Software integration test automation calibration system and method

Abstract

This invention provides an automated calibration system and method for software integration testing, relating to the field of software testing technology. The system involves a test requirement parsing module that analyzes software requirement documents to obtain requirement analysis results and generate a test case framework. A test case generation module generates test case scripts based on the test case framework. A test execution engine module configures corresponding test tasks according to the test case scripts and sets task scheduling information. Test tasks are executed according to the task scheduling information to obtain test results. A test result analysis module analyzes the test results to obtain test adjustment strategies. These strategies are then sent to an automated calibration and optimization module, which adjusts the test process and / or test case scripts according to the strategies. The adjusted test case scripts are then sent to the test execution engine module for execution. This system addresses the problem of traditional integration testing methods heavily relying on manual intervention, which limits the efficiency and effectiveness of automated testing. It improves the efficiency and effectiveness of automated testing.

Description

Technical Field

[0001] This invention relates to the field of software testing technology, and in particular to an automated calibration system and method for software integration testing. Background Technology

[0002] In software development, integration testing is a crucial step in ensuring the seamless collaboration between modules. Integration testing (also called assembly testing or joint testing) is a logical extension of unit testing, aiming to verify whether the interactions between different modules or components in a system meet expectations. While unit testing typically focuses on the correct functionality of individual modules or functions, the purpose of integration testing is to examine how these modules work together after integration, ensuring they can work seamlessly and preventing failures caused by issues with interfaces, data transfer, or resource sharing.

[0003] Currently, traditional integration testing methods include: combining modules that have already undergone unit testing into components, testing the interfaces between components to ensure they can work together correctly; then, gradually expanding to larger modules and components to ensure smooth integration of each module and component; finally, combining all relevant modules and components into a complete process for testing; and in the case where the program consists of multiple processes, testing each process one by one.

[0004] However, traditional integration testing methods rely heavily on manual labor. From test case design to test execution and result analysis, the entire process is time-consuming, labor-intensive, and prone to errors. Although some automated testing tools exist, they still face problems such as insufficient test case design, complex test environment configuration, and inaccurate test result analysis when dealing with the integration testing of complex software systems, which limits the efficiency and effectiveness of automated testing. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide an automated calibration system and method for software integration testing to eliminate or improve one or more defects existing in the prior art. It can solve the problem that traditional integration testing methods heavily rely on manual labor, and that the efficiency and effectiveness of automated testing are limited.

[0006] One aspect of the present invention provides an automated calibration system for software integration testing, the system comprising: a test requirement parsing module, a test case generation module, a test execution engine module, a test result analysis module, and an automatic calibration and optimization module;

[0007] The test requirements parsing module is used to parse the software requirements document and obtain the requirements parsing results; based on the requirements parsing results, it generates a test case framework and sends the test case framework to the test case generation module; the requirements parsing results include functional point information, interface information, and data flow information;

[0008] The test case generation module is used to generate test case scripts based on the test case framework, historical test data, industry verification data, and / or machine learning-generated data upon receiving a test case framework; and to send the test case scripts to the test execution engine module; the test case scripts include normal process test case scripts, abnormal process test case scripts, and boundary condition test case scripts.

[0009] The test execution engine module is used to configure the corresponding test tasks according to the test case scripts received, and set the task scheduling information; execute the test tasks according to the task scheduling information, obtain the test results; and send the test results to the test result analysis module.

[0010] The test result analysis module is used to analyze the test results upon receiving them using natural language processing and data mining techniques to obtain test adjustment strategies. These strategies are then sent to the automatic calibration and optimization module. The test adjustment strategies include test case adjustment strategies for test case scripts and test process adjustment strategies for optimizing the test process.

[0011] The automatic calibration and optimization module is used to adjust the test process and / or test case scripts according to the test adjustment strategy when a test adjustment strategy is received; and to send the adjusted test case scripts to the test execution engine module so that the test execution engine module can execute the test process according to the adjusted test process.

[0012] In some embodiments of the present invention, the system further includes a test environment configuration module; the task scheduling information includes environment configuration information;

[0013] The test execution engine module is also used to send environment configuration information to the test environment configuration module;

[0014] The test environment configuration module is used to configure the test environment corresponding to the test case scripts according to the received environment configuration information. The environment configuration information includes database configuration information, network service configuration information, and third-party service dependency configuration information.

