Disclosure of Invention
In view of the foregoing, it is desirable to provide a software automated testing method, apparatus, computer device, and storage medium that can more effectively improve the automated testing efficiency.
A method of software automated testing, the method comprising:
acquiring a software automation test request, and searching software to be tested and a test scene designated by the software automation test request;
Searching a preset script library according to the software to be tested and the test scene, and acquiring an RPA (Robotic process automation, robot flow automation) test execution script corresponding to the automatic test request;
executing the RPA test execution script;
In the execution process of the RPA test execution script, invoking an artificial intelligent component corresponding to the test scene to perform software test on the software to be tested in the test scene, and obtaining a software test result;
And generating a software test report according to the software test result.
In one embodiment, the searching the preset script library according to the software to be tested and the test scenario, before obtaining the RPA test execution script corresponding to the automatic test request, further includes:
Based on an RPA bottom layer flow engine and a component library, recording test flows corresponding to a plurality of test scenes by a mode of executing scripts through a visualized RPA editor, and generating RPA test execution scripts corresponding to each test scene;
and constructing a preset script library based on the RPA test execution scripts in each test scene.
In one embodiment, in the executing process of the RPA test execution script, invoking the artificial intelligence component corresponding to the test scenario to perform a software test on the software to be tested in the test scenario, and obtaining a software test result includes:
acquiring scene attributes of the test scene, and determining an artificial intelligent component corresponding to the scene attributes and a calling node of the artificial intelligent component;
and when the RPA test execution script is executed to the calling node, calling an artificial intelligent component corresponding to the scene attribute to perform software test on the software to be tested under the test scene, and obtaining a software test result.
In one embodiment, the method further comprises:
when the RPA test execution script generates an operation error, identifying the error type of the error;
When the error type is a blocking error, generating and pushing an error prompt message corresponding to the error type based on artificial intelligence;
And when the error type of the error is an operation error, searching an error processing scheme corresponding to the operation error based on artificial intelligence, and processing the operation error of the RPA test execution script based on the error processing scheme.
In one embodiment, the generating and pushing the error prompt message corresponding to the error type based on the artificial intelligence includes:
searching log information corresponding to the blocking error;
Generating an error prompt message corresponding to the blocking error according to the log information;
Pushing the error prompt message.
In one embodiment, the processing the running error of the RPA test execution script based on the artificial intelligence and the error type of the error includes:
when the error type of the error is an operation error, identifying the error type of the operation error based on artificial intelligence;
Searching a preset scheme list, and determining an error processing scheme corresponding to the error type;
And processing the running errors of the RPA test execution script based on the error processing scheme.
In one embodiment, after generating the software test report according to the software test result, the method further includes:
acquiring a software defect in the software test result;
Generating error reminding information based on the software defect;
Searching a development terminal corresponding to the software defect, and pushing the error prompt information to the development terminal.
A software automated testing apparatus, the apparatus comprising:
the request acquisition module is used for acquiring a software automation test request and searching software to be tested and a test scene designated by the software automation test request;
the script searching module is used for searching a preset script library according to the software to be tested and the test scene and acquiring an RPA test execution script corresponding to the automatic test request;
the RPA calling module is used for executing the RPA test execution script;
The software testing module is used for calling the artificial intelligent component corresponding to the testing scene in the execution process of the RPA test execution script so as to perform software testing on the software to be tested in the testing scene and obtain a software testing result;
and the report generating module is used for generating a software test report according to the software test result.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring a software automation test request, and searching software to be tested and a test scene designated by the software automation test request;
Searching a preset script library according to the software to be tested and the test scene, and acquiring an RPA test execution script corresponding to the automatic test request;
executing the RPA test execution script;
In the execution process of the RPA test execution script, invoking an artificial intelligent component corresponding to the test scene to perform software test on the software to be tested in the test scene, and obtaining a software test result;
And generating a software test report according to the software test result.
A computer storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring a software automation test request, and searching software to be tested and a test scene designated by the software automation test request;
Searching a preset script library according to the software to be tested and the test scene, and acquiring an RPA test execution script corresponding to the automatic test request;
executing the RPA test execution script;
In the execution process of the RPA test execution script, invoking an artificial intelligent component corresponding to the test scene to perform software test on the software to be tested in the test scene, and obtaining a software test result;
And generating a software test report according to the software test result.
