CN118394629A

CN118394629A - Test case generation method and device and electronic equipment

Info

Publication number: CN118394629A
Application number: CN202410375616.0A
Authority: CN
Inventors: 胡煊铭
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2024-03-29
Filing date: 2024-03-29
Publication date: 2024-07-26

Abstract

The specification provides a test case generation method, a device and an electronic device, wherein the method comprises the following steps: acquiring the service requirement of natural language description; inputting the business requirements described by the natural language into a pre-trained first artificial intelligent model to obtain requirements splitting suggestions; after the requirement splitting suggestion is confirmed, inputting the requirement splitting suggestion into a second artificial intelligent model to obtain a business logic branch in the requirement splitting suggestion; and after the business logic branch is confirmed, inputting the business logic branch into a third artificial intelligent model to obtain the test case. The scheme can reduce dependence on manpower, reduce system problems and defects caused by human errors, and enable the generated test cases to comprehensively cover business logic branches, ensure comprehensive testing of various scenes and improve the robustness and reliability of the system.

Description

Test case generation method and device and electronic equipment

Technical Field

The application relates to the technical field of computers, and can be used in the financial field, in particular to a test case generation method, a test case generation device and electronic equipment.

Background

Business requirement processing before software programming is the process of collecting, analyzing and sorting business requirements before software development. This process is intended to fully understand the needs of the customer, user, or stakeholder to ensure that the new software system is able to meet business objectives and user expectations. Specifically, business demand processing includes communication with customers and stakeholders to collect their expectations and demands; analyzing and sorting the collected requirements, and identifying key business processes, business rules and functional requirements; the requirements are ultimately validated and translated into functional and performance specifications for the software system. This process also includes determining user interface designs, data structures, business logic branches, and other related aspects of the system. The business requirement processing provides a clear direction for the subsequent development of the software project, and ensures that a development team can convert the business requirement into a viable software solution.

The generation of test cases after software programming refers to writing a series of test cases for verifying software functions after software development is completed. The test cases describe what steps should be performed in different situations and the expected results. These test cases are typically written by a test team and based on design specifications and requirements documents, determine which test scenarios should be included.

In the prior art, processing of business requirements, generation of requirements documents, and test case generation have relied on a number of manual tasks, including requirements splitting, task allocation, and test case generation. These manual tasks are not only time consuming, but also prone to errors, which can lead to inefficiency and quality problems in the required process.

Disclosure of Invention

The specification provides a test case generation method, a test case generation device and electronic equipment, so as to reduce the deployment difficulty of a gray scale environment in a system.

In order to solve the above technical problems, a first aspect of the present disclosure provides a test case generating method, including: acquiring the service requirement of natural language description; inputting the business requirements described by the natural language into a pre-trained first artificial intelligent model to obtain requirements splitting suggestions; after the requirement splitting suggestion is confirmed, inputting the requirement splitting suggestion into a second artificial intelligent model to obtain a business logic branch in the requirement splitting suggestion; and after the business logic branch is confirmed, inputting the business logic branch into a third artificial intelligent model to obtain the test case.

In some embodiments, the method further comprises: updating the first artificial intelligent model according to the modification advice of the demand splitting advice fed back by the affirmed personnel; and/or updating the second artificial intelligence model according to the modification advice of the business logic branches fed back by the validation personnel.

In some embodiments, the method further comprises: acquiring modification suggestions and/or supplementary suggestions of the test cases fed back by a tester or a target client of the software; updating the third artificial intelligence model according to the modification advice and/or the supplementary advice.

In some embodiments, the first artificial intelligence model is trained by: collecting natural language business requirements of each software programming project in history and contents of actual programming modules as training data; the content of the actual programming module is obtained through manual splitting, and the content of the actual programming module corresponds to a requirement splitting suggestion; the natural language business requirement is used as input data of the first artificial intelligent model, and the content of the actual programming module is used as expected output of the first artificial intelligent model to train the first artificial intelligent model.

In some embodiments, after the demand splitting suggestion is validated, inputting the demand splitting suggestion into a second artificial intelligence model to obtain a business logic branch in the demand splitting suggestion, comprising: acquiring the actual programming module content of a software project; and after the requirement splitting suggestion is confirmed, inputting the service requirement described by the natural language, the actual programming module content and the requirement splitting suggestion into a second artificial intelligent model to obtain a service logic branch.

In some embodiments, the second artificial intelligence model is trained by: collecting natural language service requirements of all software programming projects which are historically tested by adopting the first artificial intelligent model, actual programming module contents, requirement splitting suggestions output by the first artificial intelligent module and service logic branch contents in actual testing as training data; the service logic branch content in the actual test is the service logic branch content obtained under the condition of manual participation; the natural language service requirement, the actual programming module content and the requirement splitting suggestion output by the first artificial intelligent module are taken as input data of the second artificial intelligent model, and the service logic branch content in actual test is taken as expected output of the second artificial intelligent model to train the second artificial intelligent model.