[0015] In some embodiments of the present invention, the test environment configuration module further includes a simulated virtual submodule;

[0016] The simulated virtual submodule is used to configure high-concurrency and / or low-resource test scenarios using virtualization technology.

[0017] In some embodiments of the present invention, the test execution engine module includes a test submodule and a recording submodule;

[0018] The testing submodule integrates testing tools for automatically executing test tasks;

[0019] The recording submodule is used to record the testing process and results, including test steps, test logs, and test screenshots.

[0020] In some embodiments of the present invention, the testing tools include user interface automation testing tools, performance automation testing tools, and interface automation testing tools.

[0021] In some embodiments of the present invention, the test case adjustment strategy includes a strategy of increasing the test case coverage scenarios or a strategy of adjusting the test case priority; the test process adjustment strategy includes a strategy of adjusting the test order or a strategy of increasing concurrent tests.

[0022] The test adjustment strategy includes strategies to increase concurrent testing, automatic calibration optimization modules, and test scenarios for configuring concurrent testing through simulated virtual submodules.

[0023] In some embodiments of the present invention, the test result analysis module is further used to perform defect analysis on the test results using natural language processing technology and data mining technology, obtain defect analysis results, and associate the defect analysis results with the defect management system.

[0024] In some embodiments of the present invention, the test result analysis module is also used to generate an automated test report based on the test results and a preset test report template.

[0025] In some embodiments of the present invention, the system integrates an automated testing equipment system.

[0026] Another aspect of the present invention provides an automated calibration method for software integration testing, applied to the aforementioned automated calibration system for software integration testing, the method comprising the following steps:

[0027] The software requirements document is parsed by the test requirements parsing module to obtain the requirements parsing results. Based on the requirements parsing results, a test case framework is generated and sent to the test case generation module. The requirements parsing results include functional point information, interface information, and data flow information.

[0028] The test case generation module receives the test case framework and generates test case scripts based on the test case framework, historical test data, industry validation data, and / or machine learning-generated data. The test case scripts are then sent to the test execution engine module. The test case scripts include normal process test case scripts, abnormal process test case scripts, and boundary condition test case scripts.

[0029] The test execution engine module receives test case scripts, configures corresponding test tasks based on the test case scripts, sets task scheduling information, executes test tasks according to the task scheduling information, obtains test results, and sends the test results to the test result analysis module.

[0030] The test results are received by the test result analysis module, and defect analysis is performed on the test results using natural language processing and data mining techniques to obtain test adjustment strategies. These strategies are then sent to the automatic calibration and optimization module. The test adjustment strategies include test case adjustment strategies for test case scripts and test process adjustment strategies for optimizing the test process.

[0031] The automatic calibration and optimization module receives test adjustment strategies, adjusts the test process and / or test case scripts according to the test adjustment strategies, and sends the adjusted test case scripts to the test execution engine module so that the test execution engine module executes according to the adjusted test process.

[0032] The software integration testing automated calibration system and method of this invention can solve the problem that traditional integration testing methods rely heavily on manual labor, resulting in limited efficiency and effectiveness of automated testing. Through the software integration testing automated calibration system, test requirements can be automatically parsed by the requirement parsing module, quickly extracting key information such as functional points, interface information, and data flows. Based on these parsing results, the system can automatically generate test case frameworks and send them to the test case generation module. This module uses historical test data, industry verification data, and machine learning technology to automatically generate various types of test case scripts (including normal flow, abnormal flow, and boundary condition test case scripts). These test case scripts are then sent to the test execution engine module for automated execution. The test execution engine module executes relevant test tasks according to task scheduling information, automatically generates and collects test results. These results are further analyzed for defects by the test result analysis module, which uses natural language processing and data mining techniques to accurately identify potential defects and generate corresponding test adjustment strategies. These strategies not only involve adjusting test case scripts but also optimizing the test process. Finally, the automatic calibration and optimization module automatically optimizes the test process and test case scripts based on the generated adjustment strategies. The adjusted scripts are then retransmitted to the test execution engine module to ensure that test tasks are executed according to the optimized strategies, thereby continuously improving test effectiveness. Through this automated closed-loop test process, the problem of manual intervention in traditional integration testing methods is solved, the efficiency and effectiveness of automated testing are greatly improved, the test cycle is significantly shortened, and software defects can be more accurately discovered and targeted repair suggestions can be provided, thereby comprehensively improving software quality.