The software automatic test method, device, computer equipment and storage medium comprise the steps of searching software to be tested and a test scene designated by the software automatic test request after the software automatic test request is acquired, searching a preset script library according to the software to be tested and the test scene, acquiring an RPA test execution script corresponding to the automatic test request, executing the RPA test execution script, calling an artificial intelligent component corresponding to the test scene in the execution process of the RPA test execution script so as to perform software test on the software to be tested in the test scene, acquiring a software test result, and generating a software test report according to the software test result. According to the application, the software to be tested and the test scene are determined based on the software automatic test request, so that the RPA test execution script is obtained, the regular and repeated workflow tasks in the software test process are executed based on the RPA test execution script, and meanwhile, the artificial test process is better simulated by means of the artificial intelligent component in combination with the specific test scene, so that the automatic test efficiency is effectively improved.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The application particularly provides a software automation testing method which can be applied to an application environment shown in fig. 1. The terminal 102 may communicate with the software automation test server 104 through a network, and the terminal 102 may send a software automation test request corresponding to the software to be tested to the software automation test server 104. After receiving the software automatic test request, the software automatic test server 104 searches the software to be tested and the test scene specified by the software automatic test request, searches a preset script library according to the software to be tested and the test scene, acquires an RPA test execution script corresponding to the automatic test request, executes the RPA test execution script, calls an artificial intelligent component corresponding to the test scene in the execution process of the RPA test execution script to perform software test on the software to be tested under the test scene, acquires a software test result, and generates a software test report according to the software test result. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the software automation test server 104 may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
In one embodiment, as shown in fig. 2, a software automation test method is provided, and the method is applied to the software automation test server 104 in fig. 1 for illustration, and includes the following steps:
Step 201, acquiring a software automation test request, and searching for software to be tested and a test scene specified by the software automation test request.
Wherein automated testing is a process that converts human-driven testing behavior into machine execution. Typically, after the test cases are designed and passed through the review, the test is performed step by the tester according to the procedure described in the test cases, resulting in a comparison of the actual results with the expected results. In the process, in order to save manpower, time or hardware resources and improve the test efficiency, the concept of automatic test is introduced. The processing efficiency of the software testing process is improved by machine execution. The software automatic test request is specifically used for requesting the automatic server to perform automatic test on the specified software to be tested, the software automatic test request can be specifically generated according to a test case corresponding to the software to be tested, the test case refers to description of a test task on a specific software product, and a test scheme, a method, a technology and a strategy are embodied. The content of the method comprises a test target, a test environment, input data, test steps, expected results, test scripts and the like. It is simply understood that a test case is a set of test inputs, execution conditions, and expected results that are tailored for a particular purpose to verify that a particular software requirement is met. The software to be tested is a test target of the software automatic test method in the scheme. The test scene refers to a service scene faced by the software to be tested, which is constructed through automatic test.
The application particularly uses the RPA technology to carry out software automatic test, thereby improving the test efficiency. RPA, robotic process automation system, is an application that provides another way to automate an end user's manual process by mimicking the end user's manual process at a computer. When testing, a test staff may generate a software automation test request based on the test case, and then send the software automation test request to the software automation test server 104 through the terminal 102. The software automation test server 104 obtains the software automation test request, searches the software to be tested and the test scene specified by the software automation test request, and starts the automation test. In a specific embodiment, the software to be tested is specifically an enterprise internal investment management system, and the enterprise internal investment management system has the characteristics of more pages, more verification logics and complex interactive operation, and is relatively dependent on historical test experience of testers, and the test experience is difficult to inherit. The application solidifies the test flow under the scenes of project before investment, preexamination, project approval during investment, post-project management, exit management and the like by introducing the RPA robot into the software automation test, and replaces the manual test by the machine test, thereby expanding the regression test range, reducing the labor cost and realizing quality improvement, cost reduction and synergy.
And 203, searching a preset script library according to the software to be tested and the test scene, and acquiring an RPA test execution script corresponding to the automatic test request.
