Automatic testing method and system for AI outbound system

Abstract

The invention relates to the technical field of automated testing, in particular to an automated testing method and system for an AI outbound system, and the method comprises the steps: designing and preparing an automated testing case of the AI outbound system, and pushing the automated testing case to a distributed storage database; pushing a test task based on the automated test case, and dialing the test task by using a specific resource prefix; a call request is initiated in response to pushing of the test task, and the call request is matched to a corresponding landing gateway through routing matching; monitoring a landing gateway event matched with the call request in real time, matching the called number with a test case in a distributed storage database, and processing the call request according to the matched test case; and asserting based on an expected result of the test case, and generating a detailed test report. The method can reduce the manual dependence, improve the test coverage rate and reduce the regression test cost.

Description

Technical Field

[0001] The present application relates to the field of automated testing technology, and in particular to an automated testing method and system for an AI outbound calling system. Background Art

[0002] The rapid development of artificial intelligence (AI) technology has promoted the widespread application of AI outbound call systems, which are widely used in marketing, customer service, smart reminders and other fields. In order to ensure the stability and accuracy of the AI ​​outbound call system, the testing link is crucial in the system development and optimization process. With the expansion of the scale of AI outbound call business and the increase in system complexity, the industry has put forward higher requirements for the accuracy, coverage and efficiency of testing. Therefore, how to improve the testing capabilities of AI outbound call systems has become one of the focuses of the industry.

[0003] The testing of existing AI outbound call systems mainly relies on manual phone calls for verification. Specifically, testers usually need to use real telephone lines to call the target mobile phone number to simulate the scenario of users answering AI outbound calls and observe whether the content of the call is as expected. During the test, testers need to manually record the test results and adjust the AI ​​outbound call system's speech recognition, semantic understanding, business logic and other parameters based on the test feedback. In addition, before upgrading the system or launching new functions, a large number of regression tests are usually required to ensure the stability and reliability of the system.

[0004] However, the testing method of the existing AI outbound call system mainly relies on manual phone calls for verification, resulting in low test efficiency, limited coverage, and difficulty in adapting to the rapid iteration and upgrade of the system. First, manual dialing tests consume a lot of manpower, and are affected by factors such as line quality and operator network fluctuations, and the uncertainty of the test results is high; secondly, AI outbound calls involve complex and diverse business scenarios, and manual testing is difficult to fully cover all situations, especially edge scenarios and special application scenarios are often easily overlooked; finally, with frequent system upgrades, each version update requires a large amount of regression testing, and traditional manual testing methods are costly and difficult to meet the needs of rapid iteration. Therefore, how to reduce manual dependence, improve test coverage and reduce regression testing costs have become key technical issues that need to be urgently solved in AI outbound call systems. Summary of the invention

[0005] This application provides an automated testing method and system for an AI outbound call system, which can reduce manual dependence, improve test coverage, and reduce regression testing costs. This application provides the following technical solutions:

[0006] In a first aspect, the present application provides an automated testing method for an AI outbound call system, the method comprising:

[0007] Design and prepare automated test cases for the AI ​​outbound call system and push them to the distributed storage database;

[0008] Push test tasks based on the automated test cases and dial test tasks using specific resource prefixes;

[0009] In response to the push of the test task, a call request is initiated, and the call request is matched to a corresponding landing gateway through routing matching;

[0010] Monitor the landing gateway events matched by the call request in real time, match the called number with the test cases in the distributed storage database, and process the call request according to the matched test cases;

[0011] Assertions are made based on the expected results of the test cases and a detailed test report is generated.

[0012] In a specific feasible implementation plan, the design and preparation of automated test cases for the AI ​​outbound call system and pushing them to a distributed storage database include:

[0013] The test cases include but are not limited to called number, conversation turn, conversation content, SIP signaling, call duration and expected results.

[0014] In a specific feasible implementation plan, the design and preparation of automated test cases for the AI ​​outbound call system and pushing them to a distributed storage database include:

[0015] Automated testing is performed based on the Pytest testing framework, and the Redis distributed storage database is used as the storage medium for test cases.

