CN113886262A - Software automation test method and device, computer equipment and storage medium - Google Patents
Software automation test method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to the field of automated testing tools for research and development management, in particular to a software automated testing method, a software automated testing device, computer equipment and a storage medium. After acquiring a software automatic test request, searching software to be tested and a test scene specified by the software automatic test request; searching a preset script library, and acquiring an RPA test execution script corresponding to the automatic test request; executing the RPA test execution script; in the process of executing the RPA test execution script, calling an artificial intelligence component corresponding to a test scene so as to perform software test on software to be tested in the test scene and obtain a software test result; and generating a software test report according to the software test result. The method and the device have the advantages that regular and repeated workflow tasks in the software testing process are executed based on the RPA testing execution script, and meanwhile, the manual testing process is better simulated by means of the artificial intelligence assembly by combining a specific testing scene, so that the automatic testing efficiency is effectively improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a software automation test method, apparatus, computer device, and storage medium.
Background
Software testing is the process of running or testing a software system using manual or automated means with the purpose of checking whether it meets specified requirements or to figure out differences between expected and actual results. The process of typical software testing is usually that after a test case is designed and passes review, a tester performs the test step by step according to the procedures described in the test case, and obtains a comparison between the actual result and the expected result. Automated testing is a process that translates human-driven testing behavior into machine execution. Automated testing can take advantage of the machine's resistance to accomplishing a given test goal, such as stress testing that simulates a large number of user scenarios, when encountering tasks that manual testing is not possible to accomplish. The execution of the automatic test needs to compile a large number of test scripts to support simple and repetitive test work, each test script is a test case, and large-scale application scenes with flowerliness are difficult to be connected in series.
However, the existing test scripts used in the automated test have high maintenance cost, and often need to be updated synchronously with the update of the application, and the modification is frequent. Thereby affecting the testing efficiency of the automated testing.
Disclosure of Invention
In view of the above, there is a need to provide a software automation testing method, device, computer device and storage medium capable of more effectively improving the efficiency of automation testing.
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 specified 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 (robot process automation) test execution script corresponding to the automatic test request;
executing the RPA test execution script;
in the process of executing the RPA test execution script, calling an artificial intelligence component corresponding to the test scene so as to perform software test on the software to be tested in the test scene and obtain a software test result;
and generating a software test report according to the software test result.
In one embodiment, before searching a preset script library according to the software to be tested and the test scenario and acquiring an RPA test execution script corresponding to the automated test request, the method further includes:
based on an RPA bottom layer process engine and a component library, recording test processes corresponding to a plurality of test scenes in a script executing mode through a visual RPA editor, and generating an RPA test execution script corresponding to each test scene;
and constructing a preset script library based on the RPA test execution script under each test scene.
In one embodiment, in the process of executing the RPA test execution script, invoking an 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 artificial intelligence components corresponding to the scene attributes and calling nodes of the artificial intelligence components;
when the RPA test execution script is executed to the calling node, calling an artificial intelligence component corresponding to the scene attribute so as to perform software test on the software to be tested in the test scene and obtain a software test result.
In one embodiment, the method further comprises:
when the RPA test execution script has 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 running 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 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;
and 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 error of the RPA test execution script based on the error processing scheme.
In one embodiment, after generating a software test report according to the software test result, the method further includes:
acquiring software defects in the software test result;
generating error reminding information based on the software defect;
and searching a development terminal corresponding to the software defect, and pushing the error prompt information to the development terminal.
A software automation testing apparatus, the apparatus comprising:
the request acquisition module is used for acquiring a software automation test request and searching the software to be tested and a test scene specified 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 intelligence component corresponding to the testing scene in the execution process of the RPA testing 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 and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a software automation test request, and searching software to be tested and a test scene specified 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 process of executing the RPA test execution script, calling an artificial intelligence component corresponding to the test scene so as to perform software test on the software to be tested in the test scene and obtain a software test result;
and generating a software test report according to the software test result.