The preset script library consists of RPA test execution scripts written in advance under different scenes. Whereas RPA test execution script, generally refers to a series of instructions for a particular test that can be executed by an automated test tool, in the present case an RPA executor. In order to improve maintainability and reusability of test scripts, they must be built before the test scripts are executed. It may be found that some operations will occur during several tests. Thus, the operations should be targeted so that their implementation can be reused. Test scripts are computer readable instructions that automatically perform a test procedure (or part of a test procedure). Test scripts may be created (recorded) or automatically generated using test automation tools, or programmed in a programming language, or may be integrated with the first three methods
Specifically, the process of automated testing may be generally performed by writing a completed test execution script in advance, and in this step, the software automated test server 104 may search a preset script library based on a given test scenario, to obtain a corresponding RPA test execution script under the scenario. The software test may then be performed based on the RPA test execution script. In this process, the software automation test server 104 may specifically control the RPA executor to obtain a test execution script, and then load the obtained RPA test execution script into the RPA executor.
Step 205, execute RPA test execution script.
Step 207, in the execution process of the RPA test execution script, calling an artificial intelligent component corresponding to the test scene to perform software test on the software to be tested in the test scene, thereby obtaining a software test result.
Wherein, artificial intelligence subassembly refers to the subassembly that needs nimble processing part in the test process is accomplished through artificial intelligence technique.
Specifically, the artificial intelligence component can be used for completing test tasks in test scenes such as text recognition, conversation robots and the like in the test process. The RPA executor produces test data through the test execution script, and test results of each test case are produced, so that test tasks of relevant scenes are completed. During the testing process, the tasks of the workflow based on rules and repetition can be processed by means of an RPA digital tool, and the manual testing process can be better simulated after the workflows are connected in series based on an RPA test execution script. For example, some project prequalification projects, file uploading, opinion solicitation, approval chain configuration selection, printing application and other flow specified operation actions are involved in the investment whole flow business of an enterprise. The software testing robot manufactured by the RPA technology can rapidly and accurately complete the testing work of the processes. On one hand, a great deal of precious time of staff can be saved, the problem of higher-value and more challenging testing work can be solved, and on the other hand, the situations that manual regression is not covered fully and service use is affected after production is put on line can be reduced.
And step 209, generating a software test report according to the software test result.
The test report is used for reporting specific execution conditions of the software test process corresponding to the software automatic test request to the tester. The tester can know the specific condition of the software test based on the test report, so that the actual use effect of the software to be tested is effectively determined, and whether the software to be tested meets the expected requirement is judged.
The software automatic test method comprises the steps of searching software to be tested and a test scene designated by the software automatic test request after the software automatic test request is acquired, searching a preset script library according to the software to be tested and the test scene, acquiring an RPA test execution script corresponding to the automatic test request, executing the RPA test execution script, calling an artificial intelligent component corresponding to the test scene in the execution process of the RPA test execution script so as to perform software test on the software to be tested in the test scene, acquiring a software test result, and generating a software test report according to the software test result. According to the application, the software to be tested and the test scene are determined based on the software automatic test request, so that the RPA test execution script is obtained, the regular and repeated workflow tasks in the software test process are executed based on the RPA test execution script, and meanwhile, the artificial test process is better simulated by means of the artificial intelligent component in combination with the specific test scene, so that the automatic test efficiency is effectively improved.
In one embodiment, the method further comprises, before step 203, recording test flows corresponding to the multiple test scenes by means of execution scripts based on the RPA bottom flow engine and the component library through a visualized RPA editor, generating RPA test execution scripts corresponding to the test scenes, and constructing a preset script library based on the RPA test execution scripts in the test scenes.
Specifically, a tester can automatically test the server 104 through software, record the test flows corresponding to a plurality of test operation scenes step by step through a visual RPA editor based on an RPA bottom flow engine and a component library in a script execution mode, write corresponding test execution scripts, and then complete the collection of test scripts based on respective service scenes. One script corresponds to one test scenario, and one scenario corresponds to a plurality of tests. The test script and the test scene are internally associated through names. The visual RPA editor editing process specifically means that a tester can complete the editing work of a test execution script through a visual integrated development environment in a dragging mode to generate the test execution script. In this embodiment, through the visualized RPA editor, the RPA test execution script corresponding to each test scene can be written more intuitively and effectively, so as to ensure the efficiency of the RPA test execution script.
In one embodiment, as shown in FIG. 3, step 207 comprises:
Step 302, obtaining scene attributes of a test scene, and determining an artificial intelligent component corresponding to the scene attributes and a calling node of the artificial intelligent component.
