CN113778894B - Method, device, equipment and storage medium for constructing test cases - Google Patents
Method, device, equipment and storage medium for constructing test cases Download PDFInfo
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- CN113778894B CN113778894B CN202111095909.6A CN202111095909A CN113778894B CN 113778894 B CN113778894 B CN 113778894B CN 202111095909 A CN202111095909 A CN 202111095909A CN 113778894 B CN113778894 B CN 113778894B
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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
- G06F11/3676—Test management for coverage analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
Abstract
The invention relates to the field of artificial intelligence and discloses a method, a device, equipment and a storage medium for constructing test cases. The method comprises the following steps: according to the type of the product to be tested, a plurality of test standard labels are obtained from a test standard database, and classified to obtain a plurality of industry keywords; generating at least one business keyword according to the original data of the product to be tested; acquiring a plurality of initial test cases from a test standard database according to the industry keywords and the business keywords, and carrying out test standard detection on each initial test case to obtain a plurality of candidate test cases; normalizing the original test case and the candidate test case to construct a target training data set; and calling a preset language model, and predicting the target test case based on the test standard label. The method combines the standard test cases of the machine learning predictive product, thereby improving the coverage rate of the test scene and further improving the accuracy of the test.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method, an apparatus, a device, and a storage medium for constructing a test case.
Background
The construction of the test cases is an important technical means for realizing automatic test, and a test engineer usually adopts methods such as an equivalent class division method, a boundary value analysis method and the like to design the test cases according to the service scene of the product and tests the product based on the test cases, so that the test coverage of various possible abnormal service scenes is realized, and the quality of the software product is improved.
According to the existing test case construction method, test cases are designed manually according to the business scene of a product, and partial abnormal scenes may not be covered in the test cases, so that the accuracy of test results is low.
Disclosure of Invention
The invention mainly aims to solve the problem of low accuracy of the construction method of the existing test case.
The first aspect of the present invention provides a method for constructing a test case, including:
according to the type of a product to be tested, a plurality of test standard labels are obtained from a preset test standard library, and the test standard labels are classified according to preset industry categories to obtain a plurality of industry keywords, wherein each category of test standard labels corresponds to one industry keyword;
generating a service keyword set according to the original data of the product to be tested, wherein the original data of the product to be tested comprises demand data and a plurality of original test cases, and the service keyword set comprises at least one service keyword;
Acquiring a plurality of initial test cases from the test standard library according to the industry keywords and the business keywords in the business keyword set, and performing test standard detection on each initial test case to obtain a plurality of candidate test cases, wherein the candidate test cases are initial test cases which are qualified in detection;
constructing an initial training data set based on the original test cases and the candidate test cases, and carrying out normalization processing on each test case in the initial training data set to obtain a target training data set;
and calling a preset language model, and predicting target test cases contained in the target training data set based on the test standard labels to obtain a plurality of target test cases, wherein the matching probability between the target test cases and the test standard labels is larger than a preset threshold value.
Optionally, in a first implementation manner of the first aspect of the present invention, generating the service keyword according to the raw data of the product to be tested includes:
based on a preset word segmentation tool, carrying out word segmentation on the original data of the product to be tested to obtain target data containing a plurality of words;
Performing word segmentation statistics on the target data containing a plurality of words to obtain word segmentation frequency distribution, wherein the word segmentation frequency distribution comprises an initial frequency value corresponding to each word segment, and the initial frequency value is used for the occurrence frequency of each word segment in the target data;
and determining service keywords according to the word segmentation frequency distribution, wherein the service keywords are words corresponding to target frequency values, and the target frequency values are larger than a preset threshold value.
Optionally, in a second implementation manner of the first aspect of the present invention, after generating the service keyword set according to the raw data of the product to be tested, the method further includes:
receiving an approval instruction input by a user, identifying the business keywords in the business keyword set according to the approval instruction, and determining target business keywords qualified for approval according to the identification result;
processing the target business keywords based on a preset keyword expansion tool to obtain expanded business keywords, and adding the expanded business keywords into the business keyword set.
