CN113609023A - Precise test method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to the technical field of artificial intelligence, and discloses a precise test method, a device, equipment and a storage medium, wherein the method comprises the following steps: the precise test request carries an application version identification to be tested and a target reference application version identification; determining a target test case coding prediction model in a test case coding prediction model library according to the target reference application version identification; determining a target test case coding data set according to the code base, the application version identification to be tested, the target reference application version identification and the target test case coding prediction model; determining a target test case identification set according to the target test case coding data set and the mapping list of the test case library; and determining an accurate test result according to the target test case identification set, the test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested. The method overcomes the defect that the mapping relation between the test case and the code is obtained by adopting two ideas of dynamic analysis and static analysis.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a method, an apparatus, a device, and a storage medium for accurate testing.
Background
The accurate test adopts professional test means and methods, automatically collects, stores, calculates and visually displays the original data in the test process, accurately analyzes, improves and optimizes the tested object, and improves the test efficiency. And the accurate test establishes a mapping relation between the test case and the code on the basis of the completeness of the test case, completes data processing based on the mapping relation, is used for rapidly evaluating the influence range of code modification in smoking and regression stages, and finds out the corresponding test case according to the influence range, thereby reducing the test cost and improving the test efficiency.
In the traditional precise test, two ideas, namely dynamic analysis and static analysis, are generally adopted to obtain the mapping relation between the test cases and the codes. And the dynamic analysis adopts a code coverage rate tool, records a passed internal method when the test case is executed to generate a corresponding code coverage rate file, and obtains the mapping relation between the test case and the code by analyzing the code coverage rate file. Dynamic analysis methods rely on code coverage tools, which are incomplete or non-existent for certain development languages, some of which have performance problems and restart and deployment of services can result in loss of code coverage files. Static analysis relies on byte code analysis to obtain a series of parent-child nodes of a call chain, but byte code analysis has natural short boards in the polymorphic aspect, which easily causes the loss of a range needing to be tested after code modification.
Disclosure of Invention
The application mainly aims to provide an accurate test method, an accurate test device, an accurate test equipment and a storage medium, and aims to solve the technical problems that in the prior art, an accurate test adopts two ideas of dynamic analysis and static analysis to obtain a mapping relation between a test case and a code, a code coverage rate tool is incomplete or does not exist in the dynamic analysis, and a code coverage rate file is possibly lost in the dynamic analysis, and a bytecode analysis in the static analysis has a natural short board in a polymorphic aspect, so that the loss of a range to be tested after the code is changed is easily caused.
In order to achieve the above object, the present application provides an accurate testing method, including:
acquiring an accurate test request, wherein the accurate test request carries an application version identification to be tested and a target reference application version identification;
obtaining a test case code prediction model library, and performing test case code prediction model matching in the test case code prediction model library according to the target reference application version identification to obtain a target test case code prediction model;
acquiring a code base, and performing test case coding prediction according to the code base, the to-be-tested application version identification, the target reference application version identification and the target test case coding prediction model to obtain a target test case coding data set;
acquiring a test case library, and determining test case identifiers according to the target test case coding data set and the mapping list of the test case library to obtain a target test case identifier set;
and carrying out accurate test according to the target test case identification set, the test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested to obtain an accurate test result.
Further, before the step of obtaining the test case coding prediction model library, the method further includes:
obtaining a model training request, wherein the model training request carries an application identifier to be trained;
obtaining a training sample set according to the application identifier to be trained, wherein the training samples in the training sample set comprise: code feature vector samples and test case coding calibration data;
dividing the training sample set by adopting a preset sample division rule to obtain a training set and a test set;
training an initial model by adopting the training set, and taking the trained initial model as a model to be verified, wherein the initial model is a model obtained based on a deep neural network;
verifying the model to be verified by adopting the test set to obtain a model verification result;
when the model verification result is failed, taking the model to be verified as the initial model, repeatedly executing the step of adopting a preset sample division rule to divide the training sample set to obtain a training set and a test set until the model verification result is passed;
taking the model to be verified with the model verification result of passing as a test case coding prediction model to be stored;
and taking the application identifier to be trained and the test case coding prediction model to be stored as associated data, and updating the test case coding prediction model library according to the associated data.
Further, the step of dividing the training sample set by using a preset sample division rule to obtain a training set and a test set includes:
randomly adjusting the arrangement sequence of the training samples in the training sample set to obtain a training sample set with the sequence adjusted;
and dividing the training samples in the training sample set after the sequence adjustment into two sets by adopting a preset dividing proportion to obtain the training set and the test set.
Further, the initial model sequentially includes: the device comprises a first convolution layer, a first maximum pooling layer, a second convolution layer, a second maximum pooling layer, a first full-connection layer and a second full-connection layer, wherein the second full-connection layer adopts a Sigmoid function as an activation function.
Further, the step of performing test case coding prediction according to the code library, the to-be-tested application version identifier, the target reference application version identifier and the target test case coding prediction model to obtain a target test case coding data set includes:
according to the code base and the target reference application version identification, difference code acquisition is carried out on the to-be-processed application version identification to obtain a to-be-processed difference code;
extracting a feature vector according to the difference code to be processed to obtain a feature vector to be predicted;
and inputting the feature vector to be predicted into the target test case coding prediction model to perform test case coding prediction, so as to obtain the target test case coding data set.