In some embodiments, after the requirement splitting suggestion is validated and before inputting the requirement splitting suggestion into a second artificial intelligence model, obtaining a business logic branch in the requirement splitting suggestion, further comprising: the requirement splitting suggestion is sent to an approver for approval; obtaining approval opinions of approval personnel; and under the condition that the approval opinion passes, executing the step of inputting the requirement splitting suggestion into a second artificial intelligent model to obtain a business logic branch in the requirement splitting suggestion.

In some embodiments, the method further comprises: acquiring a modification suggestion of an approval person under the condition that the approval opinion is not passed; sending the modification suggestion to a responsible person of the requirement splitting suggestion; acquiring a requirement splitting suggestion by a responsible person of the requirement splitting suggestion according to the requirement splitting suggestion after adjustment; and re-sending the adjusted requirement splitting advice to an approver for approval.

In some embodiments, after inputting the business logic branch into the third artificial intelligence model to obtain the test case, the method further comprises: judging whether each test case needs user data or not; under the condition that the test case needs the user data, automatically extracting the user data from the database; desensitizing the extracted user data according to the specific content of the test case; and supplementing the desensitized user data into the test case.

In some embodiments, after inputting the business logic branch into the third artificial intelligence model to obtain the test case, the method further comprises: and automatically generating a test script according to the test cases to realize automatic test.

A second aspect of the present specification provides a test case generating apparatus, comprising: the first acquisition unit is used for acquiring the service requirement of the natural language description; the first processing unit is used for inputting the business requirements described by the natural language into a pre-trained first artificial intelligent model to obtain requirements splitting suggestions; the second processing unit is used for inputting the requirement splitting suggestion into a second artificial intelligent model after the requirement splitting suggestion is confirmed to obtain a business logic branch in the requirement splitting suggestion; and the third processing unit is used for inputting the business logic branch into a third artificial intelligent model after the business logic branch is confirmed to obtain the test case.

In some embodiments, the apparatus further comprises: the first updating unit is used for updating the first artificial intelligent model according to the modification suggestion of the requirement splitting suggestion fed back by the affirmed personnel; and/or a second updating unit, configured to update the second artificial intelligence model according to a modification suggestion for the business logic branch, which is fed back by the validation personnel.

In some embodiments, the apparatus further comprises: the second acquisition unit is used for acquiring modification suggestions and/or supplementary suggestions of the test cases fed back by the testers or target clients of the software; and a third updating unit, configured to update the third artificial intelligence model according to the modification suggestion and/or the supplementary suggestion.

In some embodiments, the second processing unit comprises: the first acquisition subunit is used for acquiring the actual programming module content of the software project; and the processing subunit is used for inputting the service requirement described by the natural language, the actual programming module content and the requirement splitting suggestion into a second artificial intelligence model after the requirement splitting suggestion is confirmed, so as to obtain a service logic branch.

In some embodiments, the apparatus further comprises: the delivering and checking unit is used for delivering the requirement splitting suggestion to an approver for approval after the requirement splitting suggestion is confirmed and before the requirement splitting suggestion is input into a second artificial intelligent model to obtain a business logic branch in the requirement splitting suggestion; the third acquisition unit is used for acquiring approval opinions of the approver; and under the condition that the approval opinion passes, the second processing unit executes the input of the requirement splitting suggestion into a second artificial intelligent model to obtain a business logic branch in the requirement splitting suggestion.

In some embodiments, the apparatus further comprises: a fourth obtaining unit, configured to obtain a modification suggestion of an approver if the approval opinion is that the approval fails; a sending unit, configured to send the modification suggestion to a responsible person of the requirement splitting suggestion; a fifth obtaining unit, configured to obtain a requirement splitting suggestion adjusted by a responsible person of the requirement splitting suggestion according to the modification suggestion; and the review unit resends the adjusted requirement splitting advice to an approver for approval.

In some embodiments, the apparatus further comprises: the judging unit is used for judging whether each test case needs user data after the business logic branch is input into the third artificial intelligent model to obtain the test cases; the extraction unit is used for automatically extracting the user data from the database under the condition that the user data is needed by the test case; the decryption unit is used for performing desensitization treatment on the extracted user data according to the specific content of the test case; and the supplementing unit is used for supplementing the user data subjected to the desensitization processing into the test case.

In some embodiments, the apparatus further comprises: and the generating unit is used for automatically generating a test script according to the test case after the business logic branch is input into the third artificial intelligent model to obtain the test case so as to realize automatic test.

A third aspect of the present specification provides an electronic device, comprising: the test case generation method according to any one of the first aspect is implemented by the processor executing the computer instructions.

A fourth aspect of the present specification provides a computer storage medium storing computer program instructions which, when executed by a processor, implement the test case generation method of any one of the first aspects.

A fifth aspect of the present specification provides a computer program product comprising a computer program which, when executed by a processor, implements the test case generation method of any of the first aspects.

