WO2023273177A1 - Procédé et appareil de traitement de cas de test, plate-forme et support de stockage - Google Patents

Procédé et appareil de traitement de cas de test, plate-forme et support de stockage Download PDF

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WO2023273177A1
WO2023273177A1 PCT/CN2021/136833 CN2021136833W WO2023273177A1 WO 2023273177 A1 WO2023273177 A1 WO 2023273177A1 CN 2021136833 W CN2021136833 W CN 2021136833W WO 2023273177 A1 WO2023273177 A1 WO 2023273177A1
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matrix
test
historical
feature
cases
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PCT/CN2021/136833
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English (en)
Chinese (zh)
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史媛媛
卢道和
周杰
黄涛
陈文龙
袁文静
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深圳前海微众银行股份有限公司
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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
    • G06F11/368—Test management for test version control, e.g. updating test cases to a new software version
    • 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
    • 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

Definitions

  • the embodiment of the present application relates to the technical field of financial technology (Fintech) data processing, and relates to but not limited to a test case processing method, a test case processing device, a test platform and a storage medium.
  • Financial technology Fetech
  • test cases in the financial system include front-end test cases and back-end test cases.
  • classification of test cases in the financial system is achieved by manual marking by testers, and the system under test in the financial system Whether the version is automatically tested depends on the experience of the tester, subjectively judging the frequency of requirement changes in the version of the system to be tested and the stability of the test case. It can be seen that the above method must rely on manual operation, at least there are problems of low efficiency and poor accuracy.
  • the embodiments of the present application provide a test case processing method, a test case processing device, a test platform, and a storage medium, so as to solve the problems that related technologies must rely on manual operations, and at least have low efficiency and poor accuracy.
  • the embodiment of this application provides a method, including:
  • n is a positive integer greater than or equal to 1 and less than or equal to N
  • N is the total number of front-end test cases in the version set of the system to be tested
  • An embodiment of the present application provides a device, including:
  • An acquisition module configured to acquire all front-end test cases in the system version set to be tested, and all front-end historical cases in each historical system version set in at least two historical system version sets;
  • a processing module configured to calculate the eigenvalues corresponding to the features in each of the front-end test cases in all the front-end test cases, and obtain the test feature matrix of the version set of the system to be tested;
  • the processing module is also used to calculate the feature value corresponding to the feature in each front-end historical case in all the front-end historical cases in the set of each historical system version, and obtain the historical feature of each historical system version set matrix;
  • the processing module is also used to perform dimensionality reduction processing on the test feature matrix based on a non-negative matrix factorization algorithm to obtain a dimensionality-reduced test feature matrix;
  • the processing module is further configured to perform dimensionality reduction processing on the historical feature matrix based on the non-negative matrix factorization algorithm, to obtain a dimensionally reduced historical feature matrix;
  • the processing module is also used to calculate the dimensionality-reduced feature of the nth front-end test case in the dimension-reduced test feature matrix and the dimensionality reduction of each front-end historical case in the dimension-reduced historical feature matrix
  • the degree of similarity of the features after is obtained similarity matrix;
  • said n is a positive integer greater than or equal to 1 and less than or equal to N, and N is the total number of front-end test cases in the described system version set to be tested;
  • the processing module is further configured to perform automated testing on all front-end test cases in the version set of the system under test when it is determined that all front-end test cases meet the automated test conditions based on the similarity matrix.
  • test platform including:
  • An embodiment of the present application provides a storage medium, which stores executable instructions, and is used to cause a processor to implement the above method when executed.
  • the test platform of this application After obtaining all front-end test cases and all front-end historical cases in the version set of the system to be tested, the test platform of this application first calculates the characteristic value corresponding to the feature in each front-end test case, and obtains the test features of the version set of the system to be tested Matrix, calculate the eigenvalues corresponding to the features in each front-end historical case, and obtain the historical feature matrix of each historical system version set; secondly, use the non-negative matrix algorithm to perform dimension reduction processing on the test feature matrix and the historical feature matrix respectively, And perform similarity processing on the test feature matrix after dimensionality reduction and the historical feature matrix after dimensionality reduction to obtain a similarity matrix, and then according to the similarity matrix, when all front-end test cases meet the automated test conditions, all front-end test cases Run automated tests.
  • this application solves the problem that related technologies must rely on manual operation and uncertainty brought by manual subjectivity, and at least has the problems of low efficiency and poor accuracy; realizes the establishment of a unified standard for the automatic execution of front-end test cases, and improves The accuracy of judgment is improved, and at the same time, it does not need to rely on manual operation, which improves the processing efficiency.
  • Fig. 1 is a schematic diagram of an optional architecture of the test platform provided by the embodiment of the present application.
  • Fig. 2 is an optional schematic flow chart of the test case processing method provided by the embodiment of the present application.
  • Fig. 3 is an optional schematic flow chart of the test case processing method provided by the embodiment of the present application.
  • Fig. 4 is an optional flowchart for training a classifier in a front-end case provided by an embodiment of the present application
  • Fig. 5 is an optional schematic flow chart of the test case processing method provided by the embodiment of the present application.
  • FIG. 6 is an optional schematic flow chart of a test case processing method provided in an embodiment of the present application.
  • Fig. 7 is an optional schematic flow chart of the test case processing method provided by the embodiment of the present application.
  • Fig. 8 is a schematic flowchart of an optional test case processing method provided by the embodiment of the present application.
  • FIG. 1 is a schematic structural diagram of a test platform 100 provided by an embodiment of the present application.
  • the test platform 100 shown in FIG. Various components in the test platform 100 are coupled together through the bus system 140 . It can be understood that the bus system 140 is used to realize connection and communication between these components.
  • the bus system 140 also includes a power bus, a control bus and a status signal bus. However, for clarity of illustration, the various buses are labeled as bus system 140 in FIG.
  • Processor 110 can be a kind of integrated circuit chip, has signal processing capability, such as general-purpose processor, digital signal processor (DSP, Digital Signal Processor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware Components, etc., wherein the general-purpose processor can be a microprocessor or any conventional processor, etc.
  • DSP digital signal processor
  • DSP Digital Signal Processor
  • User interface 130 includes one or more output devices 131 that enable presentation of media content, including one or more speakers and/or one or more visual displays.
  • the user interface 130 also includes one or more input devices 132, including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.
  • Memory 150 may be removable, non-removable or a combination thereof. Exemplary hardware devices include solid-state memory, hard disk drives, optical disk drives, and the like. Memory 150 optionally includes one or more storage devices located physically remote from processor 110 . Memory 150 includes volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. The non-volatile memory can be read-only memory (Read Only Memory, ROM), and the volatile memory can be random access memory (Random Access Memory, RAM). The memory 150 described in the embodiment of the present application is intended to include any suitable type of memory. In some embodiments, the memory 150 is capable of storing data to support various operations, examples of which include programs, modules, and data structures, or subsets or supersets thereof, as exemplified below.