Further, the step of extracting a feature vector according to the difference code to be processed to obtain a feature vector to be predicted includes:
acquiring a class path keyword, and acquiring a class path identifier from the difference code to be processed according to the class path keyword to obtain a class path identifier set to be processed;
respectively carrying out feature coding, vectorization processing and normalization processing according to the class path identifier set to be processed to obtain class path feature vectors;
acquiring class name keywords, and acquiring class names from the difference codes to be processed according to the class name keywords to obtain a class name set to be processed;
respectively carrying out feature coding, vectorization processing and normalization processing according to the class name set to be processed to obtain class name feature vectors;
acquiring an interface name keyword, and acquiring an interface name from the difference code to be processed according to the interface name keyword to obtain an interface name set to be processed;
respectively carrying out feature coding, vectorization processing and normalization processing according to the interface name set to be processed to obtain an interface name feature vector;
acquiring a function name keyword, and acquiring a function name from the to-be-processed difference code according to the function name keyword to obtain a to-be-processed function name set;
respectively carrying out feature coding, vectorization processing and normalization processing according to the function name set to be processed to obtain function name feature vectors;
acquiring abstract syntax tree keywords, and acquiring abstract syntax tree identifications from the difference codes to be processed according to the abstract syntax tree keywords to obtain an abstract syntax tree identification set to be processed;
respectively carrying out feature coding, vectorization processing and normalization processing according to the abstract syntax tree identification set to be processed to obtain abstract syntax tree feature vectors;
and performing matrix splicing according to the class path feature vector, the class name feature vector, the interface name feature vector, the function name feature vector and the abstract syntax tree feature vector to obtain the feature vector to be predicted.
Further, the step of performing an accurate test according to the target test case identifier set, the test case set of the test case library, the code library, the target reference application version identifier and the application version identifier to be tested to obtain an accurate test result includes:
according to the target test case identification set, test cases are obtained from the test case set of the test case library to obtain a test case set to be tested;
acquiring a difference code from the code library according to the target reference application version identification and the application version identification to be detected to obtain a target difference code;
and carrying out accurate test on the target difference code according to the test case set to be tested to obtain the accurate test result.
This application has still provided an accurate testing arrangement, the device includes:
the device comprises a request acquisition module, a target standard application version identification and a test result generation module, wherein the request acquisition module is used for acquiring a precise test request which carries the application version identification to be tested and the target standard application version identification;
the target test case code prediction model determining module is used for acquiring a test case code prediction model library, and performing test case code prediction model matching in the test case code prediction model library according to the target reference application version identification to obtain a target test case code prediction model;
the target test case coding data set determining module is used for acquiring a code base and carrying out test case coding prediction according to the code base, the application version identification to be tested, the target reference application version identification and the target test case coding prediction model to obtain a target test case coding data set;
the target test case identification set determining module is used for acquiring a test case library, and determining the test case identification according to the target test case coding data set and the mapping list of the test case library to obtain a target test case identification set;
and the accurate test result determining module is used for carrying out accurate test according to the target test case identification set, the test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested to obtain an accurate test result.
The present application further proposes a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of any of the above methods when executing the computer program.
The present application also proposes a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of any of the above.
The method comprises the steps of firstly obtaining a precise test request, wherein the precise test request carries an application version identification to be tested and a target reference application version identification, secondly carrying out test case coding prediction model matching in a test case coding prediction model library according to the target reference application version identification to obtain a target test case coding prediction model, then carrying out test case coding prediction according to the code library, the application version identification to be tested, the target reference application version identification and the target test case coding prediction model to obtain a target test case coding data set, carrying out test case identification determination according to the target test case coding data set and a mapping list of the test case library to obtain a target test case identification set, and finally carrying out test case identification determination according to the target test case identification set, The method comprises the steps of accurately testing a test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested to obtain an accurate test result, and improving the accuracy of the determined target test case identification set through a target test case coding prediction model, so that the accuracy of the accurate test is improved.
Drawings
Fig. 1 is a schematic flowchart of a precision testing method according to an embodiment of the present application;
FIG. 2 is a block diagram of a precise testing apparatus according to an embodiment of the present disclosure;
fig. 3 is a block diagram illustrating a structure of a computer device according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Referring to fig. 1, an embodiment of the present application provides a precision testing method, where the method includes:
s1: acquiring an accurate test request, wherein the accurate test request carries an application version identification to be tested and a target reference application version identification;
s2: obtaining a test case code prediction model library, and performing test case code prediction model matching in the test case code prediction model library according to the target reference application version identification to obtain a target test case code prediction model;
s3: acquiring a code base, and performing test case coding prediction according to the code base, the to-be-tested application version identification, the target reference application version identification and the target test case coding prediction model to obtain a target test case coding data set;
s4: acquiring a test case library, and determining test case identifiers according to the target test case coding data set and the mapping list of the test case library to obtain a target test case identifier set;
s5: and carrying out accurate test according to the target test case identification set, the test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested to obtain an accurate test result.