According to the test case generation method, the test case generation device and the electronic equipment, the first artificial intelligent model is used for processing the business requirements described by natural language to obtain the requirement splitting suggestion, the second artificial intelligent model is used for processing the requirement splitting suggestion to obtain the business logic branch, and the third artificial intelligent model is used for processing the business logic branch to obtain the test case. According to the scheme, the requirement requirements of natural language description are converted into the requirement splitting suggestions through the first artificial intelligent model, so that the accuracy of understanding the service requirements can be improved, and meanwhile, the artificial workload can be reduced; the service logic branches are extracted from the requirement splitting suggestions through the second artificial intelligent model, and the test cases are determined based on the service logic branches, so that the generated test cases can fully cover the service logic branches, the comprehensive test of various scenes is ensured, and the robustness and the reliability of the system are improved; the intelligent processing process reduces the chance of human intervention, and reduces the system problems and defects caused by human errors; the links of user verification requirement splitting suggestion and business logic branches are introduced, so that the transparency and compliance of requirement processing are ensured, and the accuracy of the requirement processing is ensured by user verification; the user verified requirement enters the next flow, so that the stability of the system is ensured, and the influence of the unverified requirement on the system is avoided; the process of demand splitting, business logic generation and test case generation is intelligentized, the complexity of team internal cooperation is reduced, and the overall working efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are necessary for the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the following description are only some of the embodiments described in the application, and that other drawings can be obtained from these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of a test case generation method provided in the present specification;

Fig. 2 is a schematic diagram of a specific implementation of a test case generating method according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a training method of a second artificial intelligence model;

fig. 4 is a schematic diagram of another test case generation method provided in the present specification

Fig. 5 is a schematic diagram of yet another test case generation method provided in the present specification;

fig. 6 is a schematic diagram of yet another test case generation method provided in the present specification;

FIG. 7 is a schematic diagram of a banking business demand processing and testing system;

FIG. 8 is a schematic diagram of a processing method of a banking business demand processing and testing system;

fig. 9 is a schematic diagram of a test case generating device provided in the present specification;

fig. 10 shows a schematic structural diagram of the electronic device provided in the present specification.

Detailed Description

In order to make the technical solution of the present application better understood by those skilled in the art, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application. All other embodiments, based on the embodiments of the application, which would be apparent to one of ordinary skill in the art without undue burden are intended to be within the scope of the application.

The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations. The collected information is information and data authorized by a user or fully authorized by each party, and the processing of the related data such as collection, storage, use, processing, transmission, provision, disclosure, application and the like all obeys the related laws and regulations and standards of related countries and regions, necessary security measures are taken, no prejudice to the public order colloquial is avoided, and corresponding operation entrance is provided for the user to select authorization or rejection.

In recent years, with the continuous development of natural language processing and artificial intelligence technology, automated demand processing and test case generation have become possible. In particular, natural language generative models such as ChatGPT have enjoyed great success in the field of natural language understanding and generation.

The test case generation method provided by the specification aims to utilize the potential of the technologies and bring a brand new method for the test case generation after software programming. By applying natural language processing and generation techniques to the demand processing process before software programming, the test case generation process after software programming, faster, more accurate and more intelligent demand splitting, task allocation and test case generation can be achieved. The system not only can improve the efficiency of the development department, but also is beneficial to quality assurance, ensures comprehensive testing of business logic branches, and improves the efficiency, accuracy and quality of business development processes.

As shown in fig. 1, the test case generation method provided in the present specification includes the following steps:

s10: and acquiring the service requirement of the natural language description.

The natural language description business requirement refers to the user requirement recorded by business personnel responsible for communication with the client after fully understanding the client requirement. The user requirements are the overall functionality that the customer is able to implement for the developed software system.

S20: and inputting the business requirement described by the natural language into a pre-trained first artificial intelligent model to obtain a requirement splitting suggestion output by the first artificial intelligent model.

The requirement splitting suggestion refers to specific programming module content obtained after processing the service requirement described by natural language in the software programming process. These programming module contents may be used to distribute programming tasks of a software project to various developers.

The first artificial intelligence model is used for converting the business requirements of the natural language description into requirements splitting suggestions.

In some embodiments, the first artificial intelligence model may be trained by: collecting natural language service requirements of each software programming project in history and contents of an actual programming module as training data, wherein the natural language service requirements correspond to the service requirements of the natural language description, the contents of the actual programming module are obtained by splitting under the condition of manual participation, and the contents of the actual programming module correspond to the requirement splitting advice; the natural language business requirement is used as input data of the first artificial intelligent model, and the content of the actual programming module is used as expected output of the first artificial intelligent model to train the first artificial intelligent model.

The above-mentioned "split under artificial participation" may be a complete manual split, or may be a case that is manually confirmed after automatic split by an artificial intelligence model.