  • Operating system 151 including system programs for processing various basic system services and performing hardware-related tasks, such as framework layer, core library layer, driver layer, etc., for implementing various basic services and processing hardware-based tasks;
  • the network communication module 152 is used to reach other computing devices via one or more (wired or wireless) network interfaces 120.
  • Exemplary network interfaces 120 include: Bluetooth, Wireless Compatibility Authentication (WiFi), and Universal Serial Bus ( Universal Serial Bus, USB), etc.;
  • the input processing module 153 is configured to detect one or more user inputs or interactions from one or more of the input devices 132 and translate the detected inputs or interactions.
  • the device provided by the embodiment of the present application can be realized by software.
  • FIG. 1 shows a test case processing device 154 stored in the memory 150.
  • the test case processing device 154 can be the The test case processing device, which can be software in the form of programs and plug-ins, includes the following software modules: acquisition module 1541, processing module 1542, these modules are logical, so any combination or further split. The function of each module will be explained below.
  • the device provided in the embodiment of the present application may be implemented in hardware.
  • the device provided in the embodiment of the present application may be a processor in the form of a hardware decoding processor, which is programmed to execute the In the test case processing method provided by the embodiment, for example, the processor in the form of a hardware decoding processor can adopt one or more Application Specific Integrated Circuits (Application Specific Integrated Circuit, ASIC), DSP, Programmable Logic Device (Programmable Logic Device, PLD), Complex Programmable Logic Device (Complex Programmable Logic Device, CPLD), Field Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other electronic components.
  • ASIC Application Specific Integrated Circuit
  • DSP Digital Signal Processing
  • Programmable Logic Device Programmable Logic Device
  • PLD Complex Programmable Logic Device
  • CPLD Complex Programmable Logic Device
  • Field Programmable Gate Array Field-Programmable Gate Array
  • FPGA Field-Programmable Gate Array
  • the application layer software of the new version system is the application layer software in the system under test, which is obtained by modifying a certain module in the historical version system and/or adding a certain module, which makes the web front end of the system under test often
  • the changes are relatively large, but the logic of some modules in the reused historical version system has not changed, that is, the back-end implementation has not changed.
  • the classification of the front-end and back-end in the system under test is mainly realized through manual marking by testers, and the functional verification of the web front-end of the system under test must be tested through automated scripts. Whether the functional verification of the web front-end of the test system is automatically tested mainly depends on the experience of the testers, subjectively judging the frequency of requirements changes in the version of the system under test and the stability of the test cases. It can be seen that at least there are problems of low efficiency and poor accuracy in the above method.
  • FIG. 2 is an optional flow chart of the test case processing method provided in the embodiment of the present application, which will be described in conjunction with the steps shown in FIG. 2,
  • Step 201 Acquire all front-end test cases in the system version set to be tested, and all front-end historical cases in each of the at least two historical system version sets.
  • the system to be tested is a system that tests the cases contained in the system before release
  • the historical system is a system that has been successfully released.
  • the system version to be tested can be understood as the system corresponding to the current version number
  • the historical system version can be understood as the system corresponding to the version numbers of different historical periods, and there can be multiple historical system versions.
  • developers iteratively and incrementally develop the legacy system to obtain the system under test.
  • the developer obtains the system under test by modifying a certain functional module in the historical system and/or adding a certain functional module.
  • the front-end test cases are all test cases included in the web front-end of the system to be tested, and all the test cases included in the web front-end of the system to be tested form the version set of the system to be tested; All historical cases included in the web front end of the system, and all historical cases included in the web front end of each historical system constitute each historical system version set.
  • a case can be understood as a functional module of the system front-end page.
  • a case can be a registration module of the system front-end page, and a case can also be a login module of the system front-end page.
  • the total number of front-end test cases in the system version set to be tested may be the same as or different from the total number of front-end historical cases in each of the at least two historical system version sets.
  • the total number of front-end test cases in the system version set to be tested is the same as the total number of front-end historical cases in each of the at least two historical system version sets.
  • the test platform obtains all the front-end test cases in the system version set to be tested. Further, the test platform also obtains at least two historical system version sets corresponding to the system to be tested. In each historical system version set All front-end history cases for .
  • Step 202 Calculate the eigenvalues corresponding to the features in each front-end test case in all the front-end test cases, and obtain the test feature matrix of the system version set to be tested.
  • the feature value corresponding to a feature in each front-end test case in all front-end test cases is used to represent the importance of the feature in the front-end test case.
  • the test platform can calculate the eigenvalues corresponding to the features in each front-end test case in all front-end test cases based on the word frequency-reverse file frequency algorithm, and obtain the test feature matrix of the system version set to be tested.
  • TF-IDF frequency-inverse document frequency
  • the test platform is based on the TF-IDF algorithm to calculate the feature value corresponding to the mth feature in the nth case of all front-end test cases N, and then all the features in all front-end test cases correspond to The eigenvalues of , generate the test feature matrix of the system version set under test.
  • the matrix size of the test feature matrix X of the system version set to be tested is N ⁇ M
  • n is a positive integer greater than or equal to 1 and less than or equal to N
  • N is the total number of front-end test cases in the system version set to be tested
  • m is A positive integer greater than or equal to 1 and less than or equal to N, where M is the number of features of each front-end test case.
  • Step 203 Calculate the eigenvalues corresponding to the features in each front-end historical case in all front-end historical cases in each historical system version set, and obtain the historical feature matrix of each historical system version set.
  • the feature value corresponding to the feature in each front-end historical case in all front-end historical cases in each historical system version set is used to represent the importance of the feature in the front-end historical case.
  • the total number of features in each front-end test case in all front-end test cases may be the same as or different from the total number of features in each front-end historical case in each historical system version set.
  • the total number of features in each front-end test case in all front-end test cases is the same as the total number of features in each front-end historical case in each historical system version set as an example. .
  • the test platform is based on the TF-IDF algorithm, and calculates the m-th feature of the n-th front-end historical case in all front-end historical cases N in the k-th historical system version set in at least two historical system version sets corresponding eigenvalues Then, from the eigenvalues corresponding to all the features in all front-end historical cases in the kth historical system version set, a historical feature matrix of the kth historical system version set is generated.
  • the historical feature matrix Among them, the matrix size of the historical feature matrix P k corresponding to the kth historical system version set is M ⁇ N, n is a positive integer greater than or equal to 1 and less than or equal to N, and N is the front-end historical case in each historical system version set m is a positive integer greater than or equal to 1 and less than or equal to N, and M is the number of features of each front-end historical case; k is a positive integer greater than or equal to 1 and less than or equal to K, and K is the total number of all historical system version sets.
  • step 202 and step 203 may be performed simultaneously, or step 202 and step 203 may be performed sequentially, which is not specifically limited in this application.
  • Step 204 based on the non-negative matrix factorization algorithm, perform dimensionality reduction processing on the test feature matrix to obtain a dimensionality-reduced test feature matrix.
  • the test platform calculates the eigenvalues corresponding to the features in each front-end test case in all front-end test cases based on the word frequency-reverse file frequency algorithm, and obtains the test feature matrix of the system version set to be tested.