In this embodiment, a precise test request is first obtained, where the precise test request carries an application version identifier to be tested and a target reference application version identifier, then test case coding prediction model matching is performed in the test case coding prediction model library according to the target reference application version identifier to obtain a target test case coding prediction model, then test case coding prediction is performed according to the code library, the application version identifier to be tested, the target reference application version identifier and the target test case coding prediction model to obtain a target test case coding data set, test case identifier determination is performed according to the target test case coding data set and a mapping list of the test case library to obtain a target test case identifier set, and finally, a test case set, a target test case identifier set, a test case set, a target test case, a target test case, a target, a test case, The code library, the target reference application version identification and the application version identification to be tested are subjected to accurate testing to obtain an accurate testing result, the accuracy of the determined target testing case identification set is improved through the target testing case coding prediction model, so that the accuracy of the accurate testing is improved, the whole process is not limited to a language of application development, and the defect that the mapping relation between the testing case and the code is obtained through two ideas of dynamic analysis and static analysis is overcome.
For S1, an accurate test request input by a user may be obtained, an accurate test request sent by a third-party application system may also be obtained, or an accurate test request triggered by a program according to a preset trigger condition may also be obtained. For example, the preset trigger condition is 3 points per day, which is not specifically limited by this example.
And the accurate test request is a request for accurately testing the difference codes corresponding to the to-be-tested application version identification and the target reference application version identification.
The version identification of the application to be tested is the application version identification of the version of the target application to be tested.
The target benchmark application version identification is a benchmark application version identification adopted by the accurate test request. The reference application version identification is an application version identification of a version of the target application that has been released to the production environment and is running stably.
The application version identification is an application version ID.
For S2, the test case coding prediction model library may be obtained from a database, or may be obtained from a third-party application system.
The test case coding prediction model library comprises: the method comprises application identifications and test case coding prediction models, wherein each application identification corresponds to one test case coding prediction model. The test case coding prediction model is a model obtained based on deep neural network training.
The application identification is an application ID.
Acquiring an application identifier according to the target reference application version identifier to obtain an application identifier to be matched; and matching the application identification to be matched in the test case code prediction model library, and taking the test case code prediction model corresponding to the application identification matched in the test case code prediction model library as a target test case code prediction model.
For S3, obtaining a code repository from the code repository server; and determining a difference code according to the code library, the application version identification to be tested and the target reference application version identification, extracting a feature vector according to the difference code, inputting all the extracted feature vectors into the target test case coding prediction model to predict the test case coding, and obtaining the target test case coding data set.
For example, the code repository server uses Gitlab (which is an open source application developed by Ruby on Rails to implement a self-hosting Git project repository), and the examples herein are not limited specifically.
For S4, the test case library may be obtained from a database, or may be obtained from a third-party application system.
The test case library comprises: mapping list and test case set. The mapping list includes: the test case identification and the test case coding data, wherein each test case identification corresponds to one test case coding data. The test case identifier may be data that uniquely identifies one test case, such as a test case name and a test case ID. The test case coding data is data obtained by coding the test case identification by adopting a coding dictionary. The test case set comprises one or more test cases, and each test case carries a test case identifier. It is to be understood that one or more test sub-cases may be included in a test case.
Optionally, the test case encoding data is encoding data obtained by performing unique hot encoding on the test case identifier.
And searching the test case coded data in the mapping list of the test case library according to each test case coded data in the target test case coded data set, taking the test case identifier corresponding to each test case coded data searched in the mapping list of the test case library as a target test case identifier, and obtaining the target test case identifier set according to all the target test case identifiers.
And S5, acquiring test cases from the test case set of the test case library according to the target test case identification set, testing the acquired test cases to accurately test the difference codes determined according to the code library, the application version identification to be tested and the target reference application version identification, and taking the data obtained by testing as an accurate test result.
In an embodiment, before the step of obtaining the test case coding prediction model library, the method further includes:
s21: obtaining a model training request, wherein the model training request carries an application identifier to be trained;
s22: obtaining a training sample set according to the application identifier to be trained, wherein the training samples in the training sample set comprise: code feature vector samples and test case coding calibration data;
s23: dividing the training sample set by adopting a preset sample division rule to obtain a training set and a test set;
s24: training an initial model by adopting the training set, and taking the trained initial model as a model to be verified, wherein the initial model is a model obtained based on a deep neural network;
s25: verifying the model to be verified by adopting the test set to obtain a model verification result;
s26: when the model verification result is failed, taking the model to be verified as the initial model, repeatedly executing the step of adopting a preset sample division rule to divide the training sample set to obtain a training set and a test set until the model verification result is passed;
s27: taking the model to be verified with the model verification result of passing as a test case coding prediction model to be stored;
s28: and taking the application identifier to be trained and the test case coding prediction model to be stored as associated data, and updating the test case coding prediction model library according to the associated data.