The actual programming modules refer to functional modules that are actually divided in the software project. Such as a table processing module, a page display module, an approval module, etc.

The first artificial intelligence model may employ natural language processing techniques such as ChatGPT to automatically parse, understand, and classify business requirements. ChatGPT by learning the language patterns, the mapping relationship between the business requirements of the natural language description and the contents of the actual programming modules can be determined.

For example, the business requirements of the natural language description may be: the X department wishes the bank to limit the bulk consumption of users in the victim list to reduce the property loss of these users. The resulting requirement splitting advice may include: importing the list of victims, the list can be realized by adding and deleting the details of the operations, limiting the consumption, and dividing the steps into several modules, and the message reminding.

S30: after the requirement splitting suggestion is confirmed, the requirement splitting suggestion is input into a second artificial intelligence model, and business logic branches in the requirement splitting suggestion output by the second artificial intelligence model are obtained.

In some embodiments, the demand splitting advice may be presented to a business person and/or responsible for the software architecture person for validation, receiving a modification advice for the demand splitting advice that is fed back by the validation person, and updating the first artificial intelligence model based on the modification advice fed back by the validation person.

The second artificial intelligence model is used to extract business logic branches in the requirement splitting advice. The business logic branch specifically comprises specific contents of at least one of the following: business processes and business rules, data processing logic, user interaction logic, system integration logic, security and authority control logic, exception processing logic and the like.

Business processes and business rules include defining various steps and activities in a business process, as well as business rules and conditions involved. For example, a business logic branch of an order processing system may include steps and corresponding rules for creating orders, auditing orders, paying orders, and the like.

The data processing logic describes the processing of data input, output, validation, storage, conversion, and computation. This includes creation, updating, deletion of data, and relationships and operations between data.

User interaction logic defines the logic of the user interface and user interaction, including verification of user input, response, navigation, and the like. For example, user login, form fill-in, information presentation, etc.

The system integration logic describes the integration logic of the system with external systems or services, defines the communication modes, data formats, interface specifications, etc. of the system with other systems.

Security and rights control logic: the method comprises the security-related business logic of user login authentication, authority management, data privacy protection and the like.

Exception handling logic: the processing mode of the system for the abnormal situation is defined, including error processing, abnormal notification, system recovery and the like.

The above is some possible aspects of service logic branching, and actually the scope of the service logic branching will vary according to the specific software requirements and the coding module implementing the requirements. In the software development process, business logic branches are further extracted from the program module content obtained through splitting, so that a test team can better understand the behavior and the function of the system, and the test can be effectively conducted.

In some embodiments, the second artificial intelligence model is trained by: collecting the content of an actual programming module of each software programming project in history and the service logic branch content in actual test as training data, wherein the content of the actual programming module corresponds to the requirement splitting suggestion, the service logic branch content in actual test refers to the service logic branch content obtained under the condition of manual participation, and the service logic branch content in actual test corresponds to the service logic branch in the requirement splitting suggestion; and taking the content of the actual programming module as input data of the second artificial intelligent model, taking the business logic branch content during actual testing as expected output of the second artificial intelligent model, and training the second artificial intelligent model.

The "obtained under the condition of human participation" may be obtained by complete manual operation, or may be obtained automatically by an artificial intelligence model and then confirmed manually.

In some embodiments, during execution of the software programming task, it may be found that the program module content determined before the software programming task may be missing or inadequate, and thus new program module content may be added, program module content may be modified, program module content may be deleted, etc. during execution of the software programming task. Thus, it may be undesirable to extract business logic branches for testing just prior to execution according to the software programming task.

In this regard, as shown in fig. 2, S30 may specifically be: s31: acquiring the actual programming module content of a software project; and after the requirement splitting suggestion is confirmed, inputting the service requirement, the actual programming module content and the requirement splitting suggestion described by the natural language into a second artificial intelligent model to obtain a service logic branch output by the second artificial intelligent model.

That is, the business requirements described in connection with natural language, the actual programming module content of the software project, and the requirement splitting advice collectively determine the business logic branches that are extracted. Business logic branching is critical to test case accuracy. The device can rectify the demand splitting suggestion output by the first artificial intelligent model through the service demand described by natural language and the actual programming module content, and supplement the service logic branch content which cannot be considered by the first artificial intelligent model, so that the service logic branch is more accurate, and the comprehensiveness of the subsequently generated test cases is ensured.

In the present specification, the actual program module content refers to a description of the actual program module content, which may be a natural language description, or may be a structured program language description, a pseudo code description, or the like.

Accordingly, as shown in FIG. 3, the second artificial intelligence model may be trained by:

S01: and collecting natural language service requirements of all software programming projects which are historically tested by adopting the first artificial intelligent model, actual programming module contents, requirement splitting suggestions output by the first artificial intelligent module and service logic branch contents in actual testing as training data. The service logic branch content in actual test refers to service logic branch content obtained under the condition of manual participation.