A computer storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of:
acquiring a software automation test request, and searching software to be tested and a test scene specified 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 process of executing the RPA test execution script, calling an artificial intelligence component corresponding to the test scene so as to perform software test on the software to be tested in the test scene and obtain a software test result;
and generating a software test report according to the software test result.
The software automatic test method, the device, the computer equipment and the storage medium are characterized in that after the software automatic test request is obtained, the software to be tested and a test scene specified by the software automatic test request are searched; searching a preset script library according to software to be tested and a test scene, and acquiring an RPA test execution script corresponding to the automatic test request; executing the RPA test execution script; in the process of executing the RPA test execution script, calling an artificial intelligence component corresponding to a test scene so as to perform software test on software to be tested in the test scene and obtain a software test result; and generating a software test report according to the software test result. 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, regular and repeated work flow tasks in the software test process are executed based on the RPA test execution script, meanwhile, the artificial test process is better simulated by means of the artificial intelligence assembly by combining the specific test scene, and the automatic test efficiency is effectively improved.
Drawings
FIG. 1 is a diagram illustrating an exemplary implementation of a software automation test methodology;
FIG. 2 is a flow diagram illustrating a method for automated testing of software, according to one embodiment;
FIG. 3 is a schematic sub-flow chart of step 207 of FIG. 2 in one embodiment;
FIG. 4 is a flow diagram illustrating the operation of the error correction step in one embodiment;
FIG. 5 is a flowchart illustrating the software defect notification step in one embodiment;
FIG. 6 is a block diagram of an embodiment of an apparatus for automated testing of software;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes 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 the like.
The application specifically provides a software automation testing method, which can be applied to the 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 software to be tested to the software automation test server 104. After receiving the software automation test request, the software automation test server 104 searches for the software to be tested and the test scene specified by the software automation test request; searching a preset script library according to software to be tested and a test scene, and acquiring an RPA test execution script corresponding to the automatic test request; executing the RPA test execution script; in the process of executing the RPA test execution script, calling an artificial intelligence component corresponding to a test scene so as to perform software test on software to be tested in the test scene and obtain a software test result; and generating a software test report according to the software test result. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the software automation test server 104 may be an independent server, or a cloud server that provides basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), and a big data and artificial intelligence platform.
In one embodiment, as shown in fig. 2, a software automation test method is provided, which is described by taking the method as an example applied to the software automation test server 104 in fig. 1, and includes the following steps:
step 201, acquiring a software automation test request, and searching software to be tested and a test scene specified by the software automation test request.
The automated testing is a process for converting a test behavior driven by human into machine execution. Typically, after a test case is designed and passes review, the test is performed step by a tester according to the procedures described in the test case, 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 testing efficiency, the concept of automatic testing is introduced. The processing efficiency of the software testing process is improved through machine execution. The software automation test request is specifically used for requesting the automation server to automatically test the specified software to be tested, and the software automation test request can be specifically generated according to a test case corresponding to the software to be tested, wherein the test case is description of a test task performed on a specific software product and embodies a test scheme, a method, a technology and a strategy. The contents include test targets, test environments, input data, test steps, expected results, test scripts, etc. It can be simply understood that a test case is a set of test inputs, execution conditions, and expected results tailored for a particular purpose to verify whether a particular software requirement is met. And the software to be tested is the test target of the software automatic test method in the scheme. The test scenario refers to a service scenario faced by software to be tested, which is constructed through automated testing.
The software automatic testing is specifically carried out by using the RPA technology, and the testing efficiency is improved. An RPA, a robotic process automation system, is an application that provides another way to automate an end user's manual process by mimicking the way an end user manually operates on a computer. In conducting the test, the test worker 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 multiple pages, multiple checking logics and complex interactive operation, and relatively depends on historical test experiences of testers, and the test experiences are difficult to inherit. The application is that the RPA robot is introduced into the software automatic test, the test flows under the scenes of pre-project establishment, pre-examination, decision-making, examination and approval printing during the investment, post-project management, quit management and the like of the investment business flow are solidified, and the machine test replaces the manual test, so that the range of regression test can be enlarged, the labor cost is reduced, and the quality improvement, the cost reduction and the efficiency improvement are realized.