Optionally, in a third implementation manner of the first aspect of the present invention, the performing test standard detection on each initial test case to obtain a plurality of candidate test cases includes:
Classifying the initial test cases according to the industry keywords to obtain a plurality of test rules, wherein each test rule comprises a plurality of initial test cases, and each test rule corresponds to at least one industry keyword;
and verifying the test cases in each test rule based on the test standard content corresponding to the test standard label, and determining a plurality of candidate test cases according to the verification result.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the calling a preset language model, and predicting, based on the test standard label, the target test cases included in the target training data set, where obtaining a plurality of target test cases includes:
calling a multi-layer translation network in a preset prediction model, and coding each test case in the target training data set to obtain an effective word vector corresponding to each training data;
calculating the matching probability between each test case in the target training data set and the test standard label based on the effective word vector corresponding to each training data to obtain multi-classification matching probability distribution;
and screening test cases with matching probability larger than a preset threshold value from the multi-classification matching probability distribution to obtain target test cases.
Optionally, in a fifth implementation manner of the first aspect of the present invention, after encoding each test case in the target training data set by using the multi-layer translation network in the preset prediction model to obtain an effective word vector corresponding to each piece of training data, calculating a matching probability between each test case in the target training data set and the test standard label based on the effective word vector corresponding to each piece of training data, and before obtaining a multi-class matching probability distribution, the method further includes:
and calling an embedded layer network in the prediction model, and convolving the effective word vector corresponding to each piece of training data to obtain a low-dimensional and dense text vector.
Optionally, in a sixth implementation manner of the first aspect of the present invention, after calculating a matching probability between each test case in the target training data set and the test standard label based on the valid word vector corresponding to each training data to obtain a multi-classification matching probability distribution, before screening test cases with a matching probability greater than a preset threshold from the multi-classification matching probability distribution to obtain a target test case, the method further includes:
Calculating a loss value corresponding to the multi-classification matching probability distribution according to a preset loss function;
and updating the multi-classification matching probability distribution based on the loss value corresponding to the multi-classification matching probability distribution.
The second aspect of the present invention provides a test case building apparatus, including:
the system comprises an industry keyword generation module, a test standard label generation module and a test standard label generation module, wherein the industry keyword generation module is used for acquiring a plurality of test standard labels from a preset test standard library according to the type of a product to be tested, and classifying the test standard labels according to preset industry categories to obtain a plurality of industry keywords, wherein each category of test standard labels corresponds to one industry keyword;
the business keyword generation module is used for generating business keywords according to the original data of the product to be tested, wherein the original data of the product to be tested comprises demand data and a plurality of original test cases;
the standard detection module is used for acquiring a plurality of initial test cases from the test standard library according to the industry keywords and the business keywords, and carrying out test standard detection on each initial test case to obtain a plurality of candidate test cases, wherein the candidate test cases are initial test cases which are qualified in detection;
The data preprocessing module is used for constructing an initial training data set based on the original test cases and the candidate test cases, and carrying out normalization processing on each test case in the initial training data set to obtain a target training data set;
the model prediction module is used for calling a preset language model, and predicting target test cases contained in the target training data set based on the test standard labels to obtain a plurality of target test cases, wherein the matching probability between the target test cases and the test standard labels is larger than a preset threshold value.
Optionally, in a first implementation manner of the second aspect of the present invention, the service keyword generating module specifically includes:
the word segmentation unit is used for carrying out word segmentation on the original data of the product to be tested based on a preset word segmentation tool to obtain target data containing a plurality of words;
the statistics unit is used for carrying out word segmentation statistics on the target data containing the plurality of words to obtain word segmentation frequency distribution, wherein the word segmentation frequency distribution comprises an initial frequency value corresponding to each word segment, and the initial frequency value is used for representing the occurrence frequency of each word segment in the target data;
The construction unit is used for determining at least one business keyword according to the word segmentation frequency distribution, and constructing a business keyword set according to the business keyword, wherein the business keyword is a word segmentation corresponding to an initial frequency value larger than a preset threshold value.
Optionally, in a second implementation manner of the second aspect of the present invention, the service keyword generating module specifically includes:
the word segmentation unit is used for carrying out word segmentation on the original data of the product to be tested based on a preset word segmentation tool to obtain target data containing a plurality of words;
the statistics unit is used for carrying out word segmentation statistics on the target data containing the plurality of words to obtain word segmentation frequency distribution, wherein the word segmentation frequency distribution comprises an initial frequency value corresponding to each word segment, and the initial frequency value is used for representing the occurrence frequency of each word segment in the target data;
the construction unit is used for determining at least one business keyword according to the word segmentation frequency distribution and constructing a business keyword set according to the business keyword, wherein the business keyword is a word segmentation corresponding to an initial frequency value larger than a preset threshold value;
The approval unit is used for receiving approval instructions input by a user, identifying each business keyword in the business keyword set according to the approval instructions, and determining target business keywords qualified for approval according to the identification result;
the expansion unit is used for processing the target business keywords based on a preset keyword expansion tool to obtain expanded business keywords, and adding the expanded business keywords into the business keyword set.