S02: the natural language service requirement, the actual programming module content and the requirement splitting suggestion output by the first artificial intelligent module are taken as input data of the second artificial intelligent model, and the service logic branch content in actual test is taken as expected output of the second artificial intelligent model to train the second artificial intelligent model.

In some embodiments, after obtaining the service logic branch output by the second artificial intelligence model, a requirement document used in the software code generation process can also be generated according to the service logic branch, and the code task can be allocated based on the service logic branch.

In other embodiments, the test case generation is accomplished using the first artificial intelligence model, the second artificial intelligence model, and the third artificial intelligence model, without using the three models to generate the demand document that is adapted for use in the software code. By the arrangement, the logic of the software code and the logic of the test case are different, so that the software can be more effectively tested, and the situation that the testing effect is invalid due to the homogenization of the logic of the software code and the logic of the test case is avoided.

The second artificial intelligence model may employ ChatGPT models, through the deep learning capabilities of ChatGPT, the system is able to intelligently identify business logic branches in demand, including possible conditional branches and critical flows. The system may generate suggestions for business logic branches that are clearly presented to the development and testing team. This helps the team better understand the business needs, ensuring a deep understanding and handling of each logical branch.

S40: and after the business logic branch is confirmed, inputting the business logic branch into a third artificial intelligent model to obtain a test case output by the third artificial intelligent model.

Test case generation is a key step in ensuring software quality, which helps to discover flaws and errors in the software. By executing various test cases, it is possible to verify whether the software meets business requirements and user expectations. Test cases typically cover individual functional modules of the software, such as input verification, functional operation, boundary conditions, abnormal situations, etc., to ensure that the software is functioning properly under all conditions.

Test case generation also involves determining test data, input conditions, and expected output, and developing a test execution plan. These test cases may be executed prior to release of the software to verify the correctness, performance, and stability of the software.

For example, the test cases referred to by the table processing logic include: whether a list can be added, what the display interface is successful, whether the list can be queried through date, whether the list is queried through identity identification, whether a button is perfect, whether the list can be normally transferred to an approver for approval, whether page paging display is normal under the condition of more projects, and the like.

The test cases may also include structured test cases. The structured test case comprises: input data, expected output and execute steps. Such structured output helps the test team to quickly understand and perform the test, improving test efficiency. For example, after entering an identity for a module and executing the module, the expected result is that the data of the corresponding user in the table is deleted. The test case may enter the input data in the structured data into the module and check to see if the corresponding user data in the table is deleted.

In some embodiments, the business logic branch may be presented to a business person and/or responsible for the software architecture person for validation, a modification suggestion to the business logic branch fed back by the validation person is received, and the second artificial intelligence model is updated according to the modification suggestion fed back by the validation person.

The third artificial intelligence model is used for automatically generating test cases according to the business logic branches. In some embodiments, the third artificial intelligence model may be trained by: collecting actual business logic branches of each software programming project in history and test cases in actual test; and taking the actual business logic branch as the input of the third artificial intelligent model, taking the test case in the actual test case as the expected output of the third artificial intelligent model, and training the third artificial intelligent model.

In some embodiments, modification suggestions and/or supplementary suggestions of the test case fed back by the tester or the target client of the software may also be obtained; the third artificial intelligence model is updated according to the modification advice and/or the supplemental advice.

The intelligent degree of the scheme for generating the test cases can be optimized by continuously collecting feedback information in the development and test processes, including the accuracy of demand splitting, the understanding degree of business logic branches and the actual effect of test cases, and automatically adjusting the remembering of the first, second and third artificial intelligent models. The automatic optimization mechanism ensures that the system continuously improves the intelligent level in continuous learning and adapts to the change of the service environment.

In some embodiments, as shown in fig. 4, after the requirement splitting suggestion is confirmed and before the requirement splitting suggestion is input into the second artificial intelligence model, the following S50 to S100 are further included.

S50: and sending the requirement splitting suggestion to an approver for approval.

S60: and obtaining approval opinions of the approver.

And under the condition that the approval opinion is approved, executing S30 to input the requirement splitting suggestion into a second artificial intelligent model to obtain a business logic branch in the requirement splitting suggestion.

S70: and under the condition that the approval opinion is not passed, acquiring a modification suggestion of an approver.

S80: and sending the modification suggestion to a responsible person of the requirement splitting suggestion.

S90: and acquiring the requirement splitting suggestion by a responsible person who acquires the requirement splitting suggestion according to the requirement splitting suggestion after the modification suggestion is adjusted.

S100: and re-sending the adjusted requirement splitting advice to an approver for approval.

As shown in fig. 5, after the business logic branch is input into the third artificial intelligence model to obtain the test case, the following S110 to S140 are further included.

S110: and judging whether each test case needs user data.

S120: in the case that the test case requires user data, the user data is automatically extracted from the database.

S130: and desensitizing the extracted user data according to the specific content of the test case.