  • the test platform performs dimensionality reduction processing on the test feature matrix through the non-negative matrix decomposition algorithm, and obtains the test feature matrix after dimensionality reduction.
  • the dimension-reduced test feature matrix is used to replace the original test feature matrix.
  • the dimension-reduced test feature matrix The feature matrix is processed, which not only reduces the storage space, but also reduces the calculation amount of computer resources.
  • the non-negative matrix factorization (Non-negative matrix factorization, NMF) algorithm is a matrix factorization method under the constraint that all elements in the matrix are non-negative numbers, that is, for a given non-negative matrix A, the NMF algorithm can find a A non-negative matrix U and a non-negative matrix V with smaller dimensions, that is, A ⁇ U ⁇ V, to obtain a dimensionally reduced non-negative matrix U.
  • NMF non-negative matrix factorization
  • Step 205 based on the non-negative matrix factorization algorithm, perform dimensionality reduction processing on the historical feature matrix to obtain a dimensionally reduced historical feature matrix.
  • the test platform calculates the feature value corresponding to the feature in each front-end historical case in all front-end historical cases in each historical system version set based on the word frequency-reverse document frequency algorithm, and obtains each historical system version set
  • the test platform uses the non-negative matrix factorization algorithm to reduce the dimensionality of the historical feature matrix to obtain the historical feature matrix after dimensionality reduction.
  • the historical feature matrix after dimensionality reduction is used to replace the original historical feature matrix.
  • the historical feature matrix after dimensionality reduction The feature matrix is processed, which not only reduces the storage space, but also reduces the calculation amount of computer resources.
  • step 204 and step 205 may be performed simultaneously, and step 204 and step 205 may also be performed sequentially, which is not specifically limited in this application.
  • Step 206 calculate the similarity between the dimensionality-reduced features of the nth front-end test case in the dimension-reduced test feature matrix and the dimension-reduced features of each front-end historical case in the dimension-reduced historical feature matrix, and obtain similarity degree matrix.
  • n is a positive integer greater than or equal to 1 and less than or equal to N, and N is the total number of front-end test cases in the system version set to be tested.
  • the test platform performs dimensionality reduction processing on the test feature matrix based on the non-negative matrix decomposition algorithm, obtains the dimensionality-reduced test feature matrix, and performs dimensionality reduction processing on the historical feature matrix based on the non-negative matrix decomposition algorithm , when the dimensionality-reduced historical feature matrix is obtained, calculate the dimensionality-reduced feature of the nth front-end test case in the dimension-reduced test feature and the dimensionality reduction of each front-end historical case in the dimensionality-reduced historical feature matrix
  • the similarity of the final features is obtained to obtain a similarity matrix, so that the test platform can judge whether all the front-end test cases in the system version set under test meet the automated test conditions based on the similarity matrix.
  • Step 207 based on the similarity matrix, when it is determined that all front-end test cases meet the automated test conditions, perform automated tests on all front-end test cases in the system version set to be tested.
  • the automated test condition is the condition that the front-end test case in the system version set to be tested can use the automated script to perform the automated test.
  • the test platform calculates the dimensionality-reduced features of the nth front-end test case in the dimension-reduced test feature matrix and the dimension-reduced features of each front-end historical case in the dimension-reduced historical feature matrix In the case of obtaining the similarity matrix, based on the similarity matrix, when it is determined that all front-end test cases meet the automated test conditions, all front-end test cases in the system version set to be tested are automatically tested through automated scripts.
  • test case processing method After the test platform obtains all front-end test cases and all front-end historical cases in the system version set to be tested, first, calculates the feature value corresponding to the feature in each front-end test case, Obtain the test feature matrix of the system version set to be tested, calculate the eigenvalues corresponding to the features in each front-end historical case, and obtain the historical feature matrix of each historical system version set; secondly, through the non-negative matrix algorithm, the test feature matrix and Dimensionality reduction processing is performed on the historical feature matrix, and the similarity processing is performed on the reduced test feature matrix and the reduced historical feature matrix to obtain a similarity matrix, and then according to the similarity matrix, it is determined that all front-end test cases meet the requirements of automation When testing conditions, automate tests for all front-end test cases.
  • this application solves the problem that related technologies must rely on manual operation and uncertainty brought by manual subjectivity, and at least has the problems of low efficiency and poor accuracy; realizes the establishment of a unified standard for the automatic execution of front-end test cases, and improves The accuracy of judgment is improved, and at the same time, it does not need to rely on manual operation, which improves the processing efficiency.
  • FIG. 3 is an optional schematic flowchart of the test case processing method provided in the embodiment of the present application, which will be described in conjunction with the steps shown in FIG. 3,
  • Step 301 Obtain all test cases in the version set of the system to be tested.
  • Step 302 Input all the test cases in the version set of the system under test into the trained classifier, and obtain the front-end test cases in the version set of the system under test output by the trained classifier.
  • all test cases in the system version set to be tested include front-end test cases and back-end test cases.
  • the trained classifier is used to classify all the test cases in the version set of the system under test to obtain the front-end test cases among all the test cases in the version set of the system under test.
  • a classifier is a method of data mining. The classifier can map the data records in the database to one of the given categories, so that it can be applied to data prediction.
  • a classifier is a general term for methods for classifying samples in data mining. Classification methods include algorithms such as decision trees, logistic regression, naive Bayesian, and neural networks. Classification methods also include support vector machines (support vector machines, SVM) algorithm.
  • the test platform obtains all marked front-end historical cases and back-end historical cases in each historical system version set in at least two historical system version sets as a training sample set Afterwards, the test platform uses the TF-IDF method to calculate the eigenvalues corresponding to each feature in the front-end historical cases in each historical system version set in at least two historical system version sets, and the corresponding eigenvalues for each feature in the back-end historical cases eigenvalues, so as to obtain the training sample feature matrix. The test platform trains the classifier through the training sample feature matrix to obtain a trained classifier.
  • SVM is used as an example to illustrate.
  • the test platform uses SVM for classification training.
  • SVM is a binary classification model, which mainly looks for the classifier with the largest interval in the feature space, combined with the kernel function , can classify nonlinear features, and the actual implementation can be converted into a problem of solving convex quadratic programming.
  • the optimal solution of the Lagrangian parameters can be obtained corresponding Take the classification decision function as:
  • b is the parameter to confirm the classification decision function corresponding to each sample
  • the exponential kernel function
  • ⁇ is the distance between features
  • l is the hyperparameter of the kernel function.
  • the SVM classifier corresponding to the exponential kernel function is a binary classifier of the exponential function, then the final classification decision function is Based on this, the sample set is trained to obtain the final classifier.
  • the test platform uses the TF-IDF method to calculate each test case in all the test cases in the system version set to be tested.
  • the eigenvalue corresponding to a feature is obtained to obtain the test feature matrix, and the test feature matrix is input into the trained classifier to obtain the front-end test cases in the version set of the system under test output by the trained classifier.
  • the test platform inputs all test cases in the version set of the system under test into the trained classifier, and obtains the front end in the version set of the system under test output by the trained classifier.
