CN114003493A - Test data processing method, device, equipment and storage medium - Google Patents
Test data processing method, device, equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3692—Test management for test results analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention relates to the field of artificial intelligence, and discloses a test data processing method, a test data processing device, test data processing equipment and a storage medium, which are used for improving the efficiency of test data processing. The test data processing method comprises the following steps: acquiring an initial image corresponding to test data to be processed; preprocessing the initial image to obtain a standard image corresponding to the initial image; inputting the standard image into a preset test data analysis model for test analysis to obtain a test element corresponding to the standard image, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer; and calling a preset case database to perform test case matching on the test elements to obtain target test cases corresponding to the test elements. In addition, the invention also relates to a block chain technology, and the target test case can be stored in the block chain node.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a test data processing method, a test data processing device, test data processing equipment and a storage medium.
Background
With the rapid development of computer technology, the software industry has also developed at a rapid pace. The test belongs to an important link in the software development process, the stability and compatibility of the developed software can be verified through case test, software errors and defects can be found, and the developed software has an opportunity to be popularized online only if the test passes.
In the process of software development, a test scenario in which multiple functions need to be executed simultaneously is usually encountered, and if a developer is completely relied on to write and combine each test program in each test process, the test efficiency of the developed software is seriously affected, and the processing efficiency of the test case is low.
Disclosure of Invention
The invention provides a test data processing method, a test data processing device, test data processing equipment and a storage medium, which are used for improving the efficiency of test data processing.
The first aspect of the present invention provides a test data processing method, including: inquiring test data to be processed from a preset case database, and acquiring an initial image corresponding to the test data; preprocessing the initial image to obtain a standard image corresponding to the initial image; inputting the standard image into a preset test data analysis model for test analysis to obtain a test element corresponding to the standard image, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer; and calling a preset case database to perform test case matching on the test elements to obtain target test cases corresponding to the test elements.
Optionally, in a first implementation manner of the first aspect of the present invention, before querying the test data to be processed from the preset case database and acquiring the initial image corresponding to the test data, the test data processing method further includes: obtaining a plurality of sample test data images and a training model; performing element extraction on the plurality of sample test data images to obtain a plurality of training elements in each sample test data image; taking a plurality of training elements in each sample test data image as training sample data; inputting the training sample data into the training model for model training, and taking the training model after training as a test data analysis model.
Optionally, in a second implementation manner of the first aspect of the present invention, the preprocessing the initial image to obtain a standard image corresponding to the initial image includes: carrying out graying processing on the initial image to obtain a grayed image; carrying out binarization processing on the grayed image to obtain a binarized image; carrying out noise reduction processing on the binary image to obtain a noise reduction image; and carrying out edge detection on the noise-reduced image and removing a background image to obtain a standard image.
Optionally, in a third implementation manner of the first aspect of the present invention, the inputting the standard image into a preset test data analysis model for test analysis to obtain a test element corresponding to the standard image, where the preset test data analysis model includes an input layer, a hidden layer, and an output layer, and includes: inputting the standard image into a preset test data analysis model, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer; performing feature extraction on the standard image through the input layer to obtain image feature data; carrying out nonlinear processing on the image characteristic data through the hidden layer to obtain nonlinear data; and extracting elements of the nonlinear data through the output layer to obtain test elements corresponding to the standard image.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the invoking a preset case database to perform test case matching on the test element to obtain a target test case corresponding to the test element includes: acquiring an element type corresponding to a test element, wherein the element type comprises an operating system, a control and a page general function; and matching the test cases corresponding to the test elements from a preset case database according to the element types to obtain the target test case.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the matching, according to the element type, the test case corresponding to the test element from a preset case database to obtain the target test case includes: if the element type is the operating system, extracting a compatible test case of the operating system from a case database, and taking the compatible test case as a target test case; if the element type is the control, extracting a control test case from a case database, and taking the control test case as a target test case; and if the element type is the universal function of the page, extracting a page test case from a case database, and taking the page test case as a target test case.
Optionally, in a sixth implementation manner of the first aspect of the present invention, after the invoking a preset case database and respectively performing test case matching on the test elements to obtain a target test case corresponding to each test element, the test data processing method further includes: obtaining iteration information corresponding to the target test case; case evaluation is carried out on the target test case based on the iteration information to obtain an evaluation result; and adjusting the target test case based on the evaluation result.