[0033] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the description and drawings.

[0034] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. In the drawings:

[0036] Figure 1 This is a schematic diagram of the structure of an automated calibration system for software integration testing provided in an embodiment of the present invention;

[0037] Figure 2 A flowchart of an automated calibration method for software integration testing provided in an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0039] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0040] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0041] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0042] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0043] like Figure 1The diagram shows a schematic of the structure of an automated calibration system for software integration testing provided in an embodiment of this application. In some embodiments of the present invention, the automated calibration system for software integration testing integrates an Automated Test Equipment System (ATE). An ATE refers to a system that uses a combination of hardware and software to perform automated testing of a software system using testing equipment.

[0044] according to Figure 1 As can be seen, the software integration test automated calibration system provided in the embodiments of this application includes a test requirement parsing module 110, a test case generation module 120, a test execution engine module 130, a test result analysis module 140, and an automatic calibration optimization module 150.

[0045] The test requirements parsing module 110 refers to a module or electronic device used to parse the Software Requirements Specification (SRS) and generate a test case framework based on the parsing results. This electronic device can be a mobile phone, laptop, or server; this embodiment does not limit the type of electronic device.

[0046] In this context, a software requirements document is a document that provides a detailed description of the requirements for a software system. Its purpose is to accurately convey the functionalities, performance, design constraints, and other requirements that the software system should possess. In some embodiments of this invention, the software requirements document includes, but is not limited to, the software requirements specification and the software functional requirements document.

[0047] The test requirements parsing module 110 parses the software requirements document, including syntax parsing and semantic parsing. Syntax parsing identifies key terms, formats, and structures in the software requirements document, while semantic parsing understands the meaning of the content in the software requirements document, thus obtaining the requirements parsing results.

[0048] In some embodiments of the present invention, syntax parsing and semantic parsing can be implemented using pre-configured automated tools. These automated tools for syntax parsing include, but are not limited to, ANTLR parsers, Yacc parsers, or Bison parsers; while the automated tools for semantic parsing include, but are not limited to, Natural Language Toolkit (NLTK) or Bidirectional Encoder Representations from Transformers (BERT).

[0049] After the requirements analysis module 110 obtains the requirements analysis results, a test case framework is generated based on these results. The test case framework is a specific test structure designed based on the requirements analysis results. It is implemented using a specific standard (such as JUnit, TestNG, Selenium, etc.) and includes the basic structure, execution order, and expected results of the test cases.

[0050] Specifically, the test requirement parsing module 110 is used to parse the software requirement document and obtain the requirement parsing result; based on the requirement parsing result, a test case framework is generated and sent to the test case generation module 120.

[0051] In some embodiments of the present invention, the requirements parsing results include function point information, interface information, and data flow information. Accordingly, the test case framework includes function point test cases, interface test cases, and data flow test cases.

[0052] Function point information refers to the information of each independent functional module or operation in the system. The requirements analysis module needs to identify the various functions that the system needs to implement from the software requirements document, and divide the system into functional modules according to the functional requirements, such as login function, payment function, report generation function, etc.

[0053] Interface information includes API interface information and inter-system interface information. API interfaces refer to the system interfaces described in the software requirements document identified by the test requirements parsing module 110, including inputs, outputs, data formats, protocols, etc.; inter-system interfaces refer to the communication protocols and data exchange interfaces between different systems or subsystems in the software requirements document identified by the test requirements parsing module 110.

[0054] Data flow information refers to the path and processing of data within a system. It includes information about input and output data, data transmission methods, storage paths, and data dependencies between modules.

[0055] In actual implementation, the requirements analysis results may also include other information, such as constraint information. This embodiment does not limit the amount or type of information in the requirements analysis results.

