Automatic interface case updating method and system based on daily manual test

Abstract

The invention discloses an automatic interface case updating method and system based on daily manual testing, and the method comprises the steps: carrying out a rear collection strategy at an access layer, intercepting the flow passing through the access layer, and collecting interface request information; kafka and Zookeeper software components are obtained and deployed, and a Zookeeper service and a Kafka service are started after configuration is completed; creating a Kafka theme for storing the interface request information by using a Kafka management tool; capturing interface request traffic and writing the interface request traffic into a Kafka theme by utilizing a traffic capturing tool and combining network service log configuration; reading the interface request information from the Kafka theme, and converting the interface request information into command data in a preset format according to a conversion rule; generating a unique identifier as a test case ID for the converted command data in the preset format by adopting a Hash algorithm; and summarizing the converted command data and the test case ID into a text file, and importing the text file into an interface automation platform to update the case. According to the method, the efficiency and the accuracy of interface automation case updating are improved, and the test cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of software testing, and in particular relates to an interface automation use case updating method and system based on daily manual testing. Background Art

[0002] In the software development life cycle, interface automation testing is crucial to ensuring software quality, improving test efficiency and coverage. With the continuous expansion and iteration of software functions, interfaces are also frequently changed, which makes the update and maintenance of interface automation test cases a difficult task.

[0003] The traditional manual update method of interface automation test cases is not only inefficient and prone to omissions and errors, but also has inconsistent standards among different testers, making it difficult to cope with the testing needs of large-scale interfaces. In addition, the cost of manual maintenance continues to rise with the increase in the number of test cases, seriously affecting the overall efficiency of software development and testing. Therefore, there is an urgent need for an efficient and accurate interface automation case update solution to solve the problems existing in the existing technology. Summary of the invention

[0004] To this end, the present invention provides an interface automation use case update method and system based on daily manual testing to solve the problems of low efficiency, easy omission and error in traditional technologies.

[0005] In order to achieve the above object, the present invention provides the following technical solution: an interface automation use case update method based on daily manual testing, comprising:

[0006] Traffic interception and information collection: Implement post-collection strategies at the access layer to intercept traffic passing through the access layer and collect interface request information;

[0007] Deploy and start basic services: Obtain and deploy Kafka and Zookeeper software components, and start Zookeeper and Kafka services after completing the configuration;

[0008] Data storage topic creation: Use the Kafka management tool to create a Kafka topic to store the interface request information;

[0009] Data capture and writing: Use traffic capture tools and network service log configuration to capture interface request traffic and write it to the Kafka topic;

[0010] Data conversion processing: reading the interface request information from the Kafka topic and converting it into command data in a preset format according to the conversion rules;

[0011] Test case identification generation: For the converted command data in the preset format, a hash algorithm is used to generate a unique identifier as the test case ID;

[0012] Use case data import and update: The converted command data and test case IDs are summarized into a text file and imported into the interface automation platform to update the use case.

[0013] As an optimization solution for the interface automation use case update method based on daily manual testing, non-intrusive network monitoring technology is used in the traffic interception and information collection process to store the collected interface request information in the form of structured data; the interface request information includes the requested URL, request method, request parameters, request header and request body.

[0014] As an optimization solution for the interface automation use case update method based on daily manual testing, during the basic service deployment and startup process, the configuration parameters of Kafka and Zookeeper are adjusted according to the system's network bandwidth, data processing volume, and response time requirements.

[0015] As an optimization solution for the interface automation use case update method based on daily manual testing, during the data storage topic creation process, the number of partitions and the replication factor set when creating the topic are configured according to the data volume and read and write frequency of the interface request information.

[0016] As an optimization solution for the interface automation use case update method based on daily manual testing, in the data capture and writing process, the traffic capture tool realizes traffic capture based on network data packet parsing technology, and the network service log configuration specifies the log recording granularity and storage path.

[0017] As an optimization solution for the interface automation case update method based on daily manual testing, during the data conversion process, the conversion rules are customized according to the test case format supported by the interface automation platform, and the specified characters are encoded.