According to the method and the device, the training set is adopted to train the initial model and the test set is adopted to test the trained initial model, the initial model is obtained based on the deep neural network, and therefore the accuracy of the determined test case coding prediction model to be stored is improved.
For S21, a model training request input by the user may be obtained, and a model training request sent by a third-party application system may also be obtained.
And a model training request, namely a request for training the initial model to obtain the test case coding prediction model.
The application identifier to be trained is the application identifier of the application needing to train the test case coding prediction model.
For S22, a training sample set input by the user may be obtained according to the application identifier to be trained, or a training sample set sent by a third-party application system may be obtained according to the application identifier to be trained. That is, the data of the training sample set is the data identified for the application to be trained.
It is to be understood that the training samples are training samples derived based on manual labeling and/or code coverage tools.
The code feature vector sample is a vector obtained by extracting the difference of the code corresponding to the application version identifier to be predicted of the application identifier to be trained relative to the difference code corresponding to the reference application version identifier corresponding to the application identifier to be trained.
The code feature vector samples include, but are not limited to: class path feature vector samples, class name feature vector samples, interface name feature vector samples, function name feature vector samples and abstract syntax tree feature vector samples. The class path feature vector sample is a standardized feature vector obtained by performing feature coding, vectorization and normalization on a class path in a difference code corresponding to the application identifier to be trained. The class name feature vector sample is a standardized feature vector obtained by performing feature coding, vectorization and normalization on the class name in the difference code corresponding to the application identifier to be trained. The interface name feature vector sample is a standardized feature vector obtained by performing feature coding, vectorization and normalization on the interface name in the difference code corresponding to the application identifier to be trained. And the function name feature vector sample is a standardized feature vector obtained by performing feature coding, vectorization and normalization on the function name in the difference code corresponding to the application identifier to be trained. The abstract syntax tree feature vector sample is a standardized feature vector obtained by performing feature coding, vectorization and normalization on the abstract syntax tree in the difference code corresponding to the application identifier to be trained.
The test case coding calibration data is the calibration result of the test case coding data of the test case required by the code feature vector sample. For example, the test case coding calibration data is a coding vector, each vector element is a numerical value (which may include 0 or may also include 1) from 0 to 1, and when a vector element is 1, it means that the test case corresponding to the vector element is a test case required by the application identifier to be trained. That is to say, the number of vector elements in the test case coding calibration data is the same as the number of test cases corresponding to the application identifier to be trained.
And S23, dividing the training samples in the training sample set into two sets by adopting a preset dividing proportion to obtain a training set and a test set.
For example, the preset division ratio is: 7:3, that is, 70% of the training samples in the training sample set are divided into a training set, and 30% of the training samples in the training sample set are divided into a test set, which is not specifically limited by the example.
For S24, when the initial model is trained using the training set, the loss function of the initial model is binary cross entropy.
The specific steps of training the initial model by using the training set are not described herein.
And S25, verifying the model to be verified by using the test set, determining that the model verification result is passed when the verification result meets the expected verification requirement, and determining that the model verification result is failed when the verification result does not meet the expected verification requirement.
The specific steps of verifying the model to be verified by using the test set are not described herein again.
For S26, when the model verification result is failed, it means that the model to be verified does not meet the expected verification requirement yet, so the model to be verified may be used as the initial model, and steps S23 to S26 may be repeatedly performed until the model verification result is passed.
For S27, when the model verification result is pass, it means that the model to be verified also meets the expected verification requirement, and therefore the model to be verified that the model verification result is pass may be used as the test case coding prediction model to be stored.
And the test case coding prediction model to be stored is the test case coding prediction model which needs to be stored in the test case coding prediction model library.
For S28, the application identifier to be trained and the test case coding prediction model to be stored are used as associated data, the test case coding prediction model library is updated according to the associated data, that is, the application identifier to be trained is stored in the application identifier of the test case coding prediction model library, and the test case coding prediction model to be stored is stored in the test case coding prediction model of the test case coding prediction model library.
In an embodiment, the step of dividing the training sample set by using a preset sample division rule to obtain a training set and a test set includes:
s231: randomly adjusting the arrangement sequence of the training samples in the training sample set to obtain a training sample set with the sequence adjusted;
s232: and dividing the training samples in the training sample set after the sequence adjustment into two sets by adopting a preset dividing proportion to obtain the training set and the test set.
In the embodiment, the training samples in the training sample set are randomly adjusted in the arrangement sequence, and then the training samples in the training sample set after the sequence adjustment are divided into two sets by adopting a preset division ratio, so that cross validation is realized during each iterative training, the accuracy of model training is improved, and the efficiency of model training is improved.
For S231, in each iterative training, randomly adjusting the arrangement order of all the training samples in the training sample set, and using the training sample set after the adjustment as the training sample set after the order adjustment.
And S232, dividing the training samples in the training sample set after the sequence adjustment into two sets by adopting a preset dividing proportion to obtain the training set and the test set. So that the training set trained per iteration may be different and the test set trained per iteration may be different.