Step 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 is composed of pre-written RPA test execution scripts under different scenes. The RPA test execution script generally refers to a series of instructions for a specific test, which may be executed by an automated test tool, and in this application refers to an RPA executor. To improve the maintainability and reusability of test scripts, they must be built before they are executed. It may be found that some operations will occur during several tests. Therefore, the destination should determine the targets of these operations so that their implementations can be multiplexed. A test script is computer readable instructions that automatically perform a test procedure (or a portion of a test procedure). The test script can be created (recorded) or automatically generated by using a test automation tool, or can be completed by programming in a programming language, or can be completed by integrating the first three methods
Specifically, the process of the automated testing may generally be performed by a pre-written test execution script, and in this step, the software automation testing server 104 may search a preset script library based on a given test scenario to obtain a corresponding RPA test execution script in the scenario. The script may then be executed based on the RPA test to perform the software test. 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 the RPA test execution script.
And step 207, in the process of executing the RPA test execution script, calling an artificial intelligence component corresponding to the test scene so as to perform software test on the software to be tested in the test scene, and acquiring a software test result.
The artificial intelligence component is a component which needs to flexibly process part in the testing process by an artificial intelligence technology.
Specifically, the artificial intelligence component can be used for completing test tasks in test scenes such as text recognition and a conversation robot in the test process. And the RPA executor manufactures test data through the test execution script and outputs the test result of each test case so as to complete the test task of the relevant scene. In the testing process, repeated workflow tasks based on rules can be processed by means of an RPA digital tool, and the workflow is connected in series based on an RPA testing execution script, so that the manual testing process can be better simulated. For example, in the investment full-flow business of an enterprise, certain procedural specified operation actions such as establishment pre-review decision making, file uploading, opinion solicitation, examination and approval chain configuration selection, application by impression and the like are involved. The software testing robot manufactured by applying the RPA technology can quickly and accurately complete the testing work of the processes. On one hand, a large amount of precious time of the staff can be saved, more valuable and more challenging testing work can be solved, and on the other hand, the situations that manual regression coverage is incomplete and service use is influenced after the staff are put into production and brought 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 the specific execution condition of the software test process corresponding to the software automation test request to a tester. The tester can know the specific conditions of the software test based on the test report, thereby effectively determining the actual use effect of the software to be tested and judging whether the actual use effect meets the expected requirements.
According to the software automatic test method, after the software automatic test request is obtained, the software to be tested and the test scene specified by the software automatic test request are searched; searching a preset script library according to software to be tested and a test scene, and acquiring an RPA test execution script corresponding to the automatic test request; executing the RPA test execution script; in the process of executing the RPA test execution script, calling an artificial intelligence component corresponding to a test scene so as to perform software test on software to be tested in the test scene and obtain a software test result; and generating a software test report according to the software test result. 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, regular and repeated work flow tasks in the software test process are executed based on the RPA test execution script, meanwhile, the artificial test process is better simulated by means of the artificial intelligence assembly by combining the specific test scene, and the automatic test efficiency is effectively improved.
In one embodiment, before step 203, the method further includes: based on an RPA bottom layer process engine and a component library, recording test processes corresponding to a plurality of test scenes in a script executing mode through a visual RPA editor, and generating an RPA test execution script corresponding to each test scene; and constructing a preset script library based on the RPA test execution script under each test scene.
Specifically, a tester can record test flows corresponding to a plurality of test job scenarios step by step in a script execution manner through a visual RPA editor based on an RPA bottom-layer flow engine and a component library through the software automation test server 104, write corresponding test execution scripts, and then complete collection of test scripts based on respective service scenarios. One script corresponds to one test scenario, and one scenario corresponds to a plurality of tests. The test script and the test scenario are internally associated through names. The visual editing process of the RPA editor 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 visual RPA editor, the RPA test execution script corresponding to each test scenario can be written more intuitively and effectively, and the efficiency of the RPA test execution script is ensured.