  • the test cases pass the obtained front-end test cases through the jieba model, traverse each front-end test case, and mark the traversed front-end test cases as "front-end".
  • front-end and back-end test cases are automatically classified, freeing manpower, saving labor costs, reducing the risk of misclassification caused by manual marking, and improving processing efficiency.
  • the test platform after the test platform marks the front-end test cases and the back-end test cases in the version set of the system to be tested, in order to improve the classification accuracy of the classifier, the test platform will use the version of the system to be tested
  • the marked front-end test cases and back-end test cases in the collection are re-added to the training sample set, and the classifier is continuously trained, so that the classifier can classify the front-end test cases more accurately and quickly.
  • Step 303 Obtain all front-end historical cases in each historical system version set in at least two historical system version sets.
  • Step 304 Calculate the eigenvalues corresponding to the features in each front-end test case in all the front-end test cases, and obtain the test feature matrix of the system version set to be tested.
  • Step 305 Calculate the eigenvalues corresponding to the features in each front-end historical case in all front-end historical cases in each historical system version set, and obtain the historical feature matrix of each historical system version set.
  • Step 306 based on the non-negative matrix factorization algorithm, perform dimensionality reduction processing on the test feature matrix to obtain a dimensionality-reduced test feature matrix.
  • step 306 is based on the non-negative matrix factorization algorithm to perform dimensionality reduction processing on the test feature matrix to obtain the dimensionality-reduced test feature matrix, which can be performed through steps 401 to 403 shown in FIG. 5; or steps 401 to 403.
  • Step 401 Decompose the test feature matrix through a non-negative matrix factorization algorithm based on the determined feature numbers after dimension reduction of the test feature matrix to obtain a test projection matrix and a test fundamental matrix.
  • the value range of the test platform based on the feature number S after dimensionality reduction is Select a positive integer S to determine the feature number S after dimension reduction, and decompose the test feature matrix X through the non-negative matrix decomposition algorithm, and randomly generate the test projection matrix W1 and the test basic matrix B1.
  • the size of the test projection matrix W1 is N ⁇ S
  • the size of the test fundamental matrix B1 is S ⁇ M.
  • Step 402. Obtain a first product matrix obtained by multiplying the test projection matrix and the test fundamental matrix.
  • Step 403 If the first difference matrix obtained by subtracting the first product matrix from the test feature matrix conforms to the difference threshold matrix, determine the test projection matrix corresponding to the first difference matrix as the reduced-dimensional test feature matrix.
  • the difference threshold matrix is used to determine the difference matrix between the test feature matrix and the first product matrix obtained by multiplying the test projection matrix and the test fundamental matrix.
  • the value of each element in the difference threshold matrix may be 10 -6 .
  • the test platform decomposes the test feature matrix X through the non-negative matrix decomposition algorithm based on the determined feature number S after dimensionality reduction of the test feature matrix, and obtains the test projection matrix W1 and the test basic matrix B1 , to obtain the first product matrix Q1 obtained by multiplying the test projection matrix W1 and the test fundamental matrix B1.
  • the test projection matrix W1 corresponding to the first difference matrix E1 is the test feature matrix after dimensionality reduction
  • the test feature matrix after dimensionality reduction The size of is N ⁇ S.
  • Step 404 If the first difference matrix obtained by subtracting the first product matrix from the test feature matrix does not conform to the difference threshold matrix, adjust each element in the test projection matrix through the projection matrix adjustment model to obtain the adjusted test projection matrix.
  • the projection matrix adjustment model is:
  • W' ns is the element in row n and column s in the adjusted test projection matrix
  • W is the test projection matrix
  • W ns is the element in row n and column s in the test projection matrix
  • X is the test feature matrix
  • B is the test fundamental matrix
  • B T is the transpose matrix of the test fundamental matrix
  • (XB T ) ns is the nth row s of the matrix obtained by multiplying the test feature matrix X and the transpose matrix B T of the test fundamental matrix
  • the elements of the column, (WBB T ) ns are the elements of the nth row and the sth column in the matrix obtained by multiplying the test projection matrix W, the test fundamental matrix B and the transpose matrix B T of the test fundamental matrix.
  • the test platform determines that the first difference matrix E1 obtained by subtracting the first product matrix Q1 from the test feature matrix X does not meet the difference threshold matrix E, and adjusts the model for each of the test projection matrix W1 through the projection matrix. One element is adjusted to obtain the adjusted test projection matrix W11.
  • Step 405 Adjust each element in the test fundamental matrix through the fundamental matrix adjustment model to obtain an adjusted test fundamental matrix.
  • the basic matrix adjustment model is:
  • B'sm is the element of the sth row and the mth column in the adjusted test fundamental matrix
  • B is the test fundamental matrix
  • B sm is the element of the sth row and the mth column in the test fundamental matrix
  • X is the test feature matrix
  • W is the test projection matrix
  • W T is the transpose matrix of the test projection matrix
  • (W T X) sm is the matrix obtained by multiplying the transpose matrix W T of the test projection matrix by the test feature matrix X
  • the elements in column m, (W T WB ) sm are the elements in row s and column m in the matrix obtained by multiplying the transposition matrix W T of the test projection matrix, the test projection matrix W and the test fundamental matrix B.
  • the test platform adjusts each element in the basic projection matrix B1 through the basic matrix adjustment model to obtain the adjusted test basic matrix B11.
  • Step 406 Obtain a second product matrix obtained by multiplying the adjusted test projection matrix and the adjusted test fundamental matrix.
  • Step 407 If the second difference matrix obtained by subtracting the second product matrix from the test feature matrix conforms to the difference threshold matrix, determine that the adjusted test projection matrix corresponding to the second difference matrix is the reduced-dimensional test feature matrix.
  • the test platform obtains the second product matrix Q2 obtained by multiplying the adjusted test projection matrix W11 and the adjusted test fundamental matrix B11;
  • the difference matrix E2 conforms to the difference threshold matrix E. Since the test platform needs to find the test projection matrix corresponding to the difference threshold matrix E, it is the test feature matrix after dimensionality reduction. At this time, because the second difference matrix E2 satisfies the condition, then Determine the adjusted test projection matrix W11 corresponding to the second difference matrix E2 as the test feature matrix after dimensionality reduction
  • Step 408 If the second difference matrix obtained by subtracting the second product matrix from the test feature matrix does not conform to the difference threshold matrix, adjust each element in the adjusted test projection matrix through the projection matrix adjustment model to obtain a new adjustment After the test projection matrix.
  • Step 409 Adjust each element in the adjusted test fundamental matrix through the fundamental matrix adjustment model to obtain a new adjusted test fundamental matrix.
  • Step 410 Obtain a third product matrix obtained by multiplying the newly adjusted test projection matrix and the newly adjusted test fundamental matrix.
  • Step 411 If the third difference matrix obtained by subtracting the third product matrix from the test feature matrix conforms to the difference threshold matrix, determine that the newly adjusted test projection matrix corresponding to the third difference matrix is the reduced-dimensional test feature matrix.