[0018] As an optimization solution for the interface automation case update method based on daily manual testing, in the test case identification generation process, the command data is normalized before the hash algorithm is used for calculation;

[0019] During the use case data import and update process, the interface automation platform performs data format verification and integrity check on the file before importing the text file, including checking the number, type and order of data fields.

[0020] The present invention also provides an interface automation use case update system based on daily manual testing, comprising:

[0021] Traffic interception and collection module, used to implement post-collection strategy at the access layer, intercept traffic passing through the access layer and collect interface request information;

[0022] The basic service deployment module is used to obtain and deploy Kafka and Zookeeper software components, and start the Zookeeper service and Kafka service after completing the configuration;

[0023] A data storage topic creation module, used to use a Kafka management tool to create a Kafka topic for storing the interface request information;

[0024] The data capture and writing module is used to use the traffic capture tool in combination with the network service log configuration to capture the interface request traffic and write it into the Kafka topic;

[0025] A data conversion processing module is used to read the interface request information from the Kafka topic and convert it into command data in a preset format according to the conversion rules;

[0026] A test case identification generation module is used to generate a unique identifier as a test case ID for the converted command data in a preset format by using a hash algorithm;

[0027] The use case data import and update module is used to summarize the converted command data and test case ID into a text file and import it into the interface automation platform to update the use case.

[0028] As a preferred solution for the interface automation use case update system based on daily manual testing, the traffic interception and collection module adopts non-intrusive network monitoring technology to store the collected interface request information in the form of structured data; the interface request information includes the requested URL, request method, request parameters, request header and request body.

[0029] As a preferred solution for the interface automation use case update system based on daily manual testing, in the basic service deployment module, the configuration parameters of Kafka and Zookeeper are adjusted according to the system's network bandwidth, data processing volume and response time requirements.

[0030] As a preferred solution for the interface automation use case update system based on daily manual testing, in the data storage topic creation module, the number of partitions and the replication factor set when creating the topic are configured according to the data volume and read and write frequency of the interface request information.

[0031] As a preferred solution for the interface automation use case update system based on daily manual testing, in the data capture writing module, the traffic capture tool realizes traffic capture based on network data packet parsing technology, and the network service log configuration specifies the log recording granularity and storage path.

[0032] As a preferred solution for the interface automation case update system based on daily manual testing, in the data conversion processing module, the conversion rules are customized according to the test case format supported by the interface automation platform, and the designated characters are encoded.

[0033] As a preferred solution for the interface automation use case update system based on daily manual testing, in the test case identification generation module, the command data is normalized before the hash algorithm is used for calculation;

[0034] As a preferred solution for the interface automation use case update system based on daily manual testing, the use case data is imported into the update module, and the interface automation platform performs data format verification and integrity check on the file before importing the text file, including checking the number, type and order of data fields.

[0035] The beneficial effects of the present invention are as follows: by implementing a post-collection strategy at the access layer, the traffic passing through the access layer is intercepted and interface request information is collected; Kafka and Zookeeper software components are obtained and deployed, and Zookeeper service and Kafka service are started after configuration is completed; Kafka management tool is used to create a Kafka topic storing the interface request information; a traffic capture tool is used, combined with network service log configuration, to capture interface request traffic and write it into a Kafka topic; the interface request information is read from the Kafka topic, and converted into command data in a preset format according to a conversion rule; for the converted command data in the preset format, a unique identifier is generated using a hash algorithm as a test case ID; the converted command data and test case ID are summarized into a text file, and imported into an interface automation platform to update the case. The present invention realizes the automatic update of interface automation cases, greatly reduces manual operations, improves test efficiency and accuracy, and ensures the consistency of test cases. It can effectively respond to the testing needs of large-scale interfaces, reduce test maintenance costs, promptly discover interface problems, improve software quality, and provide users with a better user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the drawings required for the implementation methods or the prior art descriptions are briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0037] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0038] Figure 1 A flow chart of an interface automation use case update method based on daily manual testing provided by an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of the architecture of an interface automation use case update system based on daily manual testing provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example 1

[0042] See also Figure 1 In an embodiment of the present invention, a method for updating an interface automation use case based on daily manual testing is provided, comprising the following steps:

[0043] S1, Traffic interception and information collection: Implement post-collection strategy at the access layer to intercept traffic passing through the access layer and collect interface request information;

[0044] S2. Deploy and start basic services: Obtain and deploy Kafka and Zookeeper software components, and start Zookeeper and Kafka services after configuration.