The information which plays a key role for the test case itself can be subjected to desensitization processing without desensitization processing, and the information which does not play a key role for the test case can be subjected to desensitization processing so as to prevent the user privacy from being revealed in the test process.

S140: and supplementing the desensitized user data into the test case.

The test described in S110 to S140 may refer to a test performed in a case where written software is connected to an actual user database. Compared with the test by adopting virtual data, the test can reflect the effectiveness of written software on a data processing mode, so that the test result is more reliable.

As shown in fig. 6, after inputting the business logic branch into the third artificial intelligence model to obtain the test case, the method further includes: s150: and automatically generating a test script according to the test cases to realize automatic test.

The method for generating test cases provided in the present specification will be described below by taking the banking demand processing and testing system shown in fig. 7 as an example. The system shown in fig. 7 includes an intelligent demand processing module S1, a business logic branch generation module S2, an automated test case generation module S3, and an intelligent feedback and optimization module S4.

The intelligent demand processing module S1 can automatically analyze, understand and classify the service demands by using natural language processing technologies such as ChatGPT. ChatGPT through learning the language mode, key information in the requirements, including service requirements, function requirements and data requirements, can be accurately identified. The intelligent demand processing module is also capable of generating demand splitting suggestions. Specifically, a demand split suggestion is generated based on the results of the natural language processing. These suggestions provide an intelligent division of business requirement structures, providing a clear working basis for subsequent development and testing.

The business logic branch generation module S2 identifies business logic branches based on the demand splitting advice. Through the deep learning capability of ChatGPT, business logic in demand, including possible conditional branches and critical flows, is intelligently identified. The business logic branch generation module generates suggestions for business logic branches that are clearly presented to the development and testing team. This helps the team better understand the business needs, ensuring a deep understanding and handling of each logical branch.

The automated test case generation module S3 automatically generates corresponding test scenarios based on the demand splitting and business logic suggestions. This includes an exhaustive test case for each business logic branch, ensuring comprehensive and efficient test coverage.

And the intelligent feedback and optimization module S4 is used for collecting feedback information in the development and test process continuously, wherein the feedback information comprises the accuracy of demand splitting, the understanding degree of business logic branches and the actual effect of test cases. Based on the collected feedback information, the system automatically adjusts ChatGPT the model to optimize its intelligence level for business demand processing. The automatic optimization mechanism ensures that the system continuously improves the intelligent level in continuous learning and adapts to the change of the service environment.

FIG. 8 is a schematic diagram of a process flow of a banking business demand processing and testing system.

S5, logging in a system: and the user inputs a user name and a password to log in through a safe login interface provided by the system. After the system verifies the identity of the user, the system enters a main interface of the system.

S6, submitting service requirements: the user finds the "submit demand" function in the main interface and clicks into the demand submit page. At this page, the user may fill in detailed information of the business requirements, including business requirements, function requirements, and data requirements. The user can directly input or upload related documents.

S7, intelligent demand processing: the intelligent demand processing module of the system starts up immediately upon the user submitting the demand. Through natural language processing techniques such as ChatGPT, the system intelligently parses and understands the requirements submitted by the user. The system generates a requirement splitting suggestion and displays the splitting result to the user.

S8, verifying requirement splitting suggestions: the user can verify and adjust one by one in the requirement splitting advice displayed by the system. The system provides relevant business logic branching information for each suggestion. The user can intuitively know the split logic through the interface, so that the accuracy of system understanding is ensured.

S9, submitting the verified requirement: after the user verifies and confirms the requirement splitting suggestion, the requirement enters an approval process. After the user submits the requirement, the system automatically sends a notice to the upper level approver.

S10, upper-level approval: after the upper level approver receives the notification, the upper level approver logs in the system approval interface. The approver checks the requirement splitting suggestion, and confirms the reasonability and compliance with the service specification. The approver may approve the requirement for further processing and may also propose modification advice.

S11, splitting modification requirements: if the approver proposes a modification suggestion, the system will inform the user to make a corresponding adjustment. The user modifies the demand splitting according to the approver's advice and resubmisses the approval.

S12, continuing service logic branch generation: once the demand is approved, the system enters the business logic branch generation phase. The system intelligently generates business logic branch suggestions based on the verified demand split suggestions.

S13, verifying service logic branches: and the user verifies the service logic branch to ensure that the system correctly understands the service logic. The user can check the suggestions of each branch one by one, and can also adjust according to specific conditions.

S14, submitting the validated business logic branches: after the user completes the verification of the business logic branch, the user can submit verification information. The system uses the user feedback for automatic optimization of the intelligent model and proceeds to the final stage, automated test case generation.

S15, generating a test case: and the system intelligently generates corresponding test cases according to the verification information of the business logic branches. The test cases include an exhaustive test scenario for each business logic branch to ensure comprehensive test coverage.