  • the test platform determines that the second difference matrix E2 obtained by subtracting the second product matrix Q2 from the test feature matrix X does not meet the difference threshold matrix E, and the adjusted test projection matrix is adjusted by the projection matrix adjustment model Each element in W11 is adjusted to obtain the newly adjusted test projection matrix W12; secondly, the test platform adjusts each element in the adjusted test basic matrix B11 through the basic matrix adjustment model to obtain the newly adjusted test The basic matrix B12; then, the test platform obtains the third product matrix Q3 obtained by multiplying the newly adjusted test projection matrix W12 and the newly adjusted test basic matrix B12; finally, the test platform determines the test feature matrix X minus the third product The third difference matrix E3 obtained by matrix Q3 conforms to the difference threshold matrix E, and the newly adjusted test projection matrix W12 corresponding to the third difference matrix E3 is determined to be the test feature matrix after dimensionality reduction It should be noted that, in the embodiment of the present application, for the dimensionality-reduced test feature matrix The determination of , can be that the dimensionality-reduced test feature
  • the present application does not specifically limit the number of adjustment cycles, and the dimensionality-reduced test feature matrix that satisfies the conditions shall prevail.
  • the test platform needs to find the test projection matrix corresponding to the difference threshold matrix E as the test feature matrix after dimensionality reduction, at this time, because the third difference matrix E3 satisfies the condition, then determine the third difference matrix E3 corresponding to The newly adjusted test projection matrix W12 is the test feature matrix after dimensionality reduction
  • the test platform replaces the original test feature matrix with the reduced-dimensional test feature matrix, which not only reduces the storage space, but also reduces the calculation amount and computational complexity of computer resources, and the feature representation power is improved. At the same time Whether automated testing is performed on all front-end test cases, and accurate data is provided as the basis for calculation.
  • Step 307 based on the non-negative matrix factorization algorithm, perform dimensionality reduction processing on the historical feature matrix to obtain a dimensionally reduced historical feature matrix.
  • step 307 is based on the non-negative matrix factorization algorithm to perform dimensionality reduction processing on the historical feature matrix to obtain the dimensionality-reduced historical feature matrix, which can be performed through steps 501 to 503 shown in FIG. 6; or steps 501 to 503.
  • Step 501 Based on the determined feature numbers of the historical feature matrix after dimension reduction, the historical feature matrix is decomposed through a non-negative matrix factorization algorithm to obtain a historical projection matrix and a historical fundamental matrix.
  • the value range of the test platform based on the feature number S after dimensionality reduction is Select a positive integer S to determine the feature number S after dimension reduction, and decompose the historical feature matrix P k through the non-negative matrix decomposition algorithm, and randomly generate the historical projection matrix W2 and historical basic matrix B2.
  • the size of the historical projection matrix W2 is N ⁇ S
  • the size of the historical basic matrix B2 is S ⁇ M.
  • Step 502. Obtain a fourth product matrix obtained by multiplying the historical projection matrix and the historical fundamental matrix.
  • Step 503 If the fourth difference matrix obtained by subtracting the fourth product matrix from the historical feature matrix conforms to the difference threshold matrix, determine that the historical projection matrix corresponding to the fourth difference matrix is the historical feature matrix after dimensionality reduction.
  • the difference threshold matrix is used to determine the difference matrix between the historical feature matrix and the first product matrix obtained by multiplying the historical projection matrix and the historical fundamental matrix.
  • the value of each element in the difference threshold matrix may be 10 -6 .
  • the test platform decomposes the historical feature matrix P k through the non-negative matrix decomposition algorithm based on the feature number S after the dimensionality reduction of the determined historical feature matrix, and obtains the situation of the historical projection matrix W2 and the historical basic matrix B2 Next, obtain the fourth product matrix Q4 obtained by multiplying the historical projection matrix W2 and the historical fundamental matrix B2.
  • the fourth difference matrix E4 obtained by subtracting the fourth product matrix Q4 from the historical feature matrix Pk conforms to the difference threshold matrix E, since the test platform needs to find the historical projection matrix that satisfies the difference threshold matrix E, it is the dimension-reduced history Feature matrix, at this time, because the fourth difference matrix E4 satisfies the condition, the historical projection matrix W2 corresponding to the fourth difference matrix E4 is the historical feature matrix after dimensionality reduction
  • the historical feature matrix after dimensionality reduction Among them, the historical feature matrix after dimensionality reduction The size is S ⁇ N.
  • Step 504 If the fourth difference matrix obtained by subtracting the fourth product matrix from the historical feature matrix does not conform to the difference threshold matrix, adjust each element in the historical projection matrix through the projection matrix adjustment model to obtain the adjusted historical projection matrix.
  • Step 505 Adjust each element in the historical basic matrix through the basic matrix adjustment model to obtain an adjusted historical basic matrix.
  • the test platform determines that the fourth difference matrix E4 obtained by subtracting the fourth product matrix Q4 from the historical feature matrix Pk does not meet the difference threshold matrix E, and adjusts the model for each historical projection matrix W2 through the projection matrix.
  • One element is adjusted to obtain the adjusted historical projection matrix W21.
  • each element in the basic projection matrix B2 is adjusted through the basic matrix adjustment model to obtain the adjusted basic projection matrix B21.
  • Step 506 Obtain a fifth product matrix obtained by multiplying the adjusted historical projection matrix and the adjusted historical fundamental matrix.
  • Step 507 If the fifth difference matrix obtained by subtracting the fifth product matrix from the historical feature matrix conforms to the difference threshold matrix, determine that the adjusted historical projection matrix corresponding to the fifth difference matrix is the reduced dimensionality historical feature matrix.
  • the test platform obtains the fifth product matrix Q5 obtained by multiplying the adjusted historical projection matrix W21 and the adjusted historical fundamental matrix B21;
  • the five-difference matrix E5 conforms to the difference threshold matrix E. Since the test platform needs to find that the historical projection matrix corresponding to the difference threshold matrix E is the historical feature matrix after dimensionality reduction, at this time, because the fifth difference matrix E5 satisfies the condition, Then determine that the adjusted historical projection matrix W21 corresponding to the fifth difference matrix E5 is the historical feature matrix after dimensionality reduction
  • Step 508 If the fifth difference matrix obtained by subtracting the fifth product matrix from the historical feature matrix does not conform to the difference threshold matrix, adjust each element in the adjusted historical projection matrix through the projection matrix adjustment model to obtain a new adjusted The post-historical projection matrix.
  • Step 509 Adjust each element in the adjusted historical basic matrix through the basic matrix adjustment model to obtain a new adjusted historical basic matrix.
  • Step 510 Obtain a sixth product matrix obtained by multiplying the newly adjusted historical projection matrix and the newly adjusted historical fundamental matrix.
  • Step 511 If the sixth difference matrix obtained by subtracting the sixth product matrix from the historical feature matrix conforms to the difference threshold matrix, determine that the newly adjusted historical projection matrix corresponding to the sixth difference matrix is the dimensionality-reduced historical feature matrix.