Optionally, in a third implementation manner of the second aspect of the present invention, the standard detection module specifically includes:
the acquisition unit is used for acquiring a plurality of initial test cases according to the industry keywords and the business keywords;
the classification unit is used for classifying the initial test cases according to the industry keywords to obtain a plurality of test rules, wherein each test rule comprises a plurality of initial test cases, and each test rule corresponds to at least one industry keyword;
and the verification unit is used for verifying the test cases in each test rule based on the test standard content corresponding to the test standard label, and determining a plurality of candidate test cases according to the verification result.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the model prediction module specifically includes:
the coding unit is used for calling a multi-layer translation network in a preset prediction model, and coding each test case in the target training data set to obtain an effective word vector corresponding to each training data;
the computing unit is used for computing the matching probability between each test case in the target training data set and the test standard label based on the effective word vector corresponding to each training data to obtain multi-classification matching probability distribution;
and the screening unit is used for screening test cases with matching probability larger than a preset threshold value from the multi-classification matching probability distribution to obtain target test cases.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the model prediction module specifically includes:
the coding unit is used for calling a multi-layer translation network in a preset prediction model, and coding each test case in the target training data set to obtain an effective word vector corresponding to each training data;
the convolution unit is used for calling an embedded layer network in the prediction model and convolving the effective word vector corresponding to each piece of training data to obtain a low-dimensional and dense text vector;
The computing unit is used for computing the matching probability between each test case in the target training data set and the test standard label based on the effective word vector corresponding to each training data to obtain multi-classification matching probability distribution;
and the screening unit is used for screening test cases with matching probability larger than a preset threshold value from the multi-classification matching probability distribution to obtain target test cases.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the model prediction module specifically includes:
the coding unit is used for calling a multi-layer translation network in a preset prediction model, and coding each test case in the target training data set to obtain an effective word vector corresponding to each training data;
the computing unit is used for computing the matching probability between each test case in the target training data set and the test standard label based on the effective word vector corresponding to each training data to obtain multi-classification matching probability distribution;
the loss value calculation unit is used for calculating the loss value corresponding to the multi-classification matching probability distribution according to a preset loss function;
the updating unit is used for updating the multi-classification matching probability distribution based on the loss value corresponding to the multi-classification matching probability distribution;
And the screening unit is used for screening test cases with matching probability larger than a preset threshold value from the multi-classification matching probability distribution to obtain target test cases.
A third aspect of the present invention provides a test case building apparatus, including: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the test case build device to execute the test case build method described above.
A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein that, when executed on a computer, cause the computer to perform the method of building test cases described above.
According to the technical scheme provided by the invention, the related test cases are matched through the industry keywords and the business keywords of the product to be tested, so that the test cases which can meet the business requirements and meet the industry test standards are obtained. Meanwhile, the method and the device integrate the original test cases of the product to be tested and the crawled candidate test cases into the standard test cases of the product by combining machine learning, so that the coverage rate of a test scene is improved, and the test accuracy is further improved.
Drawings
FIG. 1 is a schematic diagram of a first embodiment of a method for constructing test cases according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a second embodiment of a method for constructing test cases according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a third embodiment of a method for constructing test cases according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a fourth embodiment of a method for constructing test cases according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a device for constructing test cases according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of another embodiment of a test case constructing apparatus according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an embodiment of a test case building apparatus according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method, a device, equipment and a storage medium for constructing a test case, which have higher test accuracy.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
Embodiments of the present application may acquire and process relevant data based on artificial intelligence techniques. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
For easy understanding, a specific flow of an embodiment of the present invention is described below, referring to fig. 1, and one embodiment of a method for constructing a test case in an embodiment of the present invention includes:
101. obtaining a plurality of test standard labels from a preset test standard library according to the type of a product to be tested, and classifying the test standard labels according to preset industry categories to obtain a plurality of industry keywords, wherein each category of test standard labels corresponds to one industry keyword;
it can be understood that the test standard database is a database corresponding to a technical forum or a resource station authenticated by a test industry, and the test standard database includes test standards (i.e. test standard labels) corresponding to different industries, such as capacity test, network test, DIFF test, and the like of service class software, compatibility test of App software, and the like. The server firstly searches corresponding test standard labels, such as compatibility test, page abnormality test, load test and the like, according to the type (such as App test) of the product to be tested, and then crawls the corresponding test standard labels according to the corresponding good score, source and other dimensions, such as only crawling the test standard labels with the good score of 60 percent and 80 percent. After crawling a plurality of test standard labels corresponding to products to be tested, the server classifies the test standard labels according to different test industry categories to obtain a plurality of industry keywords, such as abnormal tests, network tests and the like.