[0045] S3. Data storage topic creation: Use the Kafka management tool to create a Kafka topic that stores the interface request information;

[0046] S4. Data capture and writing: Use the traffic capture tool and network service log configuration to capture interface request traffic and write it to the Kafka topic;

[0047] S5, data conversion processing: reading the interface request information from the Kafka topic, and converting it into command data in a preset format according to the conversion rules;

[0048] S6. Test case identification generation: for the converted command data in the preset format, a hash algorithm is used to generate a unique identifier as the test case ID;

[0049] S7. Use case data import and update: The converted command data and test case ID are summarized into a text file and imported into the interface automation platform to update the use case.

[0050] In this embodiment, in step S1, during the traffic interception and information collection process, non-intrusive network monitoring technology is used to store the collected interface request information in the form of structured data; the interface request information includes the requested URL, request method, request parameters, request header and request body.

[0051] Specifically, the access layer is the key node for external requests to enter the system. Through the post-collection strategy, the traffic can be intercepted without affecting the normal business process. Adopt non-intrusive network monitoring technology, use network monitoring equipment or software to monitor the transmission of network data packets. According to the rules of network protocols (such as TCP / IP), parse the information in the data packet and extract the interface request information. Store the collected interface request information in the form of structured data, such as JSON or XML format, to facilitate subsequent processing and storage. The interface request information includes the requested URL, request method (such as GET, POST, etc.), request parameters, request header and request body, which fully describe the call of the interface.

[0052] Among them, the code of Python using the Scapy library for a simple network monitoring example is as follows:

[0053] "from scapy.all import sniff

[0054] defpacket_callback(packet):

[0055] ifpacket.haslayer('TCP')andpacket.haslayer('HTTPRequest'):

[0056] url=packet['HTTPRequest'].Path.decode()

[0057] method=packet['HTTPRequest'].Method.decode()

[0058] headers=packet['HTTPRequest'].Headers.decode()

[0059] body=packet['Raw'].load.decode()ifpacket.haslayer('Raw')else""

[0060] #Here you can store information in structured data, such as a dictionary

[0061] request_info={

[0062] "url":url,

[0063] "method":method,

[0064] "headers":headers,

[0065] "body":body

[0066] }

[0067] print(request_info)

[0068] sniff(filter="tcp port 80",prn="packet_callback)".

[0069] In this embodiment, in step S2, during the basic service deployment and startup process, the configuration parameters of Kafka and Zookeeper are adjusted according to the system's network bandwidth, data processing volume, and response time requirements.

[0070] Specifically, Kafka is a high-throughput distributed message queue system used to store and process a large amount of interface request information. Zookeeper is a distributed coordination service that provides metadata management, node coordination and other functions for Kafka. Adjust the configuration parameters of Kafka and Zookeeper according to the system's network bandwidth, data processing volume and response time requirements. For example, when the network bandwidth is small, appropriately reduce the number of Kafka partitions; when the data processing volume is large, increase Kafka's replication factor to improve reliability. After starting the Zookeeper service, Kafka can rely on Zookeeper for node discovery, configuration management and other operations.

[0071] Taking the Linux system as an example, the code to start the Zookeeper and Kafka services is as follows:

[0072] “#Start Zookeeper service

[0073] bin / zookeeper-server-start.sh config / zookeeper.properties&

[0074] #Start the Kafka service

[0075] bin / kafka-server-start.sh config / server.properties&".