In one embodiment, as shown in FIG. 3, step 207 comprises:
step 302, obtaining a scene attribute of the test scene, and determining an artificial intelligence component corresponding to the scene attribute and a calling node of the artificial intelligence component.
And 304, when the RPA test execution script is executed to the calling node, calling an artificial intelligence component corresponding to the scene attribute so as to perform software test on the software to be tested in the test scene and obtain a software test result.
The RPA test execution script simulates manual test according to a set rule and interacts with a computer to process operation, so that software test is completed. Therefore, several nodes can be added in the testing process, and the testing is completed on the nodes by means of the artificial intelligence component. For example, when an intelligent examination order is involved in a test scene, it can be determined that an artificial intelligence component corresponding to a scene attribute is a text recognition component, and a calling node is an examination order node. And then when the RPA test execution script is executed to the examination node, the test task under the test scene is completed through the text recognition component. When scenes such as online customer service and the like are designed in a test scene, the artificial intelligence component corresponding to the scene attribute can be determined to be a conversation robot component, and the calling node is a conversation node. When the RPA test execution script is executed to the dialogue node, the corresponding test task is completed through the dialogue robot component. In the embodiment, the artificial intelligence component and the calling node corresponding to the scene attribute are determined, so that the software test can be accurately executed through the RPA component in the software test process.
In one embodiment, as shown in fig. 4, the method further includes:
step 401, when the RPA test execution script has an operation error, identifying an error type of the error.
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 artificial intelligence.
And 405, 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 running error of the RPA test execution script based on the error processing scheme.
Specifically, the scheme of the application can also increase an intelligent job processing mechanism based on artificial intelligence in the software automation test process, when the RPA test execution script has an operation error, the error type of the error can be identified, and then corresponding processing is performed based on the error type, so that the smooth performance of the software automation test is ensured. Specifically, intelligent error correction can be performed according to the error type. And when the technical problem that the class cannot be corrected is met, if a blocking error is met, 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 may be sent to a mailbox of a preset test responsible person, so that the test responsible person is prompted to manually intervene in the automated test process to advance the next process. If the operation error prompt information is the general operation error prompt information, the operation error prompt information can be automatically judged and processed, namely an error processing scheme corresponding to the operation error is searched based on artificial intelligence; and processing the running error of the RPA test execution script based on an error processing scheme. Therefore, the test flow of the software automatic test is not interrupted. The efficiency of software automation test is guaranteed. In the embodiment, through intelligent error correction, smooth progress of the software automation test process can be effectively ensured, and thus the efficiency of the software automation test is improved.
In one embodiment, step 403 includes: and searching log information corresponding to the blocking errors. And generating an error prompt message corresponding to the blocking error according to the log information. And pushing an error prompt message.
The log information refers to information recorded in a software running log generated correspondingly in the software automatic testing process.
Specifically, when a blocking error is encountered, the problem solving cannot be performed by artificial intelligence. Therefore, 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 pushed in a form of a mail or a short message, and the pushed object is a test worker for software automation test. When receiving the error prompt message, the test worker can perform error correction processing based on the position where the error occurs 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 recovery efficiency.
In one embodiment, step 405 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 error of the RPA test execution script based on an error processing scheme.
Specifically, for common operation errors, corresponding automatic processing schemes can be sorted out 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 artificial intelligence. In one embodiment, the error type of the operation error can be identified based on the error information based on semantic identification in artificial intelligence, and then a preset scheme list is searched to determine an error processing scheme corresponding to the error type; and processing the running error of the RPA test execution script based on an error processing scheme. In the embodiment, the operation errors in the automatic testing 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 software automatic testing is ensured.
In one embodiment, as shown in fig. 5, after step 209, the method further includes:
step 502, acquiring software defects in the software test result.
And step 504, generating error reminding information based on the software defect.