102. Generating a service keyword set according to the original data of the product to be tested, wherein the original data of the product to be tested comprises demand data and a plurality of original test cases, and the service keyword set comprises at least one service keyword;
it will be appreciated that the raw data of the product to be tested includes its product demand information and test cases designed by the test engineer according to preset test rules (e.g., equivalence class classification, boundary value classification). The server takes the word with the highest occurrence frequency in the original data as a business keyword, for example, the business keyword is "payment".
103. Acquiring a plurality of initial test cases from a test standard library according to industry keywords and business keywords in a business keyword set, and performing test standard detection on each initial test case to obtain a plurality of candidate test cases, wherein the candidate test cases are initial test cases with qualified detection;
it can be understood that the test standard database further includes initial test cases corresponding to different industries, the quality of the test cases used in the test determines the final test effect, and the initial test cases in the test standard database are test cases designed strictly according to the test standard (including the data used in the test, the test environment and the test rule), and have good quality and are accepted in the industry. The server takes the industry keywords and the business keywords as data matching conditions, acquires corresponding initial test cases from the test standard database through corresponding database query sentences, detects each acquired initial test case, judges whether the initial test cases accord with the test industry standard (namely, the content corresponding to the test standard label), and takes the initial test cases which accord with the test industry standard as candidate test cases.
104. Constructing an initial training data set based on the original test cases and the candidate test cases, and carrying out normalization processing on each test case in the initial training data set to obtain a target training data set;
it can be appreciated that the initial training data set includes a plurality of original test cases and a plurality of candidate test cases, where the original test cases are test cases designed by a test engineer according to a current product to be tested, and the candidate test cases are test cases meeting test industry standards and business requirements. In this embodiment, an engineering function is used to perform normalization processing on the test case, for example, a mapmin max function in matlab (mathematical software) is used to perform maximum and minimum normalization processing on data, and the data in the test case are mapped into [ -1,1] intervals, so that the degree of dispersion between the data is reduced, and the calculation amount during model processing is reduced.
105. And calling a preset language model, and predicting target test cases contained in the target training data set based on the test standard labels to obtain a plurality of target test cases, wherein the matching probability between the target test cases and the test standard labels is larger than a preset threshold value.
It can be understood that the language model may be a Bert model, an N-gram model, etc., after initializing the language model by the server, inputting the test standard label and the target training data set into the language model, firstly extracting features of the input data through an input layer of the language model to obtain corresponding feature vectors, secondly, performing function activation on the feature vectors through an implicit layer of the language model, and finally, calculating the matching degree between each test case in the target training data set and the test standard label through an output layer of the language model, and outputting the test cases with the matching degree greater than a preset threshold value, thereby obtaining the universal test cases with higher scene coverage.
In the embodiment, the related test cases are matched through the industry keywords and the business keywords of the product to be tested, so that the test cases which can meet the business requirements and meet the industry test standards are obtained. Meanwhile, the method and the device integrate the original test cases of the product to be tested and the crawled candidate test cases into the standard test cases of the product by combining machine learning, so that the coverage rate of a test scene is improved, and the test accuracy is further improved.
Referring to fig. 2, a second embodiment of a method for constructing a test case according to an embodiment of the present invention includes:
201. obtaining a plurality of test standard labels from a preset test standard library according to the type of a product to be tested, and classifying the test standard labels according to preset industry categories to obtain a plurality of industry keywords, wherein each category of test standard labels corresponds to one industry keyword;
step 201 is similar to the above-mentioned step 101, and is not repeated here.