And step 304, when the RPA test execution script is executed to the calling node, calling an artificial intelligent component corresponding to the scene attribute to perform software test on the software to be tested under the test scene, and obtaining a software test result.
The RPA test execution script simulates a manual test according to a set rule, and interacts with a computer to process the operation, thereby completing the software test. Thus, several nodes may be added during the test, at which the test is completed by means of artificial intelligence components. For example, when intelligent examination sheets are involved in a test scene, it may be determined that an artificial intelligent component corresponding to a scene attribute is a text recognition component, and a call node is an examination sheet node. And then, when the RPA test execution script is executed to the examination node, completing a test task under the test scene through the text recognition component. When the online customer service and other scenes are designed in the test scene, the artificial intelligent component corresponding to the scene attribute can be determined to be the dialogue robot component, and the calling node is the dialogue node. When the RPA test execution script is executed to the session node, the corresponding test task is completed through the session robot component. In this embodiment, by determining the artificial intelligence component and the call node corresponding to the scene attribute, accurate execution of the software test can be ensured by the RPA component in the process of the software test.
In one embodiment, as shown in fig. 4, further includes:
in step 401, when the RPA test execution script generates a running error, an error type of the error is identified.
And step 403, when the error type is a blocking error, generating and pushing an error prompt message corresponding to the error type based on the artificial intelligence.
And step 405, when the error type of the error is an operation error, searching an error processing scheme corresponding to the operation error based on the artificial intelligence, and processing the operation error of the RPA test execution script based on the error processing scheme.
Specifically, the scheme of the application can also increase an intelligent operation processing mechanism based on artificial intelligence in the software automation test process, when the RPA test execution script has running errors, the error type of the errors can be identified, and then corresponding processing is carried out based on the error type, so that the smooth running of the software automation test is ensured. In particular, intelligent error correction may be performed according to the type of error. When the technical problem that the class cannot be corrected is encountered, if the blocking error is encountered, an error prompt message corresponding to the error type is automatically generated and pushed based on artificial intelligence. For example, an error prompt mail or an error prompt short message can be sent to a mailbox of a preset test responsible person, so that the test responsible person is prompted to manually intervene in the automatic test flow to advance the next flow. If the information is the normal operation error prompt information, the information can be automatically judged and processed, namely, an error processing scheme corresponding to the operation error is searched based on the artificial intelligence, and the operation error of the RPA test execution script is processed based on the error processing scheme. Therefore, the testing flow of the software automatic test is not interrupted. And the efficiency of automatic testing of the software is ensured. In this embodiment, by intelligent error correction, the smooth execution of the software automation test flow can be effectively ensured, thereby improving the efficiency of the software automation test.
In one embodiment, step 403 includes looking up log information corresponding to the blocking error. And generating an error prompt message corresponding to the blocking error according to the log information. Pushing error prompt messages.
The log information refers to information recorded in a corresponding generated software running log in the software automatic test process.
In particular, when a blocking error is encountered, the solution to the problem cannot be performed through artificial intelligence. Therefore, the log information corresponding to the blocking error can be searched at this time, and the log information is helpful for locating the position where the error occurs and the specific information of the error. And then generating an error prompt message corresponding to the blocking error according to the log information, and pushing the error prompt message, wherein the error prompt message can be specifically pushed in the form of a mail or a short message, and the pushed object is specifically a test staff for software automation test. When receiving the error prompt message, the test staff can carry out error correction processing based on the position where the error appears and the specific information of the error in the error prompt message. In this embodiment, the error prompt message is generated according to the log information, which is helpful for the error repair efficiency.
In one embodiment, step 405 includes identifying an error type of the operation error based on the artificial intelligence when the error type of the error is the operation error, searching a preset scheme list, determining an error processing scheme corresponding to the error type, and processing a running error of the RPA test execution script based on the error processing scheme.
Specifically, for common operation errors, corresponding automatic processing schemes can be arranged in advance. And then, establishing an association between the operation error type and the automatic processing scheme to construct a preset scheme list. And when the error type of the error is an operation error, identifying the error type of the operation error based on the artificial intelligence. In one embodiment, the method can identify the error type of the operation error based on semantic identification in the artificial intelligence and error information, then search a preset scheme list to determine an error processing scheme corresponding to the error type, and process the operation error of the RPA test execution script based on the error processing scheme. In this embodiment, the operation errors in the automatic test process are processed by artificial intelligence and searching the preset scheme list, so that the error processing efficiency can be effectively improved, and the efficiency of the automatic test of software is ensured.