A second aspect of the present invention provides a test data processing apparatus comprising: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for inquiring test data to be processed from a preset case database and acquiring an initial image corresponding to the test data; the preprocessing module is used for preprocessing the initial image to obtain a standard image corresponding to the initial image; the processing module is used for inputting the standard image into a preset test data analysis model for test analysis to obtain a test element corresponding to the standard image, and the preset test data analysis model comprises an input layer, a hidden layer and an output layer; and the generating module is used for calling a preset case database to perform test case matching on the test elements to obtain target test cases corresponding to the test elements.
Optionally, in a first implementation manner of the second aspect of the present invention, the test data processing apparatus further includes: the training module is used for acquiring a plurality of sample test data images and training models; performing element extraction on the plurality of sample test data images to obtain a plurality of training elements in each sample test data image; taking a plurality of training elements in each sample test data image as training sample data; inputting the training sample data into the training model for model training, and taking the training model after training as a test data analysis model.
Optionally, in a second implementation manner of the second aspect of the present invention, the preprocessing module is specifically configured to: carrying out graying processing on the initial image to obtain a grayed image; carrying out binarization processing on the grayed image to obtain a binarized image; carrying out noise reduction processing on the binary image to obtain a noise reduction image; and carrying out edge detection on the noise-reduced image and removing a background image to obtain a standard image.
Optionally, in a third implementation manner of the second aspect of the present invention, the processing module is specifically configured to: inputting the standard image into a preset test data analysis model, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer; performing feature extraction on the standard image through the input layer to obtain image feature data; carrying out nonlinear processing on the image characteristic data through the hidden layer to obtain nonlinear data; and extracting elements of the nonlinear data through the output layer to obtain test elements corresponding to the standard image.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the generating module specifically further includes: the identification unit is used for acquiring an element type corresponding to the test element, wherein the element type comprises an operating system, a control and a page general function; and the matching unit is used for matching the test case corresponding to the test element from a preset case database according to the element type to obtain a target test case.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the matching unit is specifically configured to: if the element type is the operating system, extracting a compatible test case of the operating system from a case database, and taking the compatible test case as a target test case; if the element type is the control, extracting a control test case from a case database, and taking the control test case as a target test case; and if the element type is the universal function of the page, extracting a page test case from a case database, and taking the page test case as a target test case.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the test data processing apparatus further includes: the adjusting module is used for acquiring iteration information corresponding to the target test case; case evaluation is carried out on the target test case based on the iteration information to obtain an evaluation result; and adjusting the target test case based on the evaluation result.
A third aspect of the present invention provides a test data processing apparatus comprising: a memory and at least one processor, the memory having stored therein a computer program; the at least one processor calls the computer program in the memory to cause the test data processing apparatus to perform the test data processing method described above.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein a computer program which, when run on a computer, causes the computer to execute the above-described test data processing method.
In the technical scheme provided by the invention, test data to be processed is inquired, and an initial image corresponding to the test data is obtained; preprocessing the initial image to obtain a standard image; inputting the standard image into a preset test data analysis model for test analysis to obtain test elements; the preset case database is called to carry out test case matching on the test elements to obtain the target test cases corresponding to the test elements.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a test data processing method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another embodiment of a test data processing method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a test data processing apparatus according to the present invention;
FIG. 4 is a schematic diagram of another embodiment of a test data processing apparatus according to an embodiment of the present invention;
FIG. 5 is a diagram of an embodiment of a test data processing apparatus according to the embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a test data processing method, a test data processing device, test data processing equipment and a storage medium, which are used for improving the efficiency of test data processing. The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a detailed flow of an embodiment of the present invention is described below, and referring to fig. 1, a first embodiment of a test data processing method according to an embodiment of the present invention includes:
101. inquiring test data to be processed from a preset case database, and acquiring an initial image corresponding to the test data;
it is to be understood that the execution subject of the present invention may be a test data processing apparatus, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), and a big data and artificial intelligence platform.
The embodiment of the invention 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.
Specifically, the server extracts an initial image corresponding to a test case to be processed from a preset case database, the preset case database comprises a plurality of test cases, and the test cases are various test cases encountered in the test, so that the initial image can be extracted from the pictures of the test cases, and the accuracy of subsequent model processing is improved. The test data is some functions, controls and the like of the universality generally encountered when the test case is designed in the project, the case database comprises a plurality of test cases corresponding to the test data, the test cases are various test cases encountered in the test, and the initial image represents the image data generated in the test process, such as the images of some bank test systems.