Step 506, finding 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 content, in the software. The software testing process mainly determines whether the software to be tested has defects in the testing scene and what the defects of the software appear. And then generating error reminding information based on the software defect to remind developers to repair the defect. In a specific embodiment, the defects can be created one by one in the development cooperation space, corresponding error prompt information is generated, then the 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 promote the repair work of the defects. In the embodiment, the error reminding information is generated based on the software defect, so that the defect repairing reminding can be effectively carried out on the software, and the repairing efficiency of the software to be tested is improved.
It should be understood that although the various steps in the flow charts of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-5 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 6, there is provided a software automation test apparatus including:
the request obtaining module 601 is configured to obtain a software automation test request, and search for 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.
And an RPA calling module 605 for executing the RPA test execution script.
The software testing module 607 is configured to invoke the artificial intelligence component corresponding to the test scenario in the process of executing the RPA test execution script, so as to perform a software test on the software to be tested in the test scenario, and obtain a software test result.
And a report generating module 609, configured to generate a software test report according to the software test result.
In one embodiment, the script writing module is further included for: based on an RPA bottom layer process engine and a component library, recording test processes corresponding to a plurality of test scenes in a script executing mode through a visual RPA editor, and generating an RPA test execution script corresponding to each test scene; and constructing a preset script library based on the RPA test execution script under each test scene.
In one embodiment, the software testing module 607 is specifically configured to: acquiring scene attributes of a test scene, and determining artificial intelligence components corresponding to the scene attributes and calling nodes of the artificial intelligence components; and when the RPA test execution script is executed to the calling node, calling the artificial intelligence component corresponding to the scene attribute so as to perform software test on the software to be tested in the test scene and obtain a software test result.
In one embodiment, the apparatus further comprises an error correction module configured to: when the RPA test execution script has operation errors, identifying error types of the errors; 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 the artificial intelligence, and processing the running error of the RPA test execution script based on the error processing scheme.
In one embodiment, the error correction module is specifically configured to: searching log information corresponding to the blocking errors; generating an error prompt message corresponding to the blocking error according to the log information; and pushing an error prompt message.
In one embodiment, the error correction module is further configured to: 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 error of the RPA test execution script based on an error processing scheme.
In one embodiment, the system further comprises a defect reporting module, configured to: acquiring software defects in a software test result; generating error reminding information based on the software defect; and searching a development terminal corresponding to the software defect, and pushing error prompt information to the development terminal.
For specific embodiments of the software automation test apparatus, reference may be made to the above embodiments of the software automation test method, and details are not described herein again. The modules in the software automation testing device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the 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 comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing software automation test data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a software automation testing method.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program:
acquiring a software automation test request, and searching software to be tested and a test scene specified by the software automation test request;
searching a preset script library according to software to be tested and a test scene, and acquiring an RPA test execution script corresponding to the automatic test request;
executing the RPA test execution script;
in the process of executing the RPA test execution script, calling an artificial intelligence component corresponding to a test scene so as to perform software test on software to be tested in the test scene and obtain a software test result;
and generating a software test report according to the software test result.
In one embodiment, the processor, when executing the computer program, further performs the steps of: based on an RPA bottom layer process engine and a component library, recording test processes corresponding to a plurality of test scenes in a script executing mode through a visual RPA editor, and generating an RPA test execution script corresponding to each test scene; and constructing a preset script library based on the RPA test execution script under each test scene.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring scene attributes of a test scene, and determining artificial intelligence components corresponding to the scene attributes and calling nodes of the artificial intelligence components; and when the RPA test execution script is executed to the calling node, calling the artificial intelligence component corresponding to the scene attribute so as to perform software test on the software to be tested in the test scene and obtain a software test result.
In one embodiment, the processor, when executing the computer program, further performs the steps of: when the RPA test execution script has operation errors, identifying error types of the errors; 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 the artificial intelligence, and processing the running error of the RPA test execution script based on the error processing scheme.
In one embodiment, the processor, when executing the computer program, further performs the steps of: searching log information corresponding to the blocking errors; generating an error prompt message corresponding to the blocking error according to the log information; and pushing an error prompt message.