[0016] In a specific possible implementation scheme, initiating a call request in response to the push of the test task, and matching the call request to a corresponding landing gateway through route matching includes:

[0017] Initiate a call request based on FreeSWITCH, pass the dial number, conversation content and other parameters in the test case to FreeSWITCH, and initiate the call according to the configured business logic.

[0018] In a specific possible implementation scheme, initiating a call request in response to the push of the test task, and matching the call request to a corresponding landing gateway through route matching further includes:

[0019] Use OpenSIPS as a SIP routing server. After receiving a call initiation request, it will parse the request according to the predetermined routing rules.

[0020] OpenSIPS analyzes the relevant information of the request and selects the appropriate landing gateway based on the matching routing rules.

[0021] In a specific implementation scheme, the real-time monitoring of the landing gateway event matched by the call request is performed, the called number is matched with the test case in the distributed storage database, and the call request is processed according to the matched test case:

[0022] When the call request is successfully connected, the Java ESL Client obtains feedback information from the landing gateway and monitors events related to the landing gateway in real time;

[0023] After monitoring the call event, the Java ESL Client extracts the called number and matches it with the test cases in the distributed storage database;

[0024] After the Java ESL Client successfully matches the relevant test case, it controls the subsequent processing of the call according to the specific requirements in the test case.

[0025] In a specific implementation scheme, asserting the expected result based on the test case and generating a detailed test report includes:

[0026] During the execution of each test case, automatic assertions are performed based on the expected results preset in the case;

[0027] The Pytest framework compares the actual result with the expected result. If the two are consistent, the assertion passes, otherwise it reports failure.

[0028] After the assertion verification is completed, the Pytest framework automatically generates a test report.

[0029] In the second aspect, the present application provides an automated testing system for an AI outbound call system, which adopts the following technical solutions:

[0030] An automated testing system for an AI outbound calling system, comprising:

[0031] The test case generation module is used to design and prepare automated test cases for the AI ​​outbound call system and push them to the distributed storage database;

[0032] A test task push module, used to push test tasks based on the automated test cases and dial test tasks using a specific resource prefix;

[0033] A landing gateway matching module, used to initiate a call request in response to the push of the test task, and match the call request to the corresponding landing gateway through route matching;

[0034] The test case matching module is used to monitor the landing gateway events matched by the call request in real time, match the called number with the test case in the distributed storage database, and process the call request according to the matched test case;

[0035] The test report generation module is used to make assertions based on the expected results of the test cases and generate a detailed test report.

[0036] In a third aspect, the present application provides an electronic device, comprising a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to implement an automated testing method for an AI outbound call system as described in the first aspect.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium, wherein a program is stored in the storage medium, and when the program is executed by a processor, it is used to implement an automated testing method for an AI outbound call system as described in the first aspect.

[0038] In summary, the beneficial effects of this application include at least:

[0039] 1) Traditional AI outbound call system testing mainly relies on manual dialing. Testers need to manually make calls, record results, and analyze problems, which results in low testing efficiency and high costs. This application implements full-process automated testing through a series of technical means such as automated test case management, task scheduling, automatic dialing, real-time monitoring, automatic assertions, and report generation, greatly reducing manual intervention. The high concurrency of the Redis database enables fast access and scheduling of test tasks, and the event monitoring mechanism of the Java ESLClient ensures real-time response, allowing the entire test process to be executed in large-scale parallel execution, thereby significantly improving test efficiency and meeting the rapid iteration requirements of the AI ​​outbound call system.

[0040] 2) AI outbound calls involve complex technologies such as multiple rounds of conversations, voice recognition, semantic understanding, etc., and the business scenarios are diverse. Traditional manual testing is difficult to cover all situations. This application uses a structured test case management mechanism to ensure that different scenarios (such as different operators, different outbound call strategies, different network environments, etc.) can be included in the test scope. At the same time, the test tasks accurately match different landing gateways through the resource prefix mechanism to ensure the diversity of the test environment. In addition, call initiation and route matching based on FreeSWITCH and OpenSIPS can test call quality and connection stability in different environments, thereby greatly improving the test coverage and enabling the AI ​​outbound call system to adapt to a wider range of application scenarios.