[0076] In this embodiment, in step S3, during the data storage topic creation process, the number of partitions and the replication factor set when creating the topic are configured according to the data volume and read and write frequency of the interface request information.

[0077] Specifically, Kafka topics are logical classifications of messages. By creating topics, different types of interface request information can be classified and stored. According to the data volume and read and write frequency of the interface request information, set the number of partitions and replication factor of the topic. The number of partitions determines the parallel processing capability of the data. When the read and write frequency is high, the number of partitions can be increased; the replication factor determines the redundancy of the data. Increasing the replication factor can enhance the reliability of the data. You can create a topic using Kafka's command line tool or management API.

[0078] The code for creating a topic using the Kafka command line tool is as follows:

[0079]

[0080] In this embodiment, in step S4, during the data capture and writing process, the traffic capture tool implements traffic capture based on network data packet parsing technology, and the network service log configuration specifies the recording granularity and storage path of the log.

[0081] Specifically, the traffic capture tool monitors the network interface and captures the interface request traffic based on network packet parsing technology. By configuring the log of the network service (such as Nginx), detailed interface request information can be recorded, including request time, request IP, etc. The captured interface request traffic is encapsulated according to the Kafka message format, and then the message is written to the specified topic through the Kafka producer API.

[0082] The implementation code of the data capture and writing process is as follows:

[0083]

[0084] In this embodiment, in step S5, during the data conversion process, the conversion rules are customized according to the test case format supported by the interface automation platform, and the designated characters are encoded.

[0085] Specifically, use Kafka's consumer API to read the interface request information from the specified topic. Customize the conversion rules according to the test case format supported by the interface automation platform. For example, convert the interface request information into data in the curl command format to facilitate execution in the interface automation platform. During the conversion process, encode the specified characters to avoid garbled characters during data transmission and processing.

[0086] The implementation code of data conversion processing is as follows:

[0087]

[0088] In this embodiment, in step S6, during the test case identification generation process, the command data is normalized before the hash algorithm is used for calculation.

[0089] Specifically, the hash algorithm can map data of any length to a hash value of fixed length. The converted command data is normalized, such as removing redundant spaces, unifying character encoding, etc., and then the hash value is calculated using a hash algorithm (such as MD5, SHA-256, etc.). Due to the characteristics of the hash algorithm, the hash values ​​generated by different command data are almost impossible to be the same, so they can be used as unique identifiers for test cases.

[0090] The implementation code for test case identification generation is as follows:

[0091] "Important

[0092] curl_command="curl-X GET' / api / test'-H'Content-Type:application / json'-d""

[0093] test_case_id=hashlib.md5(curl_command.encode('utf-8')).hexdigest()

[0094] print(test_case_id)".

[0095] In this embodiment, in step S7, during the use case data import and update process, the interface automation platform performs data format verification and integrity check on the file before importing the text file, including checking the number, type and order of data fields.

[0096] Specifically, the converted command data and the corresponding test case ID are summarized into a text file in a certain format (such as CSV, JSON, etc.). The interface automation platform provides a data import function. Before importing, the platform performs data format verification and integrity check on the file, including checking whether the number, type and order of data fields meet the requirements. After the verification is passed, the data in the file is updated to the interface automation test case library.

[0097] Among them, Python saves the data as a CSV file implementation code as follows:

[0098] "import csv

[0099] data=[

[0100] {"test_case_id":"123456789abcdef","curl_command":"curl-X GET' / api / test'-H'Content-Type:application / json'-d""} ]

[0102] with open('test_cases.csv',mode='w',newline=")as file:

[0103] fieldnames=['test_case_id','curl_command']

[0104] writer=csv.DictWriter(file,fieldnames=fieldnames)

[0105] writer.writeheader()

[0106] forrow in data:

[0107] writer.writerow(row)".