102. Preprocessing the initial image to obtain a standard image corresponding to the initial image;
specifically, the server performs preprocessing on the initial image, where the preprocessing includes: graying, binarization, noise reduction and edge detection, wherein the graying is to convert a color image into a gray map, and the binarization processing is to set pixels on the image to be 0 or 255.
103. Inputting the standard image into a preset test data analysis model for test analysis to obtain a test element corresponding to the standard image, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer;
it should be noted that the input layer is used for defining different types of data input in the image feature data of the element; the hidden layer is used for carrying out nonlinear processing on the image characteristic data of the elements input by the input layer by utilizing an excitation function; the output layer is used for outputting and expressing the fitting result of the hidden layer and outputting the identification type corresponding to the image feature of the element.
104. And calling a preset case database to perform test case matching on the test elements to obtain target test cases corresponding to the test elements.
It should be noted that the preset case database includes a plurality of test cases, the test cases are various test cases encountered in the test, and the test cases are respectively matched with a plurality of test elements, and the element types include general functions of an operating system, a control and a page.
Further, the server stores the target test case in a blockchain database, which is not limited herein.
In the embodiment of the invention, test data to be processed is inquired, and an initial image corresponding to the test data is obtained; preprocessing the initial image to obtain a standard image; inputting the standard image into a preset test data analysis model for test analysis to obtain test elements; the preset case database is called to carry out test case matching on the test elements to obtain the target test cases corresponding to the test elements.
Referring to fig. 2, a second embodiment of the test data processing method according to the embodiment of the present invention includes:
201. inquiring test data to be processed from a preset case database, and acquiring an initial image corresponding to the test data;
optionally, the server obtains a plurality of sample test data images and training models; the server extracts elements of the sample test data images to obtain a plurality of training elements in each sample test data image; the server takes a plurality of training elements in each sample test data image as training sample data; and the server inputs training sample data into the training model for model training, and takes the training model after training as a test data analysis model.
It should be noted that the preset element extraction function is a very good array function slice provided by javascript, and a slice function may specify that extraction starts from a certain index of the array, ends when extracting another index, and starts from index 1, because there is no second parameter, the last element of the array is extracted. The plurality of test data comprise various test cases encountered in the test so as to extract a plurality of sample data from the pictures of the test data, thereby improving the accuracy of subsequent model training, and the plurality of training elements comprise: the image corresponding to the page general function, the image corresponding to the control and the image corresponding to the page compatibility test; the page general functions include: file uploading, searching, login and registration and prompting functions; the control comprises: an input box, an option control, a dialog box and a drop-down box; the page compatibility test comprises the following steps: operating system, browser, resolution. The server determines a plurality of test elements in each test data image as training sample data, the training sample data size of each test element is large to provide accuracy of a subsequent training model, a large number of graphic samples comprising the general functions, controls, an operating system browser and the like are trained, for example, functional graphic samples uploaded by files have various display forms, and a large number of training sample data uploaded by the files are obtained, so that the subsequently trained model can accurately identify the file uploading function. The input of the training model is a plurality of test elements, and the input layer of the training model is as follows: different types of data input in the image feature data for defining the test element; hiding the layer: the device comprises a display unit, a processing unit and a control unit, wherein the display unit is used for carrying out nonlinear processing on image characteristic data of elements input by an input layer by utilizing an excitation function; an output layer: and the method is used for outputting and representing the fitting result of the hidden layer and outputting the identification type corresponding to the image characteristic of the element. In addition, the training model is trained by adopting a random gradient descent method, parameters of the training model are adjusted and optimized, and the training model with the optimized parameters is used as a test data analysis model by the server.
202. Preprocessing the initial image to obtain a standard image corresponding to the initial image;
optionally, the server performs graying processing on the initial image to obtain a grayed image; the server carries out binarization processing on the grayscale image to obtain a binarization image; the server carries out noise reduction processing on the binary image to obtain a noise reduction image; and the server carries out edge detection on the noise-reduced image and removes the background image to obtain a standard image.