[0056] In some embodiments of the present invention, the test case generation module 120 is connected to the test requirement parsing module 110. The test requirement parsing module 110 and the test case generation module 120 can be different modules in the same device, or they can be independent electronic devices with established communication connections (including wireless communication connections or wired communication connections). This embodiment does not limit the connection method between the test requirement parsing module 110 and the test case generation module 120.

[0057] In some embodiments of the present invention, the test case generation module 120 is used to generate test case scripts based on the test case framework sent by the test requirement parsing module 110.

[0058] The test case scripts include normal process test case scripts, abnormal process test case scripts, and boundary condition test case scripts. Normal process test case scripts test the system's behavior under normal conditions, ensuring the system functions as expected. Abnormal process test case scripts test the system's performance when encountering invalid data or abnormal situations, ensuring the system can correctly handle these exceptions and provide appropriate error messages. Boundary condition test case scripts verify the system's performance under extreme conditions.

[0059] In some embodiments of the present invention, the test case generation module 120 can combine multiple data sources to generate high-quality test case scripts during the process of generating test case scripts. The multiple data sources include historical test data, industry verification data, and machine learning generated data generated through machine learning models.

[0060] Historical test data is data obtained from previous tests, and typically includes the execution results of test steps (including successful, failed, skipped, etc.), input data (common input parameters, valid and invalid test data, boundary conditions, etc.), expected and actual results, etc. By analyzing historical test data, it is possible to identify common operation steps, input data types, and key testing areas in test cases.

[0061] Industry validation data refers to data obtained from validation tests conducted by industry standards, best practices, or other organizations. This data provides standard testing patterns for a specific domain, helping to generate test cases that better meet industry requirements.

[0062] Machine learning generated data refers to possible input data generated using machine learning models (such as generative adversarial networks (GANs) or natural language generation models). The machine learning model can be a machine learning model trained using historical test data or industry validation data.

[0063] Specifically, the test case generation module 120 is used to generate test case scripts based on the test case framework, historical test data, industry verification data and / or machine learning generated data after receiving the test case framework; and send the test case scripts to the test execution engine module 130.

[0064] In some embodiments of the present invention, the test case generation module 120 is connected to the test execution engine module 130. The test execution engine module 130 and the test case generation module 120 can be different modules in the same device, or they can be independent electronic devices with established communication connections (including wireless communication connections or wired communication connections). This embodiment does not limit the connection method between the test execution engine module 130 and the test case generation module 120.

[0065] After receiving the test case script, the test execution engine module 130 imports the test case script, configures the corresponding test tasks according to the test cases, and sets the task scheduling information.

[0066] Specifically, the test execution engine module 130 is used to configure the corresponding test tasks according to the test case script when it receives the test case script, and set the task scheduling information; execute the test tasks according to the task scheduling information, obtain the test results; and send the test results to the test result analysis module 140.

[0067] In some embodiments of the present invention, such as Figure 1 As shown, the test execution engine module 130 includes a test submodule 1310 and a recording submodule 1320; wherein, the test submodule 1310 integrates test tools for automatically executing test tasks.

[0068] In some embodiments of the present invention, the testing tools include user interface automation testing tools, performance automation testing tools, and interface automation testing tools. For example, user interface automation testing tools include Selenium; performance automation testing tools include JMeter; and interface automation testing tools include Postman.

[0069] In some embodiments of the present invention, a recording submodule 1320 is used to record the test process and test results, including test steps, test logs and test screenshots.

[0070] In some embodiments of the present invention, the task scheduling information includes key information such as the execution time, order, priority, resource requirements, and failure handling of test tasks. The test execution engine module 130 uses this information to rationally arrange the execution of test tasks, ensuring that the testing process is efficient, orderly, and reliable.

[0071] In other embodiments of the present invention, the task scheduling information further includes environment configuration information, such as... Figure 1 As shown, the software integration testing automated calibration system provided in the embodiments of this application also includes a test environment configuration module 160. The test environment configuration module 160 is used to configure the test environment corresponding to the test task according to the environment configuration information to ensure the successful completion of the test task.

[0072] Specifically, the test execution engine module 130 is also used to send environment configuration information to the test environment configuration module 160. The test environment configuration module 160, upon receiving the environment configuration information, configures the test environment corresponding to the test case scripts according to the environment configuration information.