[0041] 3) Due to human errors, unstable environment and other factors, the test results of traditional manual testing are easily disturbed, affecting reliability. This application uses the automated assertion mechanism of the Pytest framework to strictly compare core indicators such as call duration, voice interaction content, SIP signaling, etc. to ensure the accuracy of the test results. In addition, the Java ESL Client can monitor the call status and gateway feedback in real time to ensure the real-time and integrity of call events. Finally, through automated report generation, detailed test data, pass rate analysis, failure cause tracking, etc. can be provided to provide data support for system optimization and ensure the stability and reliability of the AI ​​outbound call system in complex application environments.

[0042] By designing and storing test cases, automatically pushing test tasks, initiating and routing call requests, monitoring call events in real time, and finally performing automated assertions and report generation, the entire process of automated testing of the AI ​​outbound call system is completed. First, this solution manages test cases based on the Redis distributed storage database to ensure efficient access and concurrent scheduling of test data; then, push test tasks and attach resource prefixes so that the AI ​​outbound call system can accurately match different test scenarios and correctly perform outbound call dialing tests; in the call initiation phase, use FreeSWITCH to initiate an outbound call request, and perform routing matching through OpenSIPS to ensure that the call can accurately access the target landing gateway; then, the Java ESL Client monitors call events, matches test cases according to the called number, and handles call interactions according to the requirements of the use case; finally, the Pytest framework executes automated assertions and generates test reports to ensure the accuracy and traceability of the test results. By building a complete automated testing system, this solution achieves the technical goals of reducing manual dependence, improving test coverage, and reducing regression testing costs.

[0043] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flowchart of an automated testing method for an AI outbound call system in an embodiment of the present application.

[0045] Figure 2 It is a schematic diagram of the overall process of the automated testing method for the AI ​​outbound call system in an embodiment of the present application.

[0046] Figure 3 It is a structural block diagram of the automated testing system for the AI ​​outbound call system in an embodiment of the present application.

[0047] Figure 4It is a block diagram of an electronic device used for automated testing of an AI outbound call system in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0049] Optionally, the present application uses the automated testing method for the AI ​​outbound call system provided in each embodiment as an example for use in an electronic device, where the electronic device is a terminal or a server. The terminal may be a mobile phone, a computer, a tablet computer, etc. This embodiment does not limit the type of electronic device.

[0050] Reference Figure 1 , is a flow chart of an automated testing method for an AI outbound call system provided by an embodiment of the present application, the method comprising at least the following steps:

[0051] Step S101: Design and prepare automated test cases for the AI ​​outbound call system, and push them to the distributed storage database.

[0052] Specifically, in the automated testing process of the AI ​​outbound call system, it is necessary to first design and prepare complete test cases to ensure test coverage and the degree of automation of execution. The management and calling of test cases are the core of automated testing. Therefore, this solution uses a distributed storage database as the storage and scheduling medium for test cases, so that test data can be efficiently accessed and supports the execution of concurrent test tasks.

[0053] In the design of test cases, it is necessary to consider the multi-round dialogue characteristics of the AI ​​outbound call system and ensure that the test can cover different application scenarios. Specifically, each test case contains at least the following core information:

[0054] Called number: used to simulate outbound calls to target users to ensure the authenticity of the call scenario;

[0055] Conversation rounds: Record the complete interaction process between the AI ​​outbound calling system and the called party to ensure that the system can correctly execute the multi-round conversation logic;

[0056] Conversation content: including the voice output of the AI ​​outbound call and the user's expected response, to verify the accuracy of voice recognition and semantic understanding;

[0057] SIP signaling: used to analyze the protocol interaction process of the call to ensure that the call initiation, connection and hang-up processes meet expectations;

[0058] Call duration: Ensure that the system does not experience abnormal interruptions or timeouts during the call;

[0059] Expected result: used for automated assertions in subsequent tests to determine whether the test passes.