Specifically, the server applies a scaling method to convert the color image into the gray scale image, that is, the three components of the current pixel are respectively set as R, G, and B, and then obtains the component values of the converted pixel by using the following formula: 0.30 × R +0.59 × G +0.11 × B, and the image is subjected to binarization operation. The binarization processing of the image is to set the pixels on the image to 0 or 255, i.e. to make the whole image exhibit obvious black and white effect. Because the initial image contains both the display page and the background outside the interface, the invention needs to extract the whole page interface first and then segment each control element to be identified from the interface.
203. Inputting the standard image into a preset test data analysis model for test analysis to obtain a test element corresponding to the standard image, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer;
optionally, the server inputs the standard image into a preset test data analysis model, where the preset test data analysis model includes an input layer, a hidden layer, and an output layer; the server performs characteristic extraction on the standard image through an input layer to obtain image characteristic data; the server carries out nonlinear processing on the image characteristic data through the hidden layer to obtain nonlinear data; and the server extracts elements of the nonlinear data through the output layer to obtain test elements corresponding to the standard image.
Specifically, the server inputs the standard image into a preset test data analysis model, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer; the server performs feature extraction on the standard image through an input layer to obtain image feature data, wherein the input layer comprises two bidirectional circulation layers of 128 neurons and 64 neurons and a unidirectional circulation layer of 32 neurons; the hidden layer comprises 256 neurons, and the node of the neuron of the output layer is 1; the server carries out nonlinear processing on the image characteristic data through the hidden layer to obtain nonlinear data; the server extracts elements from the nonlinear data through the output layer to obtain a plurality of test elements corresponding to the standard image, the memory unit determines whether to write or delete the memory of the information in the neuron, and combines the image characteristic data of the previously recorded elements, the image characteristic data of the currently memorized elements and the image characteristic of the currently input elements together to record long-term information.
204. Acquiring an element type corresponding to a test element, wherein the element type comprises an operating system, a control and a page general function;
specifically, the server obtains element types corresponding to a plurality of test elements, wherein the element types comprise general functions of an operating system, a control and a page, and the server matches test cases from a preset case database according to the element types corresponding to the plurality of test elements to obtain target test cases corresponding to the plurality of test elements.
205. And matching the test cases corresponding to the test elements from a preset case database according to the element types to obtain the target test case.
Optionally, if the element type is the operating system, the server extracts a compatible test case of the operating system from the case database, and takes the compatible test case as a target test case; if the element type is the control, the server extracts a control test case from the case database, and takes the control test case as a target test case; if the element type is the universal function of the page, the server extracts the page test case from the case database and takes the page test case as a target test case.
Specifically, if the element type is the operating system, the server extracts a compatible test case of the operating system from the case database, and takes the compatible test case as a target test case; if the element type is the control, the server extracts a control test case from the case database, and takes the control test case as a target test case; if the element type is the universal function of the page, the server extracts the test case of the universal function of the page from the case database and generates an expected result of the test case so that the user can test the universal function of the page. And the server displays the target test case to a preset terminal for the user to select. The server stores the test cases into the case database, updates the element types corresponding to the target test cases into the element types of the case database, can update and perfect the content of the case database by storing the test elements currently input by the user into the case database, and can be directly called when the test cases of the same type are needed in the subsequent test process.
Optionally, the server obtains iteration information corresponding to the target test case; the server carries out case evaluation on the target test case based on the iteration information to obtain an evaluation result; and the server adjusts the target test case based on the evaluation result.
It should be noted that the server obtains iteration information corresponding to the target test case, and performs case evaluation on the target test case through a preset client, where the case evaluation is performed by a tester to ensure the logic correctness and stability of the automatically generated target test case, so as to avoid the unreliability of the obtained test result. The evaluation result is that the target test case is scored by the tester and the target test case has errors or unreasonable parts, the server obtains a corresponding adjusting computer program input by the tester based on the evaluation result, and the adjusting computer program comprises modification, addition, deletion and the like, so that the target test case is adjusted again.
Further, the server stores the target test case in a blockchain database, which is not limited herein.
In the embodiment of the invention, test data to be processed is inquired, and an initial image corresponding to the test data is obtained; preprocessing the initial image to obtain a standard image; inputting the standard image into a preset test data analysis model for test analysis to obtain test elements; the preset case database is called to carry out test case matching on the test elements to obtain the target test cases corresponding to the test elements.