[0073] In some embodiments of the present invention, the environment configuration information includes database configuration information, network service configuration information, and third-party service dependency configuration information.

[0074] The database configuration information includes database type (e.g., MySQL, Oracle), database address information, database port information, database name information, database username and password information, connection pool configuration information, etc.; the network service configuration information includes service port information, network protocol information, DNS configuration information, firewall configuration information, etc.; and the third-party service dependency configuration information includes third-party authentication information, API address information, etc.

[0075] Furthermore, in some embodiments of the present invention, such as Figure 1 As shown, the test environment configuration module 160 also includes a simulation virtual submodule 1610, which is used to configure test scenarios, including high-concurrency test scenarios and low-resource test scenarios. The high-concurrency test scenario simulates a large number of users simultaneously making requests, operations, or accesses in the system. The purpose of this scenario is to test the system's performance, response time, stability, and reliability when facing a large number of concurrent requests. The low-resource test scenario simulates the system or application running under resource-constrained conditions (such as CPU, memory, network bandwidth, etc.), aiming to test the system's performance under resource shortages, including responsiveness, stability, and fault tolerance.

[0076] Specifically, the virtual simulation submodule 1610 is used to configure high-concurrency and / or low-resource test scenarios through virtualization technology.

[0077] Virtualization technology refers to the technology of using software to simulate hardware resources (such as servers, storage, networks, etc.) to create multiple virtual instances on physical resources, including virtual machines, containerization, and virtual networks.

[0078] In some embodiments of the present invention, multiple virtual machines (or containers) are created using a virtualization platform through virtualization technology. Each virtual machine / container can simulate a user or request, simulating a high-concurrency test scenario where a large number of users or requests simultaneously access the system. By using virtual machine resources or limiting containerized resources, bandwidth and network latency are simulated to achieve the configuration of a low-resource test scenario.

[0079] After the test execution engine module 130 obtains the test results, it sends the test results to the test result analysis module 140. The test result analysis module 140 analyzes the test results and determines the test adjustment strategy.

[0080] In some embodiments of the present invention, the test execution engine module 130 is connected to the test result analysis module 140. The test execution engine module 130 and the test result analysis module 140 may be different modules in the same device, or they may be independent electronic devices with established communication connections (including wireless communication connections or wired communication connections). This embodiment does not limit the connection method between the test execution engine module 130 and the test result analysis module 140.

[0081] Specifically, the test result analysis module 140 is used to analyze the test results using natural language processing and data mining techniques upon receiving the test results, obtain a test adjustment strategy, and send the test adjustment strategy to the automatic calibration and optimization module 150.

[0082] For the structured data in the test results, natural language processing and data mining techniques are used to analyze the test results, including but not limited to: using statistical methods (such as standard deviation) to detect outliers in the results; or using machine learning algorithms (such as K-Means clustering, isolated forest, LOF, etc.) to identify abnormal patterns; or using association rule algorithms (such as Apriori, FP-growth) to discover the correlation between different events in the test; or clustering the test results (such as performance data, response time, etc.) to analyze the performance differences between different categories.

[0083] For unstructured data in the test results, natural language processing and data mining techniques are used to analyze the test results, including but not limited to: using algorithms such as TF-IDF or Word2Vec to extract keywords from the test result text and identify key issues; or using algorithms such as Latent Dirichlet Allocation (LDA) to perform topic modeling on the text in the test results and identify the main issues and concerns discussed in the report.

[0084] In some embodiments of the present invention, the test adjustment strategy includes a test case adjustment strategy for test case scripts and a test process adjustment strategy for optimizing the test process. Specifically, the test case adjustment strategy includes strategies to increase the test case coverage scenarios or adjust the test case priority; the test process adjustment strategy includes strategies to adjust the test order or increase concurrent testing.

[0085] In some embodiments of the present invention, the test tool integrated in the test execution engine module 130 is Selenium, and it satisfies Selenium = Selenium IDE + Selenium Grid + Selenium RC. In this case, the test case adjustment strategy includes the strategy of increasing the test case coverage scenarios, and the test process adjustment strategy includes the strategy of adjusting the test order.