[0060] In order to improve the execution efficiency of test tasks, this application performs automated testing based on the Pytest test framework, and combines the Redis distributed storage database as the storage medium for test cases. As a flexible and efficient unit testing framework, Pytest supports functions such as parameterized testing, test fixtures, and automated assertions, which can improve the maintainability and reusability of tests. At the same time, Redis, as a high-performance distributed storage database, can achieve fast access to test cases and support high-concurrency test task scheduling, thereby improving the execution efficiency of tests. After the test case design is completed, all test cases are pushed to the Redis database for subsequent test task calls.

[0061] Step S102: Push a test task based on the automated test case, and dial the test task using a specific resource prefix.

[0062] During the test task scheduling phase, the corresponding test tasks are generated and pushed to the AI ​​outbound calling platform based on the automated test cases stored in the Redis database. The test tasks are used to control the AI ​​outbound calling platform to initiate calls according to the preset test scenarios and verify whether the actual performance of the system meets expectations.

[0063] In addition, to ensure that the test task matches the correct execution environment, a specific resource prefix will be added to each test task when pushing the task to distinguish different network operators, test batches or call lines. After dialing the test task, the AI ​​outbound call system will match the appropriate landing gateway based on the resource prefix and formally initiate the outbound call to enter the next call processing stage.

[0064] Step S103: In response to the push of the test task, a call request is initiated, and the call request is matched to the corresponding landing gateway through routing matching.

[0065] In step S103, when the test task is successfully pushed and scheduled, the outbound call process is started through FreeSWITCH. As a communication platform, FreeSWITCH receives the dialing instructions from the scheduling system and initiates a real call request. This process includes passing the dialing number, conversation content and other parameters in the test case to FreeSWITCH, and then initiating the call according to the configured business logic to ensure that the task can correctly trigger the dialing action. After FreeSWITCH initiates the call request, OpenSIPS, as a SIP routing server, parses the request according to the predetermined routing rules after receiving the call request. OpenSIPS will analyze the relevant information of the request and select the appropriate landing gateway according to the matching routing rules. This process ensures that the call can be made in the correct network environment and hardware equipment, so that the test can simulate the real outbound call scenario and verify the call quality and stability in different environments.

[0066] Step S104: monitor the landing gateway event matched by the call request in real time, match the called number with the test case in the distributed storage database, and process the call request according to the matched test case.

[0067] In step S104, after the call is initiated and the route matching is completed, the Java ESL Client is used to monitor the events of the landing gateway in real time, and the called number is matched with the test case in the distributed storage database to control and process the call request. Java ESL Client is an event monitoring library provided by FreeSWITCH, which can subscribe to and monitor all call events of the FreeSWITCH server, including call connection, hang up, call duration, DTMF key input, etc.

[0068] Specifically, the Java ESL Client will monitor events related to the landing gateway in real time, such as call connection status, SIP signaling changes, etc. When the call request is successfully connected, the Java ESL Client immediately obtains feedback information from the landing gateway to ensure that the test process can be operated at the correct time. After monitoring the call event, the Java ESL Client will extract the called number and match it with the test cases in the distributed storage database. Each test case contains information such as the called number, conversation content, and expected results. By matching the called number, the corresponding test case is found to provide specific test guidance for subsequent call processing.

[0069] After successfully matching the relevant test case, the Java ESL Client will control the subsequent processing of the call according to the specific requirements of the test case. For example, it may execute preset voice interactions, send specified SIP signaling, verify the quality of the call, etc. In this way, the call process can be dynamically adjusted according to the specific content of each test case, thereby fully verifying the functions and performance of the AI ​​outbound call system.

[0070] Step S105: make assertions based on the expected results of the test case and generate a detailed test report.

[0071] In step S105, after completing the call processing and event monitoring, assertion verification is performed according to the expected results of each test case, and a detailed test report is generated.

