should be noted that the computer device 1 is only an example. Other existing or future electronic products that are suitable for the present invention should also be included in the scope of protection of the present invention and included here by reference.

[0253] The memory 12 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 12 may be an internal storage unit of the computer device 1, such as a mobile hard disk of the computer device 1. In other embodiments, the memory 12 may also be an external storage device of the computer device 1, such as a plug-in mobile hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the computer device 1. Furthermore, the memory 12 may include both an internal storage unit of the computer device 1 and an external storage device. The memory 12 can be used not only to store application software installed on the computer device 1 and various types of data, such as the code of a list system test program constructed based on big data, but can also be used to temporarily store data that has been output or is about to be output.

[0254] In some embodiments, the processor 13 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control core (Control Unit) of the computer device 1, utilizing various interfaces and circuits to connect the various components of the entire computer device 1. It executes or runs programs or modules stored in the memory 12 (e.g., executing a list system test program based on big data), and calls data stored in the memory 12 to execute various functions of the computer device 1 and process data.

[0255] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-mentioned embodiments of the list system testing method based on big data construction, such as Figure 1 Steps shown.

[0256] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to implement the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into a testing unit 110, a determination unit 111, an acquisition unit 112, a construction unit 113, a storage unit 114, and a verification unit 115.

[0257] The above-mentioned integrated unit implemented in the form of a software functional module can be stored in a computer-readable storage medium. The above-mentioned software functional module is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, computer device, or network device, etc.) or a processor to execute the portion of the list system testing method based on big data construction described in various embodiments of the present invention.

[0258] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the processes in the above-mentioned method embodiments by instructing relevant hardware devices through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments.

[0259] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory, etc.

[0260] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0261] Blockchain, as used in this article, refers to a novel application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks generated using cryptographic methods. Each block contains information about a batch of online transactions, used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0262] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The figure shows that only one straight line is used, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 12 and at least one processor 13.

[0263] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power supply may also include one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be detailed here.

[0264] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used to establish a communication connection between the computer device 1 and other computer devices.

[0265] Optionally, the computer device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed in the computer device 1 and to display a visual user interface.

[0266] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0267] Figure 3 Only the computer device 1 having components 12-13 is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the computer device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0268] Combine Figure 1 The memory 12 in the computer device 1 stores a plurality of instructions to implement a list system testing method based on big data construction, and the processor 13 can execute the plurality of instructions to implement:

[0269] When a to-be-tested list system is obtained, a smoke test and a system test are performed on the to-be-tested list system to obtain historical test data;

[0270] When a regression test instruction for the system on the to-be-tested list is received, determining a target system that communicates with the system on the to-be-tested list according to the regression test instruction;

[0271] Acquire a middleware system connected to the to-be-tested list system and the target system, send a message request to the middleware system, and receive data fed back by the middleware system in the message request as a first data source;

[0272] calling a target interface of the target system, and acquiring data from the target interface as a second data source based on a configuration protocol;

[0273] Obtaining a pre-configured file structure model, and constructing a third data source according to the file structure model and the historical test data;

[0274] Storing the first data source, the second data source, and the third data source in a cache data pool to obtain data to be verified;

[0275] The data structure of the data to be verified is parsed, and based on the data structure of the data to be verified, a comparison and verification is performed in the cache data pool to obtain a test result.

[0276] Specifically, the specific implementation method of the processor 13 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0277] In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical functional division, and actual implementation may employ other division methods.

[0278] The present invention can be used in a wide variety of general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0279] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0280] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0281] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0282] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0283] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in the present invention may also be implemented by a single unit or device through software or hardware. Terms such as first and second are used to indicate names and do not imply any particular order.

[0284] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A list system testing method based on big data construction, characterized in that: include: When a to-be-tested list system is obtained, a smoke test and a system test are performed on the to-be-tested list system to obtain historical test data; When a regression test instruction for the system on the to-be-tested list is received, determining a target system that communicates with the system on the to-be-tested list according to the regression test instruction; Acquire a middleware system connected to the to-be-tested list system and the target system, send a message request to the middleware system, and receive data fed back by the middleware system in the message request as a first data source; calling a target interface of the target system, and acquiring data from the target interface as a second data source based on a configuration protocol; Obtaining a pre-configured file structure model, and constructing a third data source according to the file structure model and the historical test data; Storing the first data source, the second data source, and the third data source in a cache data pool to obtain data to be verified; Parsing the data structure of the data to be verified, and performing comparative verification in the cache data pool based on the data structure of the data to be verified to obtain a test result; Wherein, the first data source is a message queue; the second data source is an interface file; The third data source is an offline file; and the file structure model is a pre-configured file template.