In one embodiment, the initial model sequentially includes: the device comprises a first convolution layer, a first maximum pooling layer, a second convolution layer, a second maximum pooling layer, a first full-connection layer and a second full-connection layer, wherein the second full-connection layer adopts a Sigmoid function as an activation function.
In the embodiment, the initial model obtained based on the deep neural network can also provide modeling for the complex nonlinear system, but the extra layers provide higher abstract layers for the model, so that the capability of the model is improved, and the accuracy of the test case coding prediction model obtained by training is improved.
Wherein the first convolution layer and the second convolution layer are both convolution layers. The first largest pooling layer and the second largest pooling layer are both largest pooling layers. The first full connection layer and the second full connection layer are full connection layers.
The first convolution layer, the first maximum pooling layer, the second convolution layer, the second maximum pooling layer and the first full-connection layer are used for feature extraction, and the second full-connection layer adopts a Sigmoid function as a final output layer.
In an embodiment, the step of performing test case coding prediction according to the code library, the to-be-tested application version identifier, the target reference application version identifier, and the target test case coding prediction model to obtain a target test case coding data set includes:
s31: according to the code base and the target reference application version identification, difference code acquisition is carried out on the to-be-processed application version identification to obtain a to-be-processed difference code;
s32: extracting a feature vector according to the difference code to be processed to obtain a feature vector to be predicted;
s33: and inputting the feature vector to be predicted into the target test case coding prediction model to perform test case coding prediction, so as to obtain the target test case coding data set.
According to the embodiment, the feature vector extraction is carried out according to the difference code to be processed, then all the extracted feature vectors are input into the target test case coding prediction model to carry out test case coding prediction, the accuracy of the determined target test case identification set is improved through the target test case coding prediction model, so that the accuracy of accurate test is improved, the whole process is not limited to application and development languages, and the defect that the mapping relation between the test cases and the codes is obtained by adopting two ideas of dynamic analysis and static analysis is overcome.
For step S31, according to the difference between the code configuration data of the to-be-detected application version identifier and the code configuration data of the target reference application version identifier, a difference code is obtained from the code library, and the obtained difference code is used as the difference code to be processed. That is, the difference code to be processed is the difference of the code of the application version identification to be tested with respect to the code of the target reference application version identification.
And S32, respectively extracting feature vectors according to the class path, the class name, the interface name, the function name and the abstract syntax tree of the difference code to be processed, and taking all the extracted feature vectors as the feature vectors to be predicted.
And S33, inputting the feature vector to be predicted into the target test case coding prediction model to predict test case coding data, and taking all the obtained test case coding data as the target test case coding data set.
In an embodiment, the step of extracting the feature vector according to the difference code to be processed to obtain the feature vector to be predicted includes:
s321: acquiring a class path keyword, and acquiring a class path identifier from the difference code to be processed according to the class path keyword to obtain a class path identifier set to be processed;
s322: respectively carrying out feature coding, vectorization processing and normalization processing according to the class path identifier set to be processed to obtain class path feature vectors;
s323: acquiring class name keywords, and acquiring class names from the difference codes to be processed according to the class name keywords to obtain a class name set to be processed;
s324: respectively carrying out feature coding, vectorization processing and normalization processing according to the class name set to be processed to obtain class name feature vectors;
s325: acquiring an interface name keyword, and acquiring an interface name from the difference code to be processed according to the interface name keyword to obtain an interface name set to be processed;
s326: respectively carrying out feature coding, vectorization processing and normalization processing according to the interface name set to be processed to obtain an interface name feature vector;
s327: acquiring a function name keyword, and acquiring a function name from the to-be-processed difference code according to the function name keyword to obtain a to-be-processed function name set;
s328: respectively carrying out feature coding, vectorization processing and normalization processing according to the function name set to be processed to obtain function name feature vectors;
s329: acquiring abstract syntax tree keywords, and acquiring abstract syntax tree identifications from the difference codes to be processed according to the abstract syntax tree keywords to obtain an abstract syntax tree identification set to be processed;
s3210: respectively carrying out feature coding, vectorization processing and normalization processing according to the abstract syntax tree identification set to be processed to obtain abstract syntax tree feature vectors;
s3211: and performing matrix splicing according to the class path feature vector, the class name feature vector, the interface name feature vector, the function name feature vector and the abstract syntax tree feature vector to obtain the feature vector to be predicted.
In this embodiment, feature vector extraction is respectively performed according to the class path, the class name, the interface name, the function name and the abstract syntax tree of the difference code to be processed, so that a basis is provided for subsequently adopting the target test case coding prediction model to predict test case coding data.
For S321, the class path keyword may be obtained from the database, or the class path keyword sent by the third-party application system may also be obtained.
And searching the class path keywords from the difference code to be processed, taking the next character string of each keyword searched from the difference code to be processed as a class path identifier, and taking all the obtained class path identifiers as the class path identifier set to be processed.
And S322, respectively performing feature coding, vectorization processing and normalization processing according to the class path identifier set to be processed, and taking the feature vector subjected to normalization processing as the class path feature vector.
And performing characteristic coding on the class path identification set to be processed by adopting unique hot coding.