[0072] Specifically, during the execution of each test case, automated assertions are performed based on the expected results preset in the case (such as call status, voice interaction content, duration, etc.). The Pytest framework will compare the actual results with the expected results. If the two are consistent, the assertion passes, otherwise the report fails. This ensures that the AI ​​outbound call system can achieve the expected results in actual applications. After the assertion verification is completed, the Pytest framework automatically generates a detailed test report, which includes information such as the execution results of each test case, the test pass rate, failed cases and their reasons. Through the generated test report, testers can comprehensively evaluate whether the functions of the AI ​​outbound call system meet the requirements and identify possible defects or optimization space. At this point, the automated testing process of the entire AI outbound call system is completed, providing strong support for the stability verification, troubleshooting and subsequent improvements of the AI ​​outbound call system.

[0073] In summary, combined with Figure 2, by designing and storing test cases, automatically pushing test tasks, initiating and routing call requests, monitoring call events in real time, and finally performing automated assertions and report generation, the entire process of automated testing of the AI ​​outbound call system is completed. First, this solution manages test cases based on the Redis distributed storage database to ensure efficient access and concurrent scheduling of test data; then, push test tasks and attach resource prefixes so that the AI ​​outbound call system can accurately match different test scenarios and correctly perform outbound call dialing tests; in the call initiation phase, use FreeSWITCH to initiate outbound call requests, and perform routing matching through OpenSIPS to ensure that the call can accurately access the target landing gateway; then, the Java ESL Client monitors call events, matches test cases according to the called number, and handles call interactions according to the requirements of the use case; finally, the Pytest framework executes automated assertions and generates test reports to ensure the accuracy and traceability of test results. By building a complete automated testing system, this solution achieves the technical goals of reducing manual dependence, improving test coverage, and reducing regression testing costs.

[0074] In order to solve the problems of low efficiency, limited coverage, and high cost of regression testing in manual testing, this application introduces automation technology in the entire testing process to improve testing efficiency and accuracy. First, the design and storage of test cases ensure the standardized management of test data, so that test tasks can be dynamically scheduled; then, task push based on resource prefixes ensures that test tasks can match the correct execution environment and accurately control outbound call requests; in the call execution stage, FreeSWITCH is combined with OpenSIPS to achieve efficient call initiation and route matching, ensuring that the test can cover different network environments; at the same time, Java ESL Client monitors landing gateway events to ensure that test tasks can accurately match called numbers and perform interactive verification according to test case requirements; finally, Pytest automates assertions and report generation to ensure that test results are quantifiable and traceable, further improving the maintainability of the test. The entire solution covers the entire process from test case management to call execution and result verification through an automated testing process, effectively improving the testing capabilities of the AI ​​outbound call system and meeting the industry's needs for efficient, accurate, and low-cost testing.

[0075] Figure 3 This is a structural block diagram of an automated testing system for an AI outbound call system provided by an embodiment of the present application. The system includes at least the following modules:

[0076] The test case generation module is used to design and prepare automated test cases for the AI ​​outbound call system and push them to the distributed storage database;

[0077] The test task push module is used to push test tasks based on automated test cases and dial test tasks using specific resource prefixes;

[0078] The landing gateway matching module is used to initiate a call request in response to the push of the test task, and match the call request to the corresponding landing gateway through routing matching;

[0079] The test case matching module is used to monitor the landing gateway events matched by the call request in real time, match the called number with the test case in the distributed storage database, and process the call request according to the matched test case;

[0080] The test report generation module is used to make assertions based on the expected results of the test cases and generate detailed test reports.

[0081] For relevant details, refer to the above method embodiment.

[0082] Figure 4 4 is a block diagram of an electronic device provided by an embodiment of the present application. The device at least includes a processor 401 and a memory 402.

[0083] The processor 401 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 401 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 401 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0084] The memory 402 may include one or more computer-readable storage media, which may be non-transitory. The memory 402 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 402 is used to store at least one instruction, which is used to be executed by the processor 401 to implement the automated testing method for the AI ​​outbound call system provided in the method embodiment of the present application.

[0085] In some embodiments, the electronic device may further optionally include: a peripheral device interface and at least one peripheral device. The processor 401, the memory 402 and the peripheral device interface may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface via a bus, a signal line or a circuit board. Schematically, the peripheral devices include but are not limited to: a radio frequency circuit, a touch display screen, an audio circuit, and a power supply.