[0086] In some other embodiments of the present invention, the test tool integrated in the test execution engine module 130 is the JMeter tool. The relevant formulas of the JMeter tool include the calculation formulas for Ramp-Up time, Transactions Per Second (TPS), Queries Per Second (QPS), Concurrency, and Pageviews (PV). Ramp-Up time represents the interval between each request, calculated as: Number of Threads / Ramp-Up time. A Ramp-Up time of 0 indicates a concurrent request time of 12. TPS is calculated as: Number of completed transactions / Time taken to complete these transactions. If a transaction controller is used, TPS is calculated as: Number of completed transactions / Time taken to complete these transactions. If no transaction controller is used, TPS is calculated as: Number of completed requests / Time taken to complete these requests 12. QPS is calculated as: Number of completed requests / Time taken to complete these requests. The difference between QPS and TPS is that QPS considers cases where each request involves multiple transactions, such as a page requesting the server multiple times 2.

[0087] When the test tool integrated in the test execution engine module 130 is Jmeter, the test case adjustment strategy includes the strategy of adjusting the priority of test cases, and the test process adjustment strategy includes the strategy of increasing concurrent tests.

[0088] In other embodiments of the present invention, the test tool integrated in the test execution engine module 130 is the Postman tool, and the status code assertion formula of the Postman tool is:

[0089] textCopy Code

[0090] pm.test("Status code is 200",function(){

[0091] pm.response.to.have.status(200);

[0092] });

[0093] The string assertion formula in Postman is:

[0094] textCopy Code

[0095] pm.test("Body matches string",function(){

[0096] pm.expect(pm.response.text()).to.include("string_you_want_to_search");

[0097] }).

[0098] When the test tool integrated in the test execution engine module 130 is Postman, the test case adjustment strategy includes the strategy of increasing the test case coverage scenarios, and the test process adjustment strategy includes the strategy of increasing concurrent testing.

[0099] In some embodiments of the present invention, after obtaining the test results, it is necessary to populate the test results into a preset report template to generate a test report. The report template defines the structure and format of the report, and the test results are embedded into the report template through an automated script. The automated script includes, for example, embedding information such as the execution status of the test cases, failure reasons, and screenshots into an HTML template to generate a detailed test report.

[0100] Specifically, the test result analysis module 140 is also used to generate automated test reports based on the test results and preset test report templates.

[0101] In practical implementation, to systematically track, manage, and resolve software defects, improve the efficiency and quality of defect management, reduce risks, enhance collaboration and transparency, and provide data support for decision-making, it is also necessary to associate the defects obtained in the test results analysis process with the Defect Management System. Based on this, the test result analysis module 140 is also used to perform defect analysis on the test results using natural language processing and data mining techniques, obtain defect analysis results, and associate these results with the Defect Management System.

[0102] In some embodiments of the present invention, the automatic calibration optimization module 150 is used to adjust the test process and / or test case scripts according to the test adjustment strategy when a test adjustment strategy is received; and send the adjusted test case scripts to the test execution engine module 130 so that the test execution engine module 130 executes the test process according to the adjusted test process.

[0103] In addition, when the test adjustment strategy includes a strategy to increase concurrent testing, the automatic calibration optimization module 150 is also used to configure the test scenario for concurrent testing through the simulated virtual submodule 1610.

[0104] In summary, the software integration testing automated calibration system provided in this embodiment can solve the problem that traditional integration testing methods heavily rely on manual labor, resulting in limited efficiency and effectiveness of automated testing. Through this system, test requirements can be automatically parsed by the requirement parsing module, quickly extracting key information such as functional points, interface information, and data flows. Based on these parsing results, the system can automatically generate test case frameworks and send them to the test case generation module. This module utilizes historical test data, industry verification data, and machine learning techniques to automatically generate various types of test case scripts (including normal flow, abnormal flow, and boundary condition test case scripts). These test case scripts are then sent to the test execution engine module for automated execution. The test execution engine module executes relevant test tasks according to task scheduling information, automatically generating and collecting test results. These results are further analyzed for defects by the test result analysis module, which uses natural language processing and data mining techniques to accurately identify potential defects and generate corresponding test adjustment strategies. These strategies not only involve adjusting test case scripts but also optimizing the test process. Finally, the automatic calibration and optimization module automatically optimizes the test process and test case scripts based on the generated adjustment strategies. The adjusted scripts are then retransmitted to the test execution engine module to ensure that test tasks are executed according to the optimized strategies, thereby continuously improving test effectiveness. Through this automated closed-loop test process, the problem of manual intervention in traditional integration testing methods is solved, the efficiency and effectiveness of automated testing are greatly improved, the test cycle is significantly shortened, and software defects can be more accurately discovered and targeted repair suggestions can be provided, thereby comprehensively improving software quality.