  • the test platform determines that the fifth difference matrix E5 obtained by subtracting the fifth product matrix Q5 from the historical feature matrix Pk does not conform to the difference threshold matrix E, and adjusts the model through the projection matrix to adjust the historical projection
  • Each element in the matrix W21 is adjusted to obtain the newly adjusted historical projection matrix W22;
  • the test platform adjusts each element in the adjusted historical basic matrix B21 through the basic matrix adjustment model to obtain the newly adjusted The historical basic matrix B22;
  • the test platform obtains the sixth product matrix Q6 obtained by multiplying the newly adjusted historical projection matrix W22 and the newly adjusted historical basic matrix B22;
  • the test platform determines the historical feature matrix P k minus the first
  • the sixth difference matrix E6 obtained by the six product matrix Q6 conforms to the difference threshold matrix E, and the newly adjusted historical projection matrix W22 corresponding to the sixth difference matrix E6 is determined to be the historical feature matrix after dimensionality reduction
  • Historical feature matrix that is to say, the present application does not specifically limit the number of adjustment cycles, and the dimensionality-reduced historical feature matrix that satisfies the conditions is obtained.
  • the test platform needs to find that the historical projection matrix corresponding to the difference threshold matrix E is the historical feature matrix after dimensionality reduction, at this time, because the sixth difference matrix E6 satisfies the condition, it is determined that the sixth difference matrix E6 corresponds to
  • the newly adjusted historical projection matrix W22 is the historical feature matrix after dimensionality reduction
  • the test platform replaces the original historical feature matrix with the historical feature matrix after dimensionality reduction, which not only reduces the storage space, but also reduces the calculation amount and computational complexity of computer resources, and the feature representation power is improved. At the same time Whether automated testing is performed on all front-end test cases, and accurate data is provided as the basis for calculation.
  • Step 308 calculating the similarity between the dimensionality-reduced features of the nth front-end test case in the dimension-reduced test feature matrix and the dimension-reduced features of each front-end historical case in the dimension-reduced historical feature matrix, to obtain the similarity degree matrix.
  • the test feature matrix after dimensionality reduction The historical feature matrix after each dimensionality reduction
  • the test platform calculates the test feature matrix after dimensionality reduction through the cosine theorem
  • the dimensionality-reduced feature of the n-th front-end test case in and the historical feature matrix of the k-th dimensionality-reduction matrix
  • the similarity of the dimensionality-reduced features of each front-end historical case in based on multiple similarities Get the similarity matrix Among them, the similarity matrix
  • the size is 1 ⁇ N.
  • the test platform calculates the dimensionality-reduced test feature matrix through the cosine theorem The nth row in and the historical feature matrix after dimensionality reduction The similarity between each column in
  • Step 309 based on the similarity matrix, when it is determined that all front-end test cases satisfy the automated test conditions, perform automated tests on all front-end test cases in the system version set to be tested.
  • step 309 is based on the similarity matrix, and when it is determined that all front-end test cases meet the automated test conditions, all front-end test cases in the system version set to be tested are automatically tested, which can be implemented through the steps shown in Figure 7:
  • Step 601. Obtain the weight corresponding to each historical system version set in at least two historical system version sets.
  • the test platform sets a weight w k for the similarity matrix ⁇ k corresponding to each historical system version set, and the weight w k is greater than or equal to 0 and less than or equal to 1.
  • the weight w k is greater than or equal to 0 and less than or equal to 1.
  • w k is the weight w k corresponding to each similarity matrix ⁇ k
  • k is the sequence number of each historical system version set in all historical system version sets
  • K is the total number of all historical system version sets
  • the test platform obtains the weight w k corresponding to each historical system version set k in at least two historical system version sets.
  • Step 602 based on the similarity matrix between each front-end test case after dimension reduction and case n in all front-end historical cases in each historical version set after dimensionality reduction, and the weight corresponding to each historical system version set, generate Each target correlation matrix between the version set of the system under test and the set of all historical system versions.
  • each front-end test case after dimension reduction includes the feature after dimension reduction of the front-end test case, and the cases in all front-end historical cases in each historical version set after dimension reduction include the features of each front-end historical case.
  • the test platform generates each target correlation matrix y k between the system version set to be tested and all historical system version sets, which can also be implemented in the following manner:
  • Step 1 Acquire weights and supplementary factors corresponding to each historical system version set in at least two historical system version sets.
  • Step2 based on the similarity matrix between each front-end test case after dimensionality reduction and case n in all front-end historical cases in each historical version set after dimensionality reduction, the weight and supplementary factor corresponding to each historical system version set , to generate each target correlation matrix between the version set of the system under test and the set of all historical system versions.
  • the test platform obtains the weight w k corresponding to each historical system version set in at least two historical system version sets, and after supplementing the factor h, compares each front-end test case after dimensionality reduction with the reduced dimensionality
  • the similarity matrix ⁇ k between cases n in all front-end historical cases in each historical version set, the weight w k corresponding to each historical system version set and the supplementary factor h, through y k w k ⁇ k +h, Generate each target correlation matrix y k between the version set of the system to be tested and the set of all historical system versions. In this way, by setting the supplementary factor h, each element in each target correlation matrix y k between the generated system-under-test version set and all historical system version sets is prevented from being 0.
  • Step 603 Obtain the maximum value of each row in each target correlation matrix, and determine a first number of maximum values greater than the first target threshold among the maximum values of all rows in each target correlation matrix.
  • the test platform obtains the maximum value of each row in each target correlation matrix y k , and obtains the maximum value greater than the first target threshold such as 1/2 among the maximum values of all rows in each target correlation matrix y k , and determine the first quantity sum1 greater than the maximum value of 1/2.
  • Step 604. Calculate the ratio of the first quantity to the total number of all front-end test cases in the system version set under test to obtain the first ratio.
  • the test platform calculates the ratio of the first quantity sum1 to the total number N of all front-end test cases in the system version set to be tested to obtain the first ratio z1, wherein,
  • Step 605. Obtain a second number of first ratios greater than the second target threshold among all first ratios corresponding to each target correlation matrix.
  • the test platform obtains the first ratio z1 greater than the second target threshold such as 1/2 among all the first ratios z1 corresponding to each target correlation matrix y k , and determines the first ratio z1 greater than 1/2 The second quantity sum2.
  • Step 606. Calculate the ratio of the second quantity to the total number of the historical system version sets to obtain the second ratio.
  • the test platform calculates the ratio of the second number sum2 to the total number K of the historical system version set to obtain the second ratio z2, wherein,
  • Step 607. If the second ratio is greater than the third target threshold, determine that all front-end test cases meet the automated test conditions, and perform automated tests on all front-end test cases in the system version set to be tested.
  • the test platform determines that the second ratio z2 is greater than the third target threshold such as 1/2, which means that all front-end test cases in the system version set to be tested are compared with all front-end historical test cases in the historical system version set. The difference is not big. At this point, it is determined that all front-end test cases meet the automated test conditions. And after the developer makes a small change to some of the scripts in the automation scripts corresponding to the historical system version set, the test platform can perform automated tests on all front-end test cases in the system version set to be tested through the modified automation scripts, so that It establishes a unified standard for the automated execution of front-end test cases, improves the accuracy of judgment, and improves processing efficiency without relying on manual operations.