In one embodiment, the processor, when executing the computer program, further performs the steps of: 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 error of the RPA test execution script based on an error processing scheme.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring software defects in a software test result; generating error reminding information based on the software defect; and searching a development terminal corresponding to the software defect, and pushing error prompt information to the development terminal.
In one embodiment, a computer storage medium is provided, having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of:
acquiring a software automation test request, and searching software to be tested and a test scene specified by the software automation test request;
searching a preset script library according to software to be tested and a test scene, and acquiring an RPA test execution script corresponding to the automatic test request;
executing the RPA test execution script;
in the process of executing the RPA test execution script, calling an artificial intelligence component corresponding to a test scene so as to perform software test on software to be tested in the test scene and 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 performs the steps of: based on an RPA bottom layer process engine and a component library, recording test processes corresponding to a plurality of test scenes in a script executing mode through a visual RPA editor, and generating an RPA test execution script corresponding to each test scene; and constructing a preset script library based on the RPA test execution script under each test scene.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring scene attributes of a test scene, and determining artificial intelligence components corresponding to the scene attributes and calling nodes of the artificial intelligence components; and when the RPA test execution script is executed to the calling node, calling the artificial intelligence component corresponding to the scene attribute so as to perform software test on the software to be tested in the test scene and obtain a software test result.
In one embodiment, the computer program when executed by the processor further performs the steps of: when the RPA test execution script has operation errors, identifying error types of the errors; 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 the artificial intelligence, 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 performs the steps of: searching log information corresponding to the blocking errors; generating an error prompt message corresponding to the blocking error according to the log information; and pushing an error prompt message.
In one embodiment, the computer program when executed by the processor further performs the steps of: 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 error of the RPA test execution script based on an error processing scheme.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring software defects in a software test result; generating error reminding information based on the software defect; and searching a development terminal corresponding to the software defect, and pushing error prompt information to the development terminal.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile 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 DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. 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 specified 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 process of executing the RPA test execution script, calling an artificial intelligence component corresponding to the test scene so as to perform software test on the software to be tested in the test scene and obtain a software test result;
and generating a software test report according to the software test result.
2. The method of claim 1, wherein before searching for a preset script library according to the software to be tested and the test scenario and obtaining an RPA test execution script corresponding to the automated test request, the method further comprises:
based on an RPA bottom layer process engine and a component library, recording test processes corresponding to a plurality of test scenes in a script executing mode through a visual RPA editor, and generating an RPA test execution script corresponding to each test scene;
and constructing a preset script library based on the RPA test execution script under each test scene.
3. The method according to claim 1, wherein in the RPA test execution script execution process, invoking an 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 comprises:
acquiring scene attributes of the test scene, and determining artificial intelligence components corresponding to the scene attributes and calling nodes of the artificial intelligence components;
when the RPA test execution script is executed to the calling node, calling an artificial intelligence component corresponding to the scene attribute so as to perform software test on the software to be tested in the test scene and obtain a software test result.
4. The method of claim 1, further comprising:
when the RPA test execution script has 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 running error of the RPA test execution script based on the error processing scheme.
5. The method of claim 4, wherein generating and pushing the error prompt message corresponding to the error type based on the artificial intelligence comprises:
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.
6. The method of claim 4, wherein processing the running error of the RPA test execution script based on the artificial intelligence and the error type of the error comprises:
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 error of the RPA test execution script based on the error processing scheme.
7. The method of claim 1, wherein after generating a software test report according to the software test result, further comprising:
acquiring software defects in the software test result;
generating error reminding information based on the software defect;
and searching a development terminal corresponding to the software defect, and pushing the error prompt information to the development terminal.
8. An automated software testing apparatus, the apparatus comprising:
the request acquisition module is used for acquiring a software automation test request and searching the software to be tested and a test scene specified 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 intelligence component corresponding to the testing scene in the execution process of the RPA testing 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.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer storage medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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