[0108] In summary, the present invention intercepts the traffic passing through the access layer and collects interface request information by implementing a post-collection strategy at the access layer; obtains and deploys Kafka and Zookeeper software components, and starts Zookeeper and Kafka services after completing the configuration; uses the Kafka management tool to create a Kafka topic that stores the interface request information; uses a traffic capture tool, combined with network service log configuration, to capture interface request traffic and write it into the Kafka topic; reads the interface request information from the Kafka topic, and converts it into command data in a preset format according to the conversion rule; for the converted command data in the preset format, a hash algorithm is used to generate a unique identifier as a test case ID; the converted command data and test case ID are summarized into a text file, and imported into the interface automation platform to update the case. The present invention realizes the automatic update of interface automation cases, greatly reduces manual operations, improves test efficiency and accuracy, and ensures the consistency of test cases. It can effectively respond to the testing needs of large-scale interfaces, reduce test maintenance costs, promptly discover interface problems, improve software quality, and provide users with a better user experience.

[0109] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0110] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] Example 2

[0112] See also Figure 2 Embodiment 2 of the present invention provides an interface automation use case update system based on daily manual testing, including:

[0113] Traffic interception and collection module 001, used to implement the post-collection strategy at the access layer, intercept the traffic passing through the access layer and collect interface request information;

[0114] Basic service deployment module 002 is used to obtain and deploy Kafka and Zookeeper software components, and start Zookeeper and Kafka services after configuration is completed;

[0115] A data storage topic creation module 003 is used to use a Kafka management tool to create a Kafka topic for storing the interface request information;

[0116] The data capture and writing module 004 is used to use the traffic capture tool in combination with the network service log configuration to capture the interface request traffic and write it into the Kafka topic;

[0117] The data conversion processing module 005 is used to read the interface request information from the Kafka topic and convert it into command data in a preset format according to the conversion rules;

[0118] The test case identification generating module 006 is used to generate a unique identifier as a test case ID for the converted command data in a preset format by using a hash algorithm;

[0119] The use case data import and update module 007 is used to summarize the converted command data and test case ID into a text file, and import it into the interface automation platform to update the use case.

[0120] In this embodiment, the traffic interception and collection module 001 uses non-intrusive network monitoring technology to store the collected interface request information in the form of structured data; the interface request information includes the requested URL, request method, request parameters, request header and request body.

[0121] In this embodiment, in the basic service deployment module 002, the configuration parameters of Kafka and Zookeeper are adjusted according to the system's network bandwidth, data processing volume, and response time requirements.

[0122] In this embodiment, in the data storage topic creation module 003, the number of partitions and the replication factor set when creating a topic are configured according to the data volume and the reading and writing frequency of the interface request information.

[0123] In this embodiment, in the data capture writing module 004, the traffic capture tool realizes traffic capture based on network data packet parsing technology, and the network service log configuration stipulates the recording granularity and storage path of the log.

[0124] In this embodiment, in the data conversion processing module 005, the conversion rules are customized according to the test case format supported by the interface automation platform, and the designated characters are encoded.

[0125] In this embodiment, in the test case identification generation module 006, the command data is normalized before the hash algorithm is used for calculation;

[0126] In this embodiment, in the use case data import and update module 007, the interface automation platform performs data format verification and integrity check on the file before importing the text file, including checking the number, type and order of data fields.

[0127] It should be noted that the information interaction, execution process and other contents between the various units of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0128] Example 3

[0129] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code of an interface automation use case update method based on daily manual testing is stored, and the program code includes instructions for executing embodiment 1 or any possible implementation method of the interface automation use case update method based on daily manual testing.

[0130] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0131] Example 4

[0132] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0133] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the interface automation use case update method based on daily manual testing of Example 1 or any possible implementation thereof.