In one embodiment, as shown in fig. 5, after step 209, the method further includes:
step 502, obtaining a software defect in a software test result.
Step 504, generating error alert information based on the software defect.
Step 506, searching a development terminal corresponding to the software defect, and pushing error prompt information to the development terminal.
Specifically, in the software development process, different developers are responsible for developing different functional modules, or contents, in the software. The software testing process mainly determines whether the software to be tested has defects in a testing scene and what the defects of the software are. And generating error reminding information based on the software defects to remind developers of repairing the defects. In a specific embodiment, defects can be created one by one in a development collaboration space, corresponding error prompt information is generated, then a development terminal corresponding to the software defects is searched, and the software defects are sent to the development terminal through the error prompt information so as to remind specific developers to prompt repair work of the defects. In the embodiment, the error reminding information is generated based on the software defects, so that the defects of the software can be effectively repaired and reminded, and the repairing efficiency of the software to be tested is improved.
It should be understood that, although the steps in the flowcharts of fig. 2-5 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-5 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or steps.
In one embodiment, as shown in FIG. 6, there is provided a software automated testing apparatus comprising:
The request obtaining module 601 is configured to obtain a software automation test request, and find software to be tested and a test scenario specified by the software automation test request.
The script searching module 603 is configured to search a preset script library according to the software to be tested and the test scenario, and obtain an RPA test execution script corresponding to the automated test request.
The RPA call module 605 is configured to execute an RPA test execution script.
The software testing module 607 is configured to call an artificial intelligent component corresponding to the test scenario in the execution process of the RPA test execution script, so as to perform software testing on the software to be tested in the test scenario, and obtain a software testing result.
Report generating module 609 is configured to generate a software test report according to the software test result.
In one embodiment, the system further comprises a script writing module, wherein the script writing module is used for recording test flows corresponding to a plurality of test scenes in a script execution mode based on an RPA bottom flow engine and a component library and through a visual RPA editor to generate RPA test execution scripts corresponding to the test scenes, and constructing a preset script library based on the RPA test execution scripts in the test scenes.
In one embodiment, the software testing module 607 is specifically configured to obtain a scenario attribute of a test scenario, determine an artificial intelligent component corresponding to the scenario attribute and a call node of the artificial intelligent component, and call the artificial intelligent component corresponding to the scenario attribute when the RPA test execution script is executed to the call node, so as to perform a software test on software to be tested in the test scenario, and obtain a software test result.
In one embodiment, the system further comprises an error correction module, wherein the error correction module is used for identifying the error type of the error when the RPA test execution script generates the operation error, generating and pushing an error prompt message corresponding to the error type based on the artificial intelligence when the error type is a blocking error, and searching an error processing scheme corresponding to the operation error based on the artificial intelligence when the error type is an operation error, and processing the operation error of the RPA test execution script based on the error processing scheme.
In one embodiment, the error correction module is specifically configured to search log information corresponding to the blocking error, generate an error prompt message corresponding to the blocking error according to the log information, and push the error prompt message.
In one embodiment, the error correction module is further configured to identify an error type of the operation error based on the artificial intelligence when the error type of the error is the operation error, search a preset scheme list, determine an error processing scheme corresponding to the error type, and process a running error of the RPA test execution script based on the error processing scheme.
In one embodiment, the system further comprises a defect report module, wherein the defect report module is used for acquiring software defects in the software test results, generating error reminding information based on the software defects, searching a development terminal corresponding to the software defects, and pushing the error reminding information to the development terminal.
For specific embodiments of the software automation test device, reference may be made to the embodiments of the software automation test method, which are not described herein. The various modules in the software automated test equipment described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store software automated test data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a software automated test method.