With reference to fig. 3, the test data processing method in the embodiment of the present invention is described above, and a test data processing apparatus in the embodiment of the present invention is described below, where a first embodiment of the test data processing apparatus in the embodiment of the present invention includes:
an obtaining module 301, configured to query test data to be processed from a preset case database, and obtain an initial image corresponding to the test data;
a preprocessing module 302, configured to preprocess the initial image to obtain a standard image corresponding to the initial image;
the processing module 303 is configured to input the standard image into a preset test data analysis model for test analysis, so as to obtain a test element corresponding to the standard image, where the preset test data analysis model includes an input layer, a hidden layer, and an output layer;
the generating module 304 is configured to invoke a preset case database to perform test case matching on the test element, so as to obtain a target test case corresponding to the test element.
In the embodiment of the invention, test data to be processed is inquired, and an initial image corresponding to the test data is obtained; preprocessing the initial image to obtain a standard image; inputting the standard image into a preset test data analysis model for test analysis to obtain test elements; the preset case database is called to carry out test case matching on the test elements to obtain the target test cases corresponding to the test elements.
Referring to fig. 4, a second embodiment of a test data processing apparatus according to the present invention includes:
an obtaining module 301, configured to query test data to be processed from a preset case database, and obtain an initial image corresponding to the test data;
a preprocessing module 302, configured to preprocess the initial image to obtain a standard image corresponding to the initial image;
the processing module 303 is configured to input the standard image into a preset test data analysis model for test analysis, so as to obtain a test element corresponding to the standard image, where the preset test data analysis model includes an input layer, a hidden layer, and an output layer;
the generating module 304 is configured to invoke a preset case database to perform test case matching on the test element, so as to obtain a target test case corresponding to the test element.
Optionally, the test data processing apparatus further includes:
a training module 305 for obtaining a plurality of sample test data images and a training model; performing element extraction on the plurality of sample test data images to obtain a plurality of training elements in each sample test data image; taking a plurality of training elements in each sample test data image as training sample data; inputting the training sample data into the training model for model training, and taking the training model after training as a test data analysis model.
Optionally, the preprocessing module 302 is specifically configured to:
carrying out graying processing on the initial image to obtain a grayed image; carrying out binarization processing on the grayed image to obtain a binarized image; carrying out noise reduction processing on the binary image to obtain a noise reduction image; and carrying out edge detection on the noise-reduced image and removing a background image to obtain a standard image.
Optionally, the processing module 303 is specifically configured to:
inputting the standard image into a preset test data analysis model, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer; performing feature extraction on the standard image through the input layer to obtain image feature data; carrying out nonlinear processing on the image characteristic data through the hidden layer to obtain nonlinear data; and extracting elements of the nonlinear data through the output layer to obtain test elements corresponding to the standard image.
Optionally, the generating module 304 further specifically includes:
the identifying unit 3041 is configured to obtain an element type corresponding to the test element, where the element type includes an operating system, a control, and a page general function;
the matching unit 3042 is configured to match the test case corresponding to the test element from a preset case database according to the element type, so as to obtain a target test case.
Optionally, the matching unit 3042 is specifically configured to:
if the element type is the operating system, extracting a compatible test case of the operating system from a case database, and taking the compatible test case as a target test case; if the element type is the control, extracting a control test case from a case database, and taking the control test case as a target test case; and if the element type is the universal function of the page, extracting a page test case from a case database, and taking the page test case as a target test case.
Optionally, the test data processing apparatus further includes:
an adjusting module 306, configured to obtain iteration information corresponding to the target test case; case evaluation is carried out on the target test case based on the iteration information to obtain an evaluation result; and adjusting the target test case based on the evaluation result.
In the embodiment of the invention, test data to be processed is inquired, and an initial image corresponding to the test data is obtained; preprocessing the initial image to obtain a standard image; inputting the standard image into a preset test data analysis model for test analysis to obtain test elements; the preset case database is called to carry out test case matching on the test elements to obtain the target test cases corresponding to the test elements.
Fig. 3 and 4 describe the test data processing apparatus in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the test data processing apparatus in the embodiment of the present invention is described in detail from the perspective of hardware processing.
Fig. 5 is a schematic structural diagram of a test data processing apparatus 500 according to an embodiment of the present invention, where the test data processing apparatus 500 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of computer program operations in the test data processing apparatus 500. Still further, the processor 510 may be arranged to communicate with the storage medium 530, to execute a series of computer program operations in the storage medium 530 on the test data processing device 500.