For S323, the class name keyword may be obtained from the database, or the class name keyword sent by the third-party application system may be obtained.
And searching the class name key words from the difference codes to be processed, taking the next character string of each key word searched from the difference codes to be processed as a class name, and taking all the obtained class names as the class name set to be processed.
And S324, respectively performing feature coding, vectorization processing and normalization processing according to the class name set to be processed, and taking the feature vector subjected to normalization processing as the class name feature vector.
And performing characteristic coding on the class name set to be processed by adopting unique hot coding.
For S325, the interface name keyword may be obtained from the database, or the interface name keyword sent by the third-party application system may be obtained.
And searching the interface name keywords from the difference code to be processed, taking the next character string of each keyword searched from the difference code to be processed as an interface name, and taking all the obtained interface names as the interface name set to be processed.
And S326, respectively performing feature coding, vectorization processing and normalization processing according to the interface name set to be processed, and taking the feature vector subjected to normalization processing as the interface name feature vector.
And performing characteristic coding on the interface name set to be processed by adopting unique hot coding.
For S327, the function name keyword may be obtained from the database, or the function name keyword sent by the third-party application system may be obtained.
And searching the function name keywords from the to-be-processed difference codes, taking the next character string of each keyword searched from the to-be-processed difference codes as a function name, and taking all the obtained function names as the to-be-processed function name set.
And S328, respectively performing feature coding, vectorization processing and normalization processing according to the function name set to be processed, and taking the feature vector subjected to the normalization processing as the function name feature vector.
And performing characteristic coding on the function name set to be processed by adopting unique hot coding.
For S329, the abstract syntax tree keyword may be obtained from the database, or the abstract syntax tree keyword sent by the third-party application system may be obtained.
And searching the abstract syntax tree keywords from the difference code to be processed, taking the next character string of each keyword searched from the difference code to be processed as an abstract syntax tree identifier, and taking all the obtained abstract syntax tree identifiers as the abstract syntax tree identifier set to be processed.
And S3210, respectively performing feature coding, vectorization processing and normalization processing according to the abstract syntax tree identifier set to be processed, and taking the feature vector subjected to normalization processing as the abstract syntax tree feature vector.
And performing characteristic coding on the abstract syntax tree identification set to be processed by adopting unique hot coding.
And for S3211, performing matrix splicing on the class path feature vector, the class name feature vector, the interface name feature vector, the function name feature vector and the abstract syntax tree feature vector to obtain the feature vector to be predicted.
In an embodiment, the step of performing an accurate test according to the target test case identifier set, the test case set of the test case library, the code library, the target reference application version identifier, and the application version identifier to be tested to obtain an accurate test result includes:
s51: according to the target test case identification set, test cases are obtained from the test case set of the test case library to obtain a test case set to be tested;
s52: acquiring a difference code from the code library according to the target reference application version identification and the application version identification to be detected to obtain a target difference code;
s53: and carrying out accurate test on the target difference code according to the test case set to be tested to obtain the accurate test result.
According to the embodiment, the target test case identification set, the test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested are subjected to accurate test, so that the application corresponding to the application version identification to be tested is subjected to accurate test of the difference code, and the test efficiency is improved.
For S51, each target test case identifier in the target test case identifier set is searched in the test case set of the test case library, a test case corresponding to the test case identifier searched in the test case set is used as a test case to be tested, and all the test cases to be tested are used as the test case set to be tested.
For S52, according to the difference between the code configuration data of the to-be-tested application version identifier and the code configuration data of the target reference application version identifier, a difference code is obtained from the code library, and the obtained difference code is used as the target difference code.
For S53, the specific steps of accurately testing the target difference code according to the test case set to be tested are not described herein again.
Referring to fig. 2, the present application further provides a precision testing apparatus, the apparatus including:
a request obtaining module 100, configured to obtain an accurate test request, where the accurate test request carries an application version identifier to be tested and a target reference application version identifier;
the target test case code prediction model determining module 200 is used for acquiring a test case code prediction model library, and performing test case code prediction model matching in the test case code prediction model library according to the target reference application version identification to obtain a target test case code prediction model;
a target test case coding data set determining module 300, configured to obtain a code base, and perform test case coding prediction according to the code base, the to-be-tested application version identifier, the target reference application version identifier, and the target test case coding prediction model to obtain a target test case coding data set;
a target test case identification set determining module 400, configured to obtain a test case library, and determine a test case identification according to the target test case encoding data set and the mapping list of the test case library to obtain a target test case identification set;
and the accurate test result determining module 500 is configured to perform an accurate test according to the target test case identifier set, the test case set of the test case library, the code library, the target reference application version identifier, and the application version identifier to be tested, so as to obtain an accurate test result.