[0086] Of course, the electronic device may also include fewer or more components, which is not limited in this embodiment.

[0087] Optionally, the present application also provides a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the automated testing method for an AI outbound call system of the above method embodiment.

[0088] Optionally, the present application also provides a computer product, which includes a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the automated testing method for an AI outbound call system of the above method embodiment.

[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. An automated testing method for an AI outbound call system, characterized in that: The method comprises: Design and prepare automated test cases for the AI ​​outbound call system and push them to the distributed storage database; Push test tasks based on the automated test cases and dial test tasks using specific resource prefixes; In response to the push of the test task, a call request is initiated, and the call request is matched to a corresponding landing gateway through routing matching; Monitor the landing gateway events matched by the call request in real time, match the called number with the test cases in the distributed storage database, and process the call request according to the matched test cases; Assertions are made based on the expected results of the test cases and a detailed test report is generated.

2. The automated testing method for an AI outbound calling system according to claim 1, characterized in that: The design and preparation of automated test cases for the AI ​​outbound call system and pushing them to the distributed storage database include: The test cases include but are not limited to called number, conversation turn, conversation content, SIP signaling, call duration and expected results.

3. The automated testing method for an AI outbound calling system according to claim 1, characterized in that: The design and preparation of automated test cases for the AI ​​outbound call system and pushing them to the distributed storage database include: Automated testing is performed based on the Pytest testing framework, and the Redis distributed storage database is used as the storage medium for test cases.

4. The automated testing method for an AI outbound calling system according to claim 1, characterized in that: The initiating a call request in response to the push of the test task, and matching the call request to a corresponding landing gateway through route matching includes: Initiate a call request based on FreeSWITCH, pass the dial number, conversation content and other parameters in the test case to FreeSWITCH, and initiate the call according to the configured business logic.

5. The automated testing method for an AI outbound calling system according to claim 4, characterized in that: The initiating a call request in response to the push of the test task, and matching the call request to a corresponding landing gateway through route matching also includes: OpenS IPS is used as a SIP routing server. After receiving a call initiation request, it parses the request according to the predetermined routing rules; OpenS IPS analyzes the relevant information of the request and selects the appropriate landing gateway based on the matching routing rules.

6. The automated testing method for an AI outbound calling system according to claim 1, characterized in that: The real-time monitoring of the landing gateway events matched by the call request is performed, the called number is matched with the test case in the distributed storage database, and the call request is processed according to the matched test case: When the call request is successfully connected, the Java ESL Client obtains feedback information from the landing gateway and monitors events related to the landing gateway in real time; After monitoring the call event, the Java ESL Client extracts the called number and matches it with the test cases in the distributed storage database; After the Java ESL Client successfully matches the relevant test case, it controls the subsequent processing of the call according to the specific requirements in the test case.

7. The automated testing method for an AI outbound calling system according to claim 1, characterized in that: The assertion based on the expected result of the test case and the generation of a detailed test report include: During the execution of each test case, automatic assertions are performed based on the expected results preset in the case; The Pytest framework compares the actual result with the expected result. If the two are consistent, the assertion passes, otherwise it reports failure. After the assertion verification is completed, the Pytest framework automatically generates a test report.

8. An automated testing system for an AI outbound calling system, characterized in that: include: The test case generation module is used to design and prepare automated test cases for the AI ​​outbound call system and push them to the distributed storage database; A test task push module, used to push test tasks based on the automated test cases and dial test tasks using a specific resource prefix; A landing gateway matching module, used to initiate a call request in response to the push of the test task, and match the call request to the corresponding landing gateway through route matching; The test case matching module is used to monitor the landing gateway events matched by the call request in real time, match the called number with the test case in the distributed storage database, and process the call request according to the matched test case; The test report generation module is used to make assertions based on the expected results of the test cases and generate a detailed test report.

9. An electronic device, characterized in that: The device includes a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to implement an automated testing method for an AI outbound call system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, which, when executed by a processor, is used to implement an automated testing method for an AI outbound call system as described in any one of claims 1 to 7.

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