It will be appreciated by those skilled in the art that the structure shown in FIG. 7 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory storing a computer program and a processor that when executing the computer program performs the steps of:
acquiring a software automatic test request, and searching software to be tested and a test scene designated by the software automatic test request;
Searching a preset script library according to the software to be tested and the test scene, and acquiring an RPA test execution script corresponding to the automatic test request;
Executing RPA test execution script;
in the execution process of the RPA test execution script, calling an artificial intelligent component corresponding to a test scene to perform software test on the software to be tested under the test scene to obtain a software test result;
And generating a software test report according to the software test result.
In one embodiment, the processor further realizes the following steps of recording test flows corresponding to a plurality of test scenes by means of an execution script based on the RPA bottom flow engine and the component library through a visualized RPA editor to generate RPA test execution scripts corresponding to the test scenes, and constructing a preset script library based on the RPA test execution scripts in the test scenes.
In one embodiment, the processor further comprises the steps of acquiring scene attributes of the test scene, determining an artificial intelligent component corresponding to the scene attributes and a calling node of the artificial intelligent component, and calling the artificial intelligent component corresponding to the scene attributes when the RPA test execution script is executed to the calling node so as to perform software testing on the software to be tested in the test scene and acquire a software test result.
In one embodiment, the processor further performs the steps of identifying an error type of the error when the RPA test execution script presents the operation error, generating and pushing an error prompt message corresponding to the error type based on the artificial intelligence when the error type is a blocking error, and processing the operation error of the RPA test execution script based on the error processing scheme when the error type of the error is an operation error based on the artificial intelligence search operation error.
In one embodiment, the processor when executing the computer program further performs the steps of searching log information corresponding to the blocking error, generating an error prompt message corresponding to the blocking error according to the log information, and pushing the error prompt message.
In one embodiment, when the processor executes the computer program, the following steps are further realized, namely when the error type of the error is an operation error, the operation error type is identified based on artificial intelligence, a preset scheme list is searched, an error processing scheme corresponding to the error type is determined, and the operation error of the RPA test execution script is processed based on the error processing scheme.
In one embodiment, the processor further comprises the steps of obtaining software defects in the software test results, generating error reminding information based on the software defects, searching a development terminal corresponding to the software defects, and pushing the error reminding information to the development terminal when executing the computer program.
In one embodiment, a computer storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a software automatic test request, and searching software to be tested and a test scene designated by the software automatic test request;
Searching a preset script library according to the software to be tested and the test scene, and acquiring an RPA test execution script corresponding to the automatic test request;
Executing RPA test execution script;
in the execution process of the RPA test execution script, calling an artificial intelligent component corresponding to a test scene to perform software test on the software to be tested under the test scene to obtain a software test result;
And generating a software test report according to the software test result.
In one embodiment, the computer program when executed by the processor further comprises the steps of recording test flows corresponding to the plurality of test scenes by means of execution scripts based on the RPA bottom flow engine and the component library through a visualized RPA editor, generating RPA test execution scripts corresponding to the test scenes, and constructing a preset script library based on the RPA test execution scripts under the test scenes.
In one embodiment, the computer program when executed by the processor further comprises the steps of obtaining scene attributes of the test scene, determining artificial intelligent components corresponding to the scene attributes and call nodes of the artificial intelligent components, and calling the artificial intelligent components corresponding to the scene attributes when the RPA test execution script is executed to the call nodes so as to perform software testing on the software to be tested in the test scene, and obtaining software test results.
In one embodiment, the computer program when executed by the processor further implements the steps of identifying an error type of the error when the RPA test execution script presents an operation error, generating and pushing an error prompt message corresponding to the error type based on the artificial intelligence when the error type is a blocking error, and processing the operation error of the RPA test execution script based on the error processing scheme when the error type of the error is an operation error based on the artificial intelligence search operation error corresponding error processing scheme.
In one embodiment, the computer program when executed by the processor further performs the steps of searching for log information corresponding to the blocking error, generating an error prompt message corresponding to the blocking error based on the log information, and pushing the error prompt message.
In one embodiment, the computer program when executed by the processor further performs the steps of identifying the error type of the operation error based on the artificial intelligence when the error type of the error is the operation error, searching a preset scheme list, determining an error processing scheme corresponding to the error type, and processing the running error of the RPA test execution script based on the error processing scheme.
In one embodiment, the computer program when executed by the processor further comprises the steps of obtaining software defects in the software test results, generating error reminding information based on the software defects, searching a development terminal corresponding to the software defects, and pushing the error reminding information to the development terminal.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.