2. The method for testing a list system based on big data construction according to claim 1, characterized in that: Determining the target system communicating with the to-be-tested list system according to the regression test instruction includes: Parsing the regression test instruction to obtain information carried by the regression test instruction; Obtaining a preset tag, wherein the preset tag corresponds to an address identified by the storage system; Establishing a regular expression according to the preset tag; traversing the information carried by the regression test instruction using the regular expression, and determining the traversed information that matches the regular expression as the target address; Connecting to the target address and obtaining data from the target address to obtain a system identifier; The system corresponding to each of the system identifiers is determined as the target system.

3. The method for testing a list system based on big data construction according to claim 1, characterized in that: The constructing of the third data source according to the file structure model and the historical test data includes: Parsing the regression test instructions to obtain a target task scenario; Identifying the data structure of the target task scenario using the file structure model to obtain a target structure; Create a data reading function according to the target structure; Using the data reading function to read at least one target field from the historical test data; The target fields are assembled to obtain the third data source.

4. The method for testing a list system based on big data construction according to claim 3, characterized in that: Assembling the target fields to obtain the third data source includes: Determine the position of each target field in the intermediate file according to the target structure; splicing the target fields according to their positions in the intermediate files to obtain the intermediate files; When the target structure corresponds to a fixed-length field, identifying empty fields in the intermediate file, filling the empty fields, and performing table entry on the intermediate file after the filling process to obtain the third data source; or When the target structure corresponds to a non-fixed-length field, the intermediate file is divided by a delimiter, and the divided intermediate file is put into a table to obtain the third data source.

5. The method for testing a list system based on big data construction according to claim 1, characterized in that: The data structure based on the data to be verified is compared and verified in the cache data pool, and the test results obtained include: Obtaining a message body of a first data source in the data to be verified as a message body to be verified; Obtaining a message body from the middleware system as an actual message body; Verifying whether the message body to be verified and the actual message body are identical; When the message body to be verified is different from the actual message body, determining that the test result is that the first data source in the data to be verified fails the verification; or When the message body to be verified is the same as the actual message body, the message body to be verified is sent to the target system. When a response message from the target system is monitored, it is determined that the test result is that the first data source in the data to be verified has passed the verification; or when no response message from the target system is monitored, it is determined that the test result is that the first data source in the data to be verified has failed the verification.

6. The method for testing a list system based on big data construction according to claim 1, characterized in that: The data structure based on the data to be verified is compared and verified in the cache data pool to obtain a test result, which further includes: calling the target interface to obtain target data corresponding to the second data source in the data to be verified, and determining that the test result is that the second data source in the data to be verified passes verification when the second data source in the data to be verified is the same as the target data; or determining that the test result is that the second data source in the data to be verified fails verification when the second data source in the data to be verified is different from the target data; For the third data source in the data to be verified, obtain a file logic corresponding to the third data source as the target file logic; when the file logic of the third data source is the same as the target file logic, determine the test result as that the third data source passes verification; or when the file logic of the third data source is different from the target file logic, determine the test result as that the third data source fails verification.

7. The method for testing a list system based on big data construction according to claim 1, characterized in that: After obtaining the test results, the method further includes: When the test result shows that the to-be-tested list system passes the verification, obtaining user data in the to-be-tested list system; Acquiring user behavior information from the target system based on the user data; Extracting a text summary of the user behavior information based on the BM25 algorithm; Perform word segmentation on the text summary based on the shortest path word segmentation algorithm to obtain an intermediate text; Identify entities in the intermediate text based on a hidden Markov algorithm to obtain candidate entities; Obtain a pre-built dictionary, and use the dictionary to clean the candidate entities to obtain user labels; The user tag is sent to the target system.

8. A list system testing device based on big data construction, characterized in that: The list system testing device based on big data construction includes: The testing unit is configured to perform a smoke test and a system test on the system to be tested when the system to be tested is obtained, and obtain historical test data; a determining unit configured to, upon receiving a regression test instruction for the system on the to-be-tested list, determine a target system communicating with the system on the to-be-tested list according to the regression test instruction; an acquiring unit, configured to acquire a middleware system connected to the to-be-tested list system and the target system, send a message request to the middleware system, and receive data fed back by the middleware system in the message request as a first data source; The acquisition unit is further configured to call a target interface of the target system and acquire data from the target interface as a second data source based on a configuration protocol; a construction unit, configured to obtain a pre-configured file structure model and construct a third data source according to the file structure model and the historical test data; a storage unit, configured to store the first data source, the second data source, and the third data source into a cache data pool to obtain data to be verified; A verification unit, configured to analyze the data structure of the data to be verified, and perform comparative verification in the cache data pool based on the data structure of the data to be verified to obtain a test result; Among them, the first data source is a message queue; the second data source is an interface file; the third data source is an offline file; and the file structure model is a pre-configured file template.

9. A computer device, characterized in that: The computer device comprises: a memory storing at least one instruction; and A processor executes instructions stored in the memory to implement the list system testing method based on big data construction as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the list system testing method based on big data construction as described in any one of claims 1 to 7.

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