202. Performing word segmentation processing on the original data of the product to be tested based on a preset word segmentation tool to obtain target data containing a plurality of words, wherein the original data of the product to be tested comprises demand data and a plurality of original test cases;
it will be appreciated that the word segmentation tool may be, but is not limited to, jieba, snowNLP, where the server inputs the original data into the word segmentation tool for processing, so as to divide each sentence into a plurality of different words, for example, "in the case of weak net," WeChat Payment failure "is divided into" in the case of |of weak net| |WeChat Payment|failure ", and" in the middle, "each word is divided.
203. Performing word segmentation statistics on target data containing a plurality of words to obtain word segmentation frequency distribution, wherein the word segmentation frequency distribution comprises an initial frequency value corresponding to each word segment, and the initial frequency value is used for representing the occurrence frequency of each word segment in the target data;
it can be understood that the word segmentation frequency distribution, that is, the number of times (initial frequency value) of occurrence of each word segment in the original data after the word segmentation processing is counted, for example, the word segmentation frequency distribution t= [ WeChat: 10, payment: 65, weak network: 3].
204. Determining at least one business keyword according to word segmentation frequency distribution, and constructing a business keyword set according to the business keyword, wherein the business keyword is a word segmentation corresponding to an initial frequency value larger than a preset threshold;
it can be understood that the server determines, as the service keyword, the word having the initial frequency value greater than the preset threshold in the word segmentation frequency distribution, for example, the preset threshold is 30, and the word segmentation frequency distribution t= [ WeChat: 10, payment: 65, weak network: 3], the business keyword is "payment".
205. Acquiring a plurality of initial test cases from a test standard library according to industry keywords and business keywords in a business keyword set, and performing test standard detection on each initial test case to obtain a plurality of candidate test cases, wherein the candidate test cases are initial test cases with qualified detection;
206. Constructing an initial training data set based on the original test cases and the candidate test cases, and carrying out normalization processing on each test case in the initial training data set to obtain a target training data set;
207. and calling a preset language model, and predicting target test cases contained in the target training data set based on the test standard labels to obtain a plurality of target test cases, wherein the matching probability between the target test cases and the test standard labels is larger than a preset threshold value.
Steps 205-207 are similar to the steps 103-105, and are not repeated here.
In this embodiment, the process of generating the service keywords is described in detail, and the service keywords are accurately obtained by counting the occurrence frequency of each word after word segmentation is performed on the original data and using the word segment with the highest occurrence frequency as the service keyword.
Referring to fig. 3, a third embodiment of a method for constructing a test case according to an embodiment of the present invention includes:
301. obtaining a plurality of test standard labels from a preset test standard library according to the type of a product to be tested, and classifying the test standard labels according to preset industry categories to obtain a plurality of industry keywords, wherein each category of test standard labels corresponds to one industry keyword;
302. Generating a service keyword set according to the original data of the product to be tested, wherein the original data of the product to be tested comprises demand data and a plurality of original test cases, and the service keyword set comprises at least one service keyword;
303. acquiring a plurality of initial test cases from a test standard library according to industry keywords and service keywords in a service keyword set, and classifying the initial test cases according to the industry keywords to obtain a plurality of test rules, wherein each test rule comprises a plurality of initial test cases, and each test rule corresponds to at least one industry keyword;
it can be appreciated that each initial test case includes a test target, a test environment, input data, a test step, an expected result, a test script, etc., and one test rule corresponds to a plurality of test cases, that is, includes different test targets, different test environments, different input data, etc.; and each test rule corresponds to at least one industry keyword, for example, when one test case in the network test rule is ' weak net ', weChat payment fails ', the corresponding business keyword is ' WeChat payment ', and the industry keyword is ' weak net '.
304. Based on the test standard content corresponding to the test standard label, checking the test cases in each test rule, and determining a plurality of candidate test cases according to the check result, wherein the candidate test cases are initial test cases which are qualified in detection;
it can be understood that the test standard content is a real test result, and the test case in each test rule is a hypothetical test result, the server judges the correctness of the hypothetical result according to the real result, if the description in the test case accords with the real result (correct), the corresponding data identifier is added to the test case in the database, the server obtains the test case containing the data identifier, and determines the test case as a candidate test case.