[0105] This embodiment provides an automated calibration method for software integration testing, such as... Figure 2 As shown, the method includes at least steps S201 to S205:

[0106] Step S201: The software requirements document is parsed by the test requirements parsing module to obtain the requirements parsing results, and a test case framework is generated based on the requirements parsing results. The test case framework is then sent to the test case generation module.

[0107] The requirements analysis results include functional point information, interface information, and data flow information.

[0108] Step S202: The test case framework is received through the test case generation module, and a test case script is generated based on the test case framework, historical test data, industry verification data and / or machine learning generated data. The test case script is then sent to the test execution engine module.

[0109] The test case scripts include normal process test case scripts, abnormal process test case scripts, and boundary condition test case scripts.

[0110] Step S203: Receive test case scripts through the test execution engine module, configure corresponding test tasks according to the test case scripts, set task scheduling information, execute test tasks according to the task scheduling information, obtain test results, and send the test results to the test result analysis module.

[0111] Step S204: Receive test results through the test result analysis module, and perform defect analysis on the test results using natural language processing and data mining techniques to obtain test adjustment strategies. Send the test adjustment strategies to the automatic calibration and optimization module. The test adjustment strategies include test case adjustment strategies for test case scripts and test process adjustment strategies for optimizing the test process.

[0112] Step S205: Receive the test adjustment strategy through the automatic calibration and optimization module, adjust the test process and / or test case scripts according to the test adjustment strategy, and send the adjusted test case scripts to the test execution engine module so that the test execution engine module executes according to the adjusted test process.

[0113] In summary, the automated calibration method for software integration testing provided in this embodiment can solve the problem that traditional integration testing methods rely heavily on manual labor, resulting in limited efficiency and effectiveness of automated testing. Through the automated calibration system for software integration testing, test requirements can be automatically parsed by the requirement parsing module, quickly extracting key information such as functional points, interface information, and data flows. Based on these parsing results, the system can automatically generate test case frameworks and send them to the test case generation module. This module utilizes historical test data, industry verification data, and machine learning technology to automatically generate various types of test case scripts (including normal flow, abnormal flow, and boundary condition test case scripts). These test case scripts are then sent to the test execution engine module for automated execution. The test execution engine module executes relevant test tasks according to task scheduling information, automatically generating and collecting test results. These results are further analyzed for defects by the test result analysis module, which uses natural language processing and data mining techniques to accurately identify potential defects and generate corresponding test adjustment strategies. These strategies not only involve adjusting test case scripts but also optimizing the test process. Finally, the automatic calibration and optimization module automatically optimizes the test process and test case scripts based on the generated adjustment strategies. The adjusted scripts are then retransmitted to the test execution engine module to ensure that test tasks are executed according to the optimized strategies, thereby continuously improving test effectiveness. Through this automated closed-loop test process, the problem of manual intervention in traditional integration testing methods is solved, the efficiency and effectiveness of automated testing are greatly improved, the test cycle is significantly shortened, and software defects can be more accurately discovered and targeted repair suggestions can be provided, thereby comprehensively improving software quality.