  • the third target threshold such as 1/2
  • FIG. 8 is an optional flow chart of the test case processing method provided by the embodiment of the present application, which will be described in conjunction with the steps shown in FIG. 8,
  • Step 701. Obtain all test cases in the version set of the system to be tested.
  • Step 702 Input all test cases in the version set of the system to be tested into the trained classifier, and obtain all front-end test cases in all test cases output by the trained classifier.
  • Step 703. Obtain all front-end historical cases in each historical system version set in at least two historical system version sets.
  • Step 704 based on the word frequency-reverse document frequency algorithm, calculate the eigenvalues corresponding to the features in each front-end test case in all front-end test cases, and obtain the test feature matrix of the system version set to be tested.
  • Step 705 Based on the word frequency-reverse document frequency algorithm, calculate the eigenvalues corresponding to the features in each front-end historical case in all front-end historical cases in each historical system version set, and obtain the historical feature matrix of each historical system version set.
  • Step 706 based on the non-negative matrix factorization algorithm, perform dimension reduction processing on the test feature matrix to obtain a dimension-reduced test feature matrix.
  • Step 707 based on the non-negative matrix factorization algorithm, perform dimensionality reduction processing on the historical feature matrix to obtain a dimensionally reduced historical feature matrix.
  • Step 708 Calculate the similarity between the dimensionality-reduced feature of the nth front-end test case in the dimension-reduced test feature matrix and the dimension-reduced feature of each front-end historical case in the dimension-reduced historical feature matrix, and obtain the similarity degree matrix.
  • Step 709 based on the obtained weight and supplementary factor corresponding to each historical system version set, and the relationship between each front-end test case after dimension reduction and case n in all front-end historical cases in each historical version set after dimension reduction
  • the similarity matrix of is used to generate each target correlation matrix between the version set of the system under test and the set of all historical system versions.
  • Step 710 based on each target correlation matrix between the system version set to be tested and all historical system version sets, determine whether all front-end test cases meet the automated test conditions, so as to determine whether to perform all front-end test cases in the system version set to be tested automated test.
  • the test platform obtains all front-end test cases and all front-end historical cases in the system version set to be tested, first, it calculates the number of front-end test cases in each front-end test case through the word frequency-reverse file frequency algorithm.
  • the eigenvalues corresponding to the features of the system to be tested are obtained from the test feature matrix of the system version set to be tested, and the eigenvalues corresponding to the features in each front-end historical case are calculated to obtain the historical feature matrix of each historical system version set; secondly, through the non-negative matrix Algorithm, the dimensionality reduction process is performed on the test feature matrix and the historical feature matrix respectively, and the similarity processing is performed on the dimensionality-reduced test feature matrix and the dimensionality-reduced historical feature matrix to obtain a similarity matrix, and then according to the similarity matrix, When it is determined that all front-end test cases meet the automation test conditions, perform automated tests on all front-end test cases.
  • this application solves the problem that related technologies must rely on manual operation and uncertainty brought by manual subjectivity, and at least has the problems of low efficiency and poor accuracy; realizes the establishment of a unified standard for the automatic execution of front-end test cases, and improves The accuracy of judgment is improved, and at the same time, it does not need to rely on manual operation, which improves the processing efficiency.
  • test case processing device 154 provided by the embodiment of the present application implemented as a software module.
  • the software modules stored in the test case processing device 154 of the memory 150 can be is a test case processing device in the test platform 100, including:
  • An acquisition module 1541 configured to acquire all front-end test cases in the system version set to be tested, and all front-end historical cases in each of the at least two historical system version sets;
  • the processing module 1542 is used to calculate the eigenvalues corresponding to the features in each front-end test case in all front-end test cases, and obtain the test feature matrix of the system version set to be tested;
  • the processing module 1542 is also used to calculate the eigenvalues corresponding to the features in each front-end historical case in all front-end historical cases in each historical system version set, and obtain the historical feature matrix of each historical system version set;
  • the processing module 1542 is also used to perform dimensionality reduction processing on the test feature matrix based on the non-negative matrix factorization algorithm, and obtain the test feature matrix after dimensionality reduction;
  • the processing module 1542 is also used to perform dimensionality reduction processing on the historical feature matrix based on the non-negative matrix factorization algorithm to obtain the historical feature matrix after dimensionality reduction;
  • the processing module 1542 is also used to calculate the similarity between the dimension-reduced features of the nth front-end test case in the dimension-reduced test feature matrix and the dimension-reduced features of each front-end historical case in the dimension-reduced historical feature matrix degree, to obtain a similarity matrix; wherein, n is a positive integer greater than or equal to 1 and less than or equal to N, and N is the total number of front-end test cases in the system version set to be tested;
  • the processing module 1542 is further configured to perform automated testing on all front-end test cases in the system version set to be tested when it is determined that all front-end test cases meet the automated test conditions based on the similarity matrix.
  • the processing module 1542 is further configured to decompose the test feature matrix through a non-negative matrix decomposition algorithm based on the determined feature number after dimensionality reduction of the test feature matrix to obtain a test projection matrix and a test fundamental matrix; obtain Module 1541 is also used to obtain the first product matrix obtained by multiplying the test projection matrix and the test fundamental matrix; the processing module 1542 is also used to obtain the first difference matrix obtained by subtracting the first product matrix from the test feature matrix according to the difference value The threshold matrix is used to determine the test projection matrix corresponding to the first difference matrix as the dimension-reduced test feature matrix.
  • the processing module 1542 is further configured to adjust each element in the test projection matrix through the projection matrix adjustment model to obtain the adjusted test projection matrix if the first difference matrix does not meet the difference threshold matrix ;
  • Each element in the test fundamental matrix is adjusted through the fundamental matrix adjustment model to obtain the adjusted test fundamental matrix;
  • the acquisition module 1541 is also used to obtain the adjusted test projection matrix and multiply the adjusted test fundamental matrix to obtain The second product matrix;
  • the processing module 1542 is also used to determine the adjusted test projection matrix corresponding to the second difference matrix if the second difference matrix obtained by subtracting the second product matrix from the test feature matrix meets the difference threshold matrix is the test feature matrix after dimensionality reduction.
  • the projection matrix adjustment model is:
  • W' ns is the element in row n and column s in the adjusted test projection matrix
  • W is the test projection matrix
  • W ns is the element in row n and column s in the test projection matrix
  • X is the test feature matrix
  • B is the test fundamental matrix
  • B T is the transpose matrix of the test fundamental matrix
  • (XB T ) ns is the nth row s of the matrix obtained by multiplying the test feature matrix X and the transpose matrix B T of the test fundamental matrix
  • the element of the column, (WBB T ) ns is the element of the nth row and the sth column in the matrix obtained after multiplying the transposition matrix B T of the test projection matrix W, the test fundamental matrix B and the test fundamental matrix;
  • the basic matrix adjustment model is:
  • B'sm is the element of the sth row and the mth column in the adjusted test fundamental matrix
  • B is the test fundamental matrix
  • B sm is the element of the sth row and the mth column in the test fundamental matrix
  • X is the test feature matrix
  • W is the test projection matrix
  • W T is the transpose matrix of the test projection matrix
  • (W T X) sm is the matrix obtained by multiplying the transpose matrix W T of the test projection matrix by the test feature matrix X
  • the elements in column m, (W T WB ) sm are the elements in row s and column m in the matrix obtained by multiplying the transposition matrix W T of the test projection matrix, the test projection matrix W and the test fundamental matrix B.