S16, structuring and outputting a test case: the system presents the generated test cases to the user in a structured manner. Each test case includes input data, expected output, and execution steps. The user can view and manage the test cases on the interface.

S17, submitting the verified test cases: and the user verifies the test case and confirms the accuracy of the test scene. Once authentication is complete, the user submits authentication information. The system uses the user's feedback for optimization of the system model.

The banking business demand processing and testing system has the following technical effects:

1. And (3) intelligent treatment: by introducing ChatGPT and other natural language processing technologies, intelligent processing of banking business requirements is realized, and understanding and splitting accuracy of the business requirements are improved.

2. Efficiency is improved: the automatic demand splitting, business logic generation and test case generation greatly improve the development and test efficiency, and reduce the time and workload of manual processing.

3. Comprehensive coverage: the intelligently generated test cases can fully cover service logic branches, ensure comprehensive testing of various scenes, and improve the robustness and reliability of the system.

4. User authentication link: and a user verification link is introduced, so that the transparency and compliance of the demand processing are ensured, and the accuracy of the demand processing is ensured by user verification.

5. Model automatic optimization: and the system automatically performs model optimization through verification feedback information of the user, continuously improves the intelligent processing level, and adapts to the change of banking environments.

6. Human error is reduced: the intelligent processing process reduces the chance of human intervention and reduces system problems and defects caused by human errors.

7. Flexibility and adjustability: the user can verify and adjust according to specific requirements, and the system maintains certain flexibility on the basis of intelligent processing and adapts to the requirements of different service scenes.

8. System stability: the user verified requirement enters the next flow, so that the stability of the system is ensured, and the influence of the unverified requirement on the system is avoided.

9. Team cooperation efficiency is improved: the process of demand splitting, business logic generation and test case generation is intelligentized, the complexity of team internal cooperation is reduced, and the overall working efficiency is improved.

10. Data driven decision: the collected user verification feedback information and data can be used for business decision, optimizing system flow and model, and realizing more accurate business processing.

The present specification provides a test case generation apparatus that may be used to implement the method shown in fig. 1. As shown in fig. 9, the apparatus includes a first acquisition unit 10, a first processing unit 20, a second processing unit 30, and a third processing unit 40.

The first obtaining unit 10 is configured to obtain a service requirement of a natural language description.

The first processing unit 20 is configured to input the service requirement described in the natural language into a pre-trained first artificial intelligence model, and obtain a requirement splitting suggestion.

The second processing unit 30 is configured to input the requirement splitting suggestion into a second artificial intelligence model after the requirement splitting suggestion is confirmed, so as to obtain a business logic branch in the requirement splitting suggestion.

The third processing unit 40 is configured to input the service logic branch into a third artificial intelligence model after the service logic branch is confirmed, so as to obtain a test case.

The description and functions of the above devices may be understood by referring to the content of the test case generation method, and will not be repeated.

An embodiment of the present invention also provides an electronic device, as shown in fig. 10, which may include a processor 1001 and a memory 1002, where the processor 1001 and the memory 1002 may be connected by a bus or other means, and in fig. 10, the connection is exemplified by a bus.

The processor 1001 may be a central processing unit (Central Processing Unit, CPU). The Processor 1001 may also be a chip such as another general purpose Processor, a digital signal Processor (DIGITAL SIGNAL Processor), an Application SPECIFIC INTEGRATED Circuit (ASIC), a Field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or a combination thereof.

The memory 1002 is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules (e.g., the first acquisition unit 10, the first processing unit 20, the second processing unit 30, and the third processing unit 40 shown in fig. 9) corresponding to the test case generation method in the embodiment of the present invention. The processor 1001 executes various functional applications of the processor and data processing, i.e., implements the test case generation method in the method embodiments described above, by running non-transitory software programs, instructions, and modules stored in the memory 1002.

Memory 1002 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created by the processor 1001, and the like. In addition, the memory 1002 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 1002 may optionally include memory located remotely from processor 1001, such remote memory being connectable to processor 1001 through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory 1002 that, when executed by the processor 1001, perform the test case generation method in the embodiment shown in fig. 1.

The specific details of the electronic device may be correspondingly understood by referring to the corresponding related descriptions and effects in the method embodiment, which are not repeated herein.

The present specification also provides a computer storage medium storing computer program instructions which, when executed by a processor, implement the steps of the test case generation method described above.

The present specification also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the test case generation method described above.

It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, where the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. Wherein the storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a hard disk (HARD DISK DRIVE, abbreviated as HDD), a Solid state disk (Solid-state-STATE DRIVE, SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are referred to each other, and each embodiment is mainly described as different from other embodiments.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function.

For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.

From the above description of embodiments, it will be apparent to those skilled in the art that the present application may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method of some parts of the various embodiments of the present application.

The application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

While the present application has been described by way of embodiments, those of ordinary skill in the art will recognize that there are many variations and modifications of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and modifications as do not depart from the spirit of the application.