In this embodiment, a precise test request is first obtained, where the precise test request carries an application version identifier to be tested and a target reference application version identifier, then test case coding prediction model matching is performed in the test case coding prediction model library according to the target reference application version identifier to obtain a target test case coding prediction model, then test case coding prediction is performed according to the code library, the application version identifier to be tested, the target reference application version identifier and the target test case coding prediction model to obtain a target test case coding data set, test case identifier determination is performed according to the target test case coding data set and a mapping list of the test case library to obtain a target test case identifier set, and finally, a test case set, a target test case identifier set, a test case set, a target test case, a target test case, a target, a test case, The code library, the target reference application version identification and the application version identification to be tested are subjected to accurate testing to obtain an accurate testing result, the accuracy of the determined target testing case identification set is improved through the target testing case coding prediction model, so that the accuracy of the accurate testing is improved, the whole process is not limited to a language of application development, and the defect that the mapping relation between the testing case and the code is obtained through two ideas of dynamic analysis and static analysis is overcome.
Referring to fig. 3, a computer device, which may be a server and whose internal structure may be as shown in fig. 3, is also provided in the embodiment of the present application. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer designed processor is used to provide computational and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer equipment is used for storing data such as the accurate test method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a precision testing method. The accurate test method comprises the following steps: acquiring an accurate test request, wherein the accurate test request carries an application version identification to be tested and a target reference application version identification; obtaining a test case code prediction model library, and performing test case code prediction model matching in the test case code prediction model library according to the target reference application version identification to obtain a target test case code prediction model; acquiring a code base, and performing test case coding prediction according to the code base, the to-be-tested application version identification, the target reference application version identification and the target test case coding prediction model to obtain a target test case coding data set; acquiring a test case library, and determining test case identifiers according to the target test case coding data set and the mapping list of the test case library to obtain a target test case identifier set; and carrying out accurate test according to the target test case identification set, the test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested to obtain an accurate test result.
In this embodiment, a precise test request is first obtained, where the precise test request carries an application version identifier to be tested and a target reference application version identifier, then test case coding prediction model matching is performed in the test case coding prediction model library according to the target reference application version identifier to obtain a target test case coding prediction model, then test case coding prediction is performed according to the code library, the application version identifier to be tested, the target reference application version identifier and the target test case coding prediction model to obtain a target test case coding data set, test case identifier determination is performed according to the target test case coding data set and a mapping list of the test case library to obtain a target test case identifier set, and finally, a test case set, a target test case identifier set, a test case set, a target test case, a target test case, a target, a test case, The code library, the target reference application version identification and the application version identification to be tested are subjected to accurate testing to obtain an accurate testing result, the accuracy of the determined target testing case identification set is improved through the target testing case coding prediction model, so that the accuracy of the accurate testing is improved, the whole process is not limited to a language of application development, and the defect that the mapping relation between the testing case and the code is obtained through two ideas of dynamic analysis and static analysis is overcome.
An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements an accurate testing method, including the steps of: acquiring an accurate test request, wherein the accurate test request carries an application version identification to be tested and a target reference application version identification; obtaining a test case code prediction model library, and performing test case code prediction model matching in the test case code prediction model library according to the target reference application version identification to obtain a target test case code prediction model; acquiring a code base, and performing test case coding prediction according to the code base, the to-be-tested application version identification, the target reference application version identification and the target test case coding prediction model to obtain a target test case coding data set; acquiring a test case library, and determining test case identifiers according to the target test case coding data set and the mapping list of the test case library to obtain a target test case identifier set; and carrying out accurate test according to the target test case identification set, the test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested to obtain an accurate test result.
According to the executed accurate test method, firstly, an accurate test request is obtained, the accurate test request carries an application version identification to be tested and a target reference application version identification, then, test case coding prediction model matching is carried out in the test case coding prediction model base according to the target reference application version identification to obtain a target test case coding prediction model, then, test case coding prediction is carried out according to the code base, the application version identification to be tested, the target reference application version identification and the target test case coding prediction model to obtain a target test case coding data set, test case identification determination is carried out according to the target test case coding data set and a mapping list of the test case base to obtain a target test case identification set, and finally, the accurate test request carries the application version identification to be tested and the target reference application version identification, The method comprises the steps of accurately testing a test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested to obtain an accurate test result, and improving the accuracy of the determined target test case identification set through a target test case coding prediction model, so that the accuracy of the accurate test is improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided herein and used in the examples may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (SSRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. A method of precision testing, the method comprising:
acquiring an accurate test request, wherein the accurate test request carries an application version identification to be tested and a target reference application version identification;
obtaining a test case code prediction model library, and performing test case code prediction model matching in the test case code prediction model library according to the target reference application version identification to obtain a target test case code prediction model;
acquiring a code base, and performing test case coding prediction according to the code base, the to-be-tested application version identification, the target reference application version identification and the target test case coding prediction model to obtain a target test case coding data set;
acquiring a test case library, and determining test case identifiers according to the target test case coding data set and the mapping list of the test case library to obtain a target test case identifier set;
and carrying out accurate test according to the target test case identification set, the test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested to obtain an accurate test result.