[0114] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0115] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0116] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0117] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations of the embodiments of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A software integration test automation calibration system, characterized by, The system includes: a test requirements parsing module, a test case generation module, a test execution engine module, a test result analysis module, and an automatic calibration and optimization module; The test requirement parsing module is used to parse the software requirement document to obtain the requirement parsing result; generate a test case framework based on the requirement parsing result, and send the test case framework to the test case generation module; the requirement parsing result includes function point information, interface information, and data flow information; The test case generation module is used to generate test case scripts based on the test case framework, historical test data, industry verification data, and / or machine learning generated data upon receiving the test case framework; and to send the test case scripts to the test execution engine module; the test case scripts include normal process test case scripts, abnormal process test case scripts, and boundary condition test case scripts. The test execution engine module is used to, upon receiving the test case script, configure the corresponding test task according to the test case script and set task scheduling information; execute the test task according to the task scheduling information to obtain test results; and send the test results to the test result analysis module. The test result analysis module is used to analyze the test results upon receiving them using natural language processing and data mining techniques to obtain a test adjustment strategy; and to send the test adjustment strategy to the automatic calibration and optimization module; the test adjustment strategy includes a test case adjustment strategy for test case scripts and a test process adjustment strategy for optimizing the test process; The automatic calibration and optimization module is used to adjust the test process and / or the test case script according to the test adjustment strategy when it receives the test adjustment strategy; and send the adjusted test case script to the test execution engine module so that the test execution engine module executes according to the adjusted test process.

2. The software integration test automation calibration system of claim 1, wherein, The system also includes a test environment configuration module; the task scheduling information includes environment configuration information. The test execution engine module is also used to send the environment configuration information to the test environment configuration module; The test environment configuration module is used to configure the test environment corresponding to the test case script according to the environment configuration information received; the environment configuration information includes database configuration information, network service configuration information, and third-party service dependency configuration information.

3. The software integration test automation calibration system of claim 2, wherein, The test environment configuration module also includes a simulation virtual submodule; The simulated virtual submodule is used to configure high-concurrency and / or low-resource test scenarios through virtualization technology.

4. The software integration test automation calibration system of claim 1, wherein, The test execution engine module includes a test submodule and a recording submodule; The testing submodule integrates testing tools for automatically executing the testing tasks; The recording submodule is used to record the test process and test results, including test steps, test logs and test screenshots.

5. The software integration test automation calibration system of claim 4, wherein, The testing tools include user interface automation testing tools, performance automation testing tools, and interface automation testing tools.

6. The software integration test automation calibration system of claim 2, wherein, The test case adjustment strategy includes strategies to increase the scenarios covered by test cases or strategies to adjust the priority of test cases; The test process adjustment strategy includes strategies to adjust the test order or to increase concurrent tests; The test adjustment strategy includes the strategy of increasing concurrent testing. The automatic calibration optimization module is also used to configure the test scenario for concurrent testing through the simulation virtual submodule.

7. The system of claim 1, wherein, The test result analysis module is also used to perform defect analysis on the test results using the natural language processing technology and the data mining technology, obtain defect analysis results, and associate the defect analysis results with the defect management system.

8. The system of claim 1, wherein, The test result analysis module is also used to generate an automated test report based on the test results and a preset test report template.

9. The system of claim 1, wherein, The system integrates an automated testing equipment system.

10. A software integration test automation calibration method, characterized in that, Applied to the automated calibration system for software integration testing according to any one of claims 1 to 9, the method includes the following steps: The software requirements document is parsed by the test requirements parsing module to obtain the requirements parsing results, and a test case framework is generated based on the requirements parsing results. The test case framework is then sent to the test case generation module. The requirements analysis results include function point information, interface information, and data flow information; The test case generation module receives the test case framework and generates test case scripts based on the test case framework, historical test data, industry verification data, and / or machine learning-generated data. The test case scripts are then sent to the test execution engine module. The test case scripts include normal process test case scripts, abnormal process test case scripts, and boundary condition test case scripts. The test execution engine module receives the test case script, configures the corresponding test task according to the test case script, sets the task scheduling information, executes the test task according to the task scheduling information, obtains the test result, and sends the test result to the test result analysis module. The test result analysis module receives the test results and performs defect analysis on the test results using natural language processing and data mining techniques to obtain a test adjustment strategy, which is then sent to the automatic calibration and optimization module. The test adjustment strategy includes a test case adjustment strategy for test case scripts and a test process adjustment strategy for optimizing the test process. The automatic calibration and optimization module receives the test adjustment strategy, adjusts the test process and / or the test case script according to the test adjustment strategy, and sends the adjusted test case script to the test execution engine module so that the test execution engine module executes according to the adjusted test process.

Images