  • the processing module 1542 is further configured to adjust each element in the adjusted test projection matrix through the projection matrix adjustment model to obtain a new adjusted The test projection matrix; each element in the adjusted test fundamental matrix is adjusted through the fundamental matrix adjustment model to obtain a new adjusted test fundamental matrix; the acquisition module 1541 is also used to obtain the newly adjusted test projection matrix and The third product matrix obtained by multiplying the newly adjusted test fundamental matrix; the processing module 1542 is also used to determine the third difference matrix if the third difference matrix obtained by subtracting the third product matrix from the test feature matrix meets the difference threshold matrix.
  • the newly adjusted test projection matrix corresponding to the value matrix is the dimensionality-reduced test feature matrix.
  • the obtaining module 1541 is also used to obtain the weight corresponding to each historical system version set in at least two historical system version sets; the processing module 1542 is also used to obtain the weight corresponding to each front-end test case and The similarity matrix between cases n in all front-end historical cases in each historical version set after dimensionality reduction, and the weight corresponding to each historical system version set, generate the relationship between the system version set to be tested and all historical system version sets Each target correlation matrix; the acquisition module 1541 is also used to obtain the maximum value of each row in each target correlation matrix, and determine among the maximum values of all rows in each target correlation matrix, which is greater than the maximum value of the first target threshold The first quantity; the processing module 1542 is also used to calculate the ratio of the first quantity and the total number of all front-end test cases in the system version set to be tested to obtain the first ratio; the acquisition module 1541 is also used to obtain each target correlation matrix Among all the corresponding first ratios, the second number of the first ratio greater than the second target threshold; the processing module 1542 is
  • the obtaining module 1541 is also used to obtain all test cases in the version set of the system under test; the processing module 1542 is also used to input all test cases in the version set of the system under test to the trained classifier In , get all front-end test cases in all test cases output by the trained classifier.
  • the embodiment of the present application provides a storage medium storing executable instructions, and the executable instruction is stored therein.
  • the executable instruction When executed by the processor, it will cause the processor to execute the method provided in the embodiment of the present application, for example, as shown in FIG. 2 - the method shown in Figure 3, Figure 5- Figure 8.
  • the storage medium can be a computer-readable storage medium, for example, a ferroelectric memory (FRAM, Ferromagnetic Random Access Memory), a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read Only Memory), Erasable Programmable Read Only Memory (EPROM, Erasable Programmable Read Only Memory), Electrically Erasable Programmable Read Only Memory (EEPROM, Electrically Erasable Programmable Read Only Memory), flash memory, magnetic surface memory, optical disc, Or memory such as CD-ROM (Compact Disk-Read Only Memory); It can also be various devices including one or any combination of the above-mentioned memories.
  • FRAM Ferroelectric memory
  • ROM Read Only Memory
  • PROM programmable read-only memory
  • EPROM Erasable Programmable Read Only Memory
  • EEPROM Electrically Erasable Programmable Read Only Memory
  • flash memory magnetic surface memory
  • optical disc Or memory such as CD-ROM (Compact Disk-Read Only Memory); It can also be various
  • executable instructions may take the form of programs, software, software modules, scripts, or code written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and its Can be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.
  • executable instructions may, but do not necessarily correspond to files in a file system, may be stored as part of a file that holds other programs or data, for example, in Hyper Text Markup Language (HTML, Hyper Text Markup Language) in one or more scripts in a document, in a single file dedicated to the program in question, or in multiple cooperating files (for example, a file that stores one or more modules, subroutines, or code sections )middle.
  • executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or alternatively, on multiple computing devices distributed across multiple sites and interconnected by a communication network. to execute.
  • the embodiment of the present application provides a test case processing method, test case processing device, test platform and storage medium, by obtaining all front-end test cases in the system version set to be tested, and each historical system in at least two historical system version sets All front-end historical cases in the version set; calculate the eigenvalues corresponding to the features in each front-end test case in all front-end test cases, and obtain the test feature matrix of the system version set to be tested; calculate all front-end history in each historical system version set In the case, the eigenvalues corresponding to the features in each front-end historical case are obtained to obtain the historical feature matrix of each historical system version set; based on the non-negative matrix decomposition algorithm, the dimensionality reduction process is performed on the test feature matrix to obtain the dimensionality-reduced test features matrix; based on the non-negative matrix factorization algorithm, the dimensionality reduction process is performed on the historical feature matrix to obtain the dimensionality-reduced historical feature matrix; the dimensionality-reduced feature and the dimensionality-reduced
  • this application solves the problem that related technologies must rely on manual operation and uncertainty brought by manual subjectivity, and at least has the problems of low efficiency and poor accuracy; realizes the establishment of a unified standard for the automatic execution of front-end test cases, and improves The accuracy of judgment is improved, and at the same time, it does not need to rely on manual operation, which improves the processing efficiency.

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Abstract

L'invention concerne un procédé et un appareil de traitement de cas de test, une plate-forme et un support de stockage. Le procédé comprend les étapes consistant à : obtenir tous les cas de test frontaux et tous les cas historiques frontaux ; calculer une valeur de caractéristique correspondant à une caractéristique dans chaque cas de test frontal pour obtenir une matrice de caractéristiques de test d'un ensemble de versions de système à tester, et calculer une valeur de caractéristique correspondant à une caractéristique dans chaque cas historique frontal pour obtenir une matrice de caractéristiques historiques de chaque ensemble de versions de système historiques ; sur la base d'un algorithme de factorisation matricielle non négative, effectuer un traitement de réduction de dimension sur la matrice de caractéristiques de test et la matrice de caractéristiques historiques respectivement pour obtenir une matrice de caractéristiques de test à dimension réduite et une matrice de caractéristiques historiques à dimension réduite ; calculer une similarité entre une caractéristique à dimension réduite du nième cas de test frontal dans la matrice de caractéristiques de test à dimension réduite et une caractéristique à dimension réduite de chaque cas historique frontal dans la matrice de caractéristiques historiques à dimension réduite pour obtenir une matrice de similarité ; et sur la base de la matrice de similarité, déterminer que tous les cas de test frontaux satisfont aux conditions de test automatisé, et effectuer un test automatisé sur tous les cas de test frontaux.
PCT/CN2021/136833 2021-06-29 2021-12-09 Procédé et appareil de traitement de cas de test, plate-forme et support de stockage WO2023273177A1 (fr)

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