2. The precise testing method according to claim 1, wherein the step of obtaining the test case coding prediction model library further comprises:
obtaining a model training request, wherein the model training request carries an application identifier to be trained;
obtaining a training sample set according to the application identifier to be trained, wherein the training samples in the training sample set comprise: code feature vector samples and test case coding calibration data;
dividing the training sample set by adopting a preset sample division rule to obtain a training set and a test set;
training an initial model by adopting the training set, and taking the trained initial model as a model to be verified, wherein the initial model is a model obtained based on a deep neural network;
verifying the model to be verified by adopting the test set to obtain a model verification result;
when the model verification result is failed, taking the model to be verified as the initial model, repeatedly executing the step of adopting a preset sample division rule to divide the training sample set to obtain a training set and a test set until the model verification result is passed;
taking the model to be verified with the model verification result of passing as a test case coding prediction model to be stored;
and taking the application identifier to be trained and the test case coding prediction model to be stored as associated data, and updating the test case coding prediction model library according to the associated data.
3. The accurate testing method according to claim 2, wherein the step of dividing the training sample set by using a preset sample division rule to obtain a training set and a testing set comprises:
randomly adjusting the arrangement sequence of the training samples in the training sample set to obtain a training sample set with the sequence adjusted;
and dividing the training samples in the training sample set after the sequence adjustment into two sets by adopting a preset dividing proportion to obtain the training set and the test set.
4. The precision testing method of claim 2, wherein the initial model comprises, in order: the device comprises a first convolution layer, a first maximum pooling layer, a second convolution layer, a second maximum pooling layer, a first full-connection layer and a second full-connection layer, wherein the second full-connection layer adopts a Sigmoid function as an activation function.
5. The precise test method according to claim 1, wherein the step of performing test case coding prediction according to the code library, the to-be-tested application version identifier, the target reference application version identifier and the target test case coding prediction model to obtain a target test case coding data set includes:
according to the code base and the target reference application version identification, difference code acquisition is carried out on the to-be-processed application version identification to obtain a to-be-processed difference code;
extracting a feature vector according to the difference code to be processed to obtain a feature vector to be predicted;
and inputting the feature vector to be predicted into the target test case coding prediction model to perform test case coding prediction, so as to obtain the target test case coding data set.
6. The precision testing method according to claim 5, wherein the step of extracting the feature vector according to the difference code to be processed to obtain the feature vector to be predicted comprises:
acquiring a class path keyword, and acquiring a class path identifier from the difference code to be processed according to the class path keyword to obtain a class path identifier set to be processed;
respectively carrying out feature coding, vectorization processing and normalization processing according to the class path identifier set to be processed to obtain class path feature vectors;
acquiring class name keywords, and acquiring class names from the difference codes to be processed according to the class name keywords to obtain a class name set to be processed;
respectively carrying out feature coding, vectorization processing and normalization processing according to the class name set to be processed to obtain class name feature vectors;
acquiring an interface name keyword, and acquiring an interface name from the difference code to be processed according to the interface name keyword to obtain an interface name set to be processed;
respectively carrying out feature coding, vectorization processing and normalization processing according to the interface name set to be processed to obtain an interface name feature vector;
acquiring a function name keyword, and acquiring a function name from the to-be-processed difference code according to the function name keyword to obtain a to-be-processed function name set;
respectively carrying out feature coding, vectorization processing and normalization processing according to the function name set to be processed to obtain function name feature vectors;
acquiring abstract syntax tree keywords, and acquiring abstract syntax tree identifications from the difference codes to be processed according to the abstract syntax tree keywords to obtain an abstract syntax tree identification set to be processed;
respectively carrying out feature coding, vectorization processing and normalization processing according to the abstract syntax tree identification set to be processed to obtain abstract syntax tree feature vectors;
and performing matrix splicing according to the class path feature vector, the class name feature vector, the interface name feature vector, the function name feature vector and the abstract syntax tree feature vector to obtain the feature vector to be predicted.
7. The method according to claim 1, wherein the step of performing the precise test according to the target test case identifier set, the test case set of the test case library, the code library, the target reference application version identifier and the application version identifier to be tested to obtain a precise test result comprises:
according to the target test case identification set, test cases are obtained from the test case set of the test case library to obtain a test case set to be tested;
acquiring a difference code from the code library according to the target reference application version identification and the application version identification to be detected to obtain a target difference code;
and carrying out accurate test on the target difference code according to the test case set to be tested to obtain the accurate test result.
8. An accurate testing device, the device comprising:
the device comprises a request acquisition module, a target standard application version identification and a test result generation module, wherein the request acquisition module is used for acquiring a precise test request which carries the application version identification to be tested and the target standard application version identification;
the target test case code prediction model determining module is used for acquiring a test case code prediction model library, and performing test case code prediction model matching in the test case code prediction model library according to the target reference application version identification to obtain a target test case code prediction model;
the target test case coding data set determining module is used for acquiring a code base and carrying out test case coding prediction according to the code base, the application version identification to be tested, the target reference application version identification and the target test case coding prediction model to obtain a target test case coding data set;
the target test case identification set determining module is used for acquiring a test case library, and determining the test case identification according to the target test case coding data set and the mapping list of the test case library to obtain a target test case identification set;
and the accurate test result determining module is used for carrying out accurate test according to the target test case identification set, the test case set of the test case library, the code library, the target reference application version identification and the application version identification to be tested to obtain an accurate test result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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