The test data processing apparatus 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and the like. It will be appreciated by those skilled in the art that the test data processing apparatus arrangement shown in figure 5 does not constitute a limitation of the test data processing apparatus and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The present invention also provides a test data processing apparatus comprising a memory and a processor, the memory having stored therein a computer readable computer program, which, when executed by the processor, causes the processor to perform the steps of the test data processing method in the above embodiments.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and which may also be a volatile computer-readable storage medium, having stored therein a computer program, which, when run on a computer, causes the computer to perform the steps of the test data processing method.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several computer programs to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A test data processing method, characterized in that the test data processing method comprises:
inquiring test data to be processed from a preset case database, and acquiring an initial image corresponding to the test data;
preprocessing the initial image to obtain a standard image corresponding to the initial image;
inputting the standard image into a preset test data analysis model for test analysis to obtain a test element corresponding to the standard image, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer;
and calling a preset case database to perform test case matching on the test elements to obtain target test cases corresponding to the test elements.
2. The method of claim 1, wherein before the querying the preset case database for the test data to be processed and acquiring the initial image corresponding to the test data, the method further comprises:
obtaining a plurality of sample test data images and a training model;
element extraction is carried out on the sample test data images through a preset element extraction function, and a plurality of training elements in each sample test data image are obtained;
taking a plurality of training elements in each sample test data image as training sample data;
inputting the training sample data into the training model for model training, and taking the training model after training as a test data analysis model.
3. The method according to claim 1, wherein the preprocessing the initial image to obtain a standard image corresponding to the initial image comprises:
carrying out graying processing on the initial image to obtain a grayed image;
carrying out binarization processing on the grayed image to obtain a binarized image;
carrying out noise reduction processing on the binary image to obtain a noise reduction image;
and carrying out edge detection on the noise-reduced image and removing a background image to obtain a standard image.
4. The method according to claim 1, wherein the inputting of the standard image into a preset test data analysis model for test analysis to obtain test elements corresponding to the standard image, the preset test data analysis model including an input layer, a hidden layer, and an output layer, includes:
inputting the standard image into a preset test data analysis model, wherein the preset test data analysis model comprises an input layer, a hidden layer and an output layer;
performing feature extraction on the standard image through the input layer to obtain image feature data;
carrying out nonlinear processing on the image characteristic data through the hidden layer to obtain nonlinear data;
and extracting elements of the nonlinear data through the output layer to obtain test elements corresponding to the standard image.
5. The method as claimed in claim 1, wherein the step of calling a preset case database to perform test case matching on the test elements to obtain target test cases corresponding to the test elements comprises:
acquiring an element type corresponding to a test element, wherein the element type comprises an operating system, a control and a page general function;
and matching the test cases corresponding to the test elements from a preset case database according to the element types to obtain the target test case.
6. The method as claimed in claim 5, wherein the obtaining a target test case by matching the test case corresponding to the test element from a preset case database according to the element type includes:
if the element type is the operating system, extracting a compatible test case of the operating system from a case database, and taking the compatible test case as a target test case;
if the element type is the control, extracting a control test case from a case database, and taking the control test case as a target test case;
and if the element type is the universal function of the page, extracting a page test case from a case database, and taking the page test case as a target test case.
7. The test data processing method according to any one of claims 1 to 6, wherein after the invoking of the preset case database and the test case matching of the test elements are respectively performed to obtain the target test case corresponding to each test element, the test data processing method further comprises:
obtaining iteration information corresponding to the target test case;
case evaluation is carried out on the target test case based on the iteration information to obtain an evaluation result;
and adjusting the target test case based on the evaluation result.
8. A test data processing apparatus, characterized in that the test data processing apparatus comprises:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for inquiring test data to be processed from a preset case database and acquiring an initial image corresponding to the test data;
the preprocessing module is used for preprocessing the initial image to obtain a standard image corresponding to the initial image;
the processing module is used for inputting the standard image into a preset test data analysis model for test analysis to obtain a test element corresponding to the standard image, and the preset test data analysis model comprises an input layer, a hidden layer and an output layer;
and the generating module is used for calling a preset case database to perform test case matching on the test elements to obtain target test cases corresponding to the test elements.
9. A test data processing apparatus, characterized in that the test data processing apparatus comprises: a memory and at least one processor, the memory having stored therein a computer program;
the at least one processor calls the computer program in the memory to cause the test data processing apparatus to perform the test data processing method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a test data processing method according to any one of claims 1 to 7.
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