[0134] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0135] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital consumer customer line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0136] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0137] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. The interface automation use case update method based on daily manual testing is characterized by: include: Traffic interception and information collection: Implement post-collection strategies at the access layer to intercept traffic passing through the access layer and collect interface request information; Deploy and start basic services: Obtain and deploy Kafka and Zookeeper software components, and start Zookeeper and Kafka services after completing the configuration; Data storage topic creation: Use the Kafka management tool to create a Kafka topic to store the interface request information; Data capture and writing: Use traffic capture tools and network service log configuration to capture interface request traffic and write it to the Kafka topic; Data conversion processing: reading the interface request information from the Kafka topic and converting it into command data in a preset format according to the conversion rules; Test case identification generation: For the converted command data in the preset format, a hash algorithm is used to generate a unique identifier as the test case ID; Use case data import and update: The converted command data and test case IDs are summarized into a text file and imported into the interface automation platform to update the use case.

2. The interface automation use case updating method based on daily manual testing according to claim 1 is characterized in that: During the flow interception and information collection process, non-intrusive network monitoring technology is used to store the collected interface request information in the form of structured data; the interface request information includes the requested URL, request method, request parameters, request header and request body.

3. The interface automation use case updating method based on daily manual testing according to claim 1 is characterized in that: During the basic service deployment and startup process, the configuration parameters of Kafka and Zookeeper are adjusted according to the system's network bandwidth, data processing volume, and response time requirements.

4. The interface automation use case updating method based on daily manual testing according to claim 1 is characterized in that: During the data storage topic creation process, the number of partitions and the replication factor set when creating the topic are configured according to the data volume and read and write frequency of the interface request information.

5. The interface automation use case updating method based on daily manual testing according to claim 1 is characterized in that: During the data capture and writing process, the traffic capture tool realizes traffic capture based on network data packet parsing technology, and the network service log configuration stipulates the recording granularity and storage path of the log.

6. The interface automation use case updating method based on daily manual testing according to claim 1 is characterized in that: During the data conversion process, the conversion rules are customized according to the test case format supported by the interface automation platform, and the designated characters are encoded.

7. The interface automation use case updating method based on daily manual testing according to claim 1 is characterized in that: In the test case identification generation process, the command data is normalized before the hash algorithm is used for calculation; During the use case data import and update process, the interface automation platform performs data format verification and integrity check on the file before importing the text file, including checking the number, type and order of data fields.

8. The interface automation use case update system based on daily manual testing is characterized by: include: Traffic interception and collection module, used to implement post-collection strategy at the access layer, intercept traffic passing through the access layer and collect interface request information; The basic service deployment module is used to obtain and deploy Kafka and Zookeeper software components, and start the Zookeeper service and Kafka service after completing the configuration; A data storage topic creation module, used to use a Kafka management tool to create a Kafka topic for storing the interface request information; The data capture and writing module is used to use the traffic capture tool in combination with the network service log configuration to capture the interface request traffic and write it into the Kafka topic; A data conversion processing module is used to read the interface request information from the Kafka topic and convert it into command data in a preset format according to the conversion rules; A test case identification generation module is used to generate a unique identifier as a test case ID for the converted command data in a preset format by using a hash algorithm; The use case data import and update module is used to summarize the converted command data and test case ID into a text file and import it into the interface automation platform to update the use case.

9. The interface automation use case update system based on daily manual testing according to claim 8, characterized in that: In the traffic interception and collection module, non-intrusive network monitoring technology is used to store the collected interface request information in the form of structured data; the interface request information includes the requested URL, request method, request parameters, request header and request body; In the basic service deployment module, the configuration parameters of Kafka and Zookeeper are adjusted according to the system's network bandwidth, data processing volume, and response time requirements; In the data storage topic creation module, the number of partitions and the replication factor set when creating a topic are configured according to the data volume and read and write frequency of the interface request information; In the data capture writing module, the traffic capture tool realizes traffic capture based on network data packet parsing technology, and the network service log configuration stipulates the recording granularity and storage path of the log.

10. The interface automation use case update system based on daily manual testing according to claim 8, characterized in that: In the data conversion processing module, the conversion rules are customized according to the test case format supported by the interface automation platform, and the designated characters are encoded; In the test case identification generation module, the command data is normalized before the hash algorithm is used for calculation; In the use case data import and update module, the interface automation platform performs data format verification and integrity check on the file before importing the text file, including checking the number, type and order of data fields.

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