Claims

1. A method of generating test cases, comprising:

Acquiring the service requirement of natural language description;

Inputting the business requirements described by the natural language into a pre-trained first artificial intelligent model to obtain requirements splitting suggestions;

After the requirement splitting suggestion is confirmed, inputting the requirement splitting suggestion into a second artificial intelligent model to obtain a business logic branch in the requirement splitting suggestion;

And after the business logic branch is confirmed, inputting the business logic branch into a third artificial intelligent model to obtain the test case.

2. The method according to claim 1, wherein the method further comprises:

Updating the first artificial intelligent model according to the modification advice of the demand splitting advice fed back by the affirmed personnel;

And/or the number of the groups of groups,

And updating the second artificial intelligent model according to the modification advice of the business logic branch fed back by the validation personnel.

3. The method according to claim 1, wherein the method further comprises:

acquiring modification suggestions and/or supplementary suggestions of the test cases fed back by a tester or a target client of the software;

Updating the third artificial intelligence model according to the modification advice and/or the supplementary advice.

4. The method according to claim 1, wherein the first artificial intelligence model is trained by:

collecting natural language business requirements of each software programming project in history and contents of actual programming modules as training data; the content of the actual programming module is obtained through manual splitting, and the content of the actual programming module corresponds to a requirement splitting suggestion;

The natural language business requirement is used as input data of the first artificial intelligent model, and the content of the actual programming module is used as expected output of the first artificial intelligent model to train the first artificial intelligent model.

5. The method of claim 1, wherein after the demand splitting suggestion is validated, inputting the demand splitting suggestion into a second artificial intelligence model to obtain a business logic branch in the demand splitting suggestion, comprising:

Acquiring the actual programming module content of a software project;

and after the requirement splitting suggestion is confirmed, inputting the service requirement described by the natural language, the actual programming module content and the requirement splitting suggestion into a second artificial intelligent model to obtain a service logic branch.

6. The method of claim 5, wherein the second artificial intelligence model is trained by:

Collecting natural language service requirements of all software programming projects which are historically tested by adopting the first artificial intelligent model, actual programming module contents, requirement splitting suggestions output by the first artificial intelligent module and service logic branch contents in actual testing as training data; the service logic branch content in the actual test is the service logic branch content obtained under the condition of manual participation;

The natural language service requirement, the actual programming module content and the requirement splitting suggestion output by the first artificial intelligent module are taken as input data of the second artificial intelligent model, and the service logic branch content in actual test is taken as expected output of the second artificial intelligent model to train the second artificial intelligent model.

7. The method of claim 1, wherein after the demand splitting suggestion is validated and before inputting the demand splitting suggestion into a second artificial intelligence model, obtaining a business logic branch in the demand splitting suggestion, further comprising:

the requirement splitting suggestion is sent to an approver for approval;

obtaining approval opinions of approval personnel;

and under the condition that the approval opinion passes, executing the step of inputting the requirement splitting suggestion into a second artificial intelligent model to obtain a business logic branch in the requirement splitting suggestion.

8. The method of claim 7, wherein the method further comprises:

acquiring a modification suggestion of an approval person under the condition that the approval opinion is not passed;

sending the modification suggestion to a responsible person of the requirement splitting suggestion;

Acquiring a requirement splitting suggestion by a responsible person of the requirement splitting suggestion according to the requirement splitting suggestion after adjustment;

And re-sending the adjusted requirement splitting advice to an approver for approval.

9. The method of claim 1, further comprising, after inputting the business logic branch into a third artificial intelligence model to obtain a test case:

judging whether each test case needs user data or not;

Under the condition that the test case needs the user data, automatically extracting the user data from the database;

desensitizing the extracted user data according to the specific content of the test case;

and supplementing the desensitized user data into the test case.

10. The method of claim 1, further comprising, after inputting the business logic branch into a third artificial intelligence model to obtain a test case:

and automatically generating a test script according to the test cases to realize automatic test.

11. A test case generation apparatus, comprising:

the first acquisition unit is used for acquiring the service requirement of the natural language description;

The first processing unit is used for inputting the business requirements described by the natural language into a pre-trained first artificial intelligent model to obtain requirements splitting suggestions;

The second processing unit is used for inputting the requirement splitting suggestion into a second artificial intelligent model after the requirement splitting suggestion is confirmed to obtain a business logic branch in the requirement splitting suggestion;

And the third processing unit is used for inputting the business logic branch into a third artificial intelligent model after the business logic branch is confirmed to obtain the test case.

12. An electronic device, comprising:

A memory and a processor communicatively coupled to each other, the memory having stored therein computer instructions that, upon execution, implement the test case generation method of any of claims 1 to 10.

13. A computer storage medium storing computer program instructions which, when executed by a processor, implement the test case generation method of any one of claims 1 to 10.

14. A computer program product comprising a computer program which, when executed by a processor, implements the test case generation method of any one of claims 1 to 10.