305. Constructing an initial training data set based on the original test cases and the candidate test cases, and carrying out normalization processing on each test case in the initial training data set to obtain a target training data set;
306. and calling a preset language model, and predicting target test cases contained in the target training data set based on the test standard labels to obtain a plurality of target test cases, wherein the matching probability between the target test cases and the test standard labels is larger than a preset threshold value.
Step 306 is similar to the above-mentioned step 104, and detailed description thereof is omitted herein.
In this embodiment, a process of testing standard detection is described in detail, and a plurality of test cases are obtained from a testing standard library, classified into a plurality of testing rules, and whether each testing rule accords with an industry testing standard is checked, so that the test cases which accord with the industry testing standard are obtained.
Referring to fig. 4, a fourth embodiment of a method for constructing a test case according to an embodiment of the present invention includes:
401. obtaining a plurality of test standard labels from a preset test standard library according to the type of a product to be tested, and classifying the test standard labels according to preset industry categories to obtain a plurality of industry keywords, wherein each category of test standard labels corresponds to one industry keyword;
402. generating a service keyword set according to the original data of the product to be tested, wherein the original data of the product to be tested comprises demand data and a plurality of original test cases, and the service keyword set comprises at least one service keyword;
403. acquiring a plurality of initial test cases from a test standard library according to industry keywords and business keywords in a business keyword set, and performing test standard detection on each initial test case to obtain a plurality of candidate test cases, wherein the candidate test cases are initial test cases with qualified detection;
404. Constructing an initial training data set based on the original test cases and the candidate test cases, and carrying out normalization processing on each test case in the initial training data set to obtain a target training data set;
405. calling a multi-layer translation network in a preset prediction model, and coding each test case in the target training data set to obtain an effective word vector corresponding to each training data;
the server encodes each test case in the target training data set by calling the multi-layer translation network of the language model, wherein each layer of translation network comprises a multi-head self-attention sub-network and a feedforward sub-network, the relation between words is learned by a self-attention mechanism (self-attention) in the multi-head self-attention sub-network, the text of a sentence is encoded, a corresponding sentence vector is obtained, and then nonlinear change is carried out on the sentence vector through the feedforward sub-network, and the nonlinear change is to introduce more information in the original sentence through a nonlinear function, for example, a logarithmic function, so as to obtain a corresponding effective word vector.
Optionally, the server further invokes an embedded layer network in the prediction model to convolve the valid word vector corresponding to each piece of training data, so as to obtain a low-dimensional and dense text vector. It can be understood that the embedded layer network at least comprises a convolution kernel of 1*1, and the server carries out convolution processing on the effective word vector corresponding to each training data through the embedded layer network, so that the effective word vector is converted from discrete high-dimensional vector representation to low-dimensional dense vector, and further, the calculated amount of the model is reduced, and the processing speed is improved.
406. Calculating the matching probability between each test case in the target training data set and the test standard label based on the effective word vector corresponding to each training data to obtain multi-classification matching probability distribution;
it can be understood that the server inputs the valid word vector corresponding to each training data into the fully connected network of the language model, sums the valid word vectors of each test case in one linear layer in the network, calculates an average value to obtain a corresponding average vector, and processes the average vector corresponding to each test case based on a preset multi-classifier (such as softmax), so as to obtain a corresponding initial multi-classification matching probability distribution, namely, a matching probability used for representing each test case and a test standard label.
407. Calculating a loss value corresponding to the multi-classification matching probability distribution according to a preset loss function, and updating the multi-classification matching probability distribution based on the loss value corresponding to the multi-classification matching probability distribution;
it will be appreciated that the loss function may employ an L1 loss, with a loss value being used to represent the deviation of the actual result from the ideal result, when the loss value is smaller, i.e. the actual result is closer to the ideal result. Further, the server adopts a random gradient descent algorithm to carry out iterative updating on the network parameters of the language model, and after each network parameter updating, the corresponding initial multi-classification matching probability distribution and the corresponding loss value are calculated again until the loss value is smaller than a preset threshold value, the convergence of the current language model is determined, the calculated initial multi-classification matching probability distribution is taken as a target multi-classification matching probability distribution, and test cases with the matching probability larger than the preset threshold value in the target multi-classification matching probability distribution are output, so that the universal test cases with higher scene coverage rate are obtained.
408. And screening test cases with matching probability larger than a preset threshold value from the multi-classification matching probability distribution to obtain target test cases, wherein the matching probability between the target test cases and the test standard labels is larger than the preset threshold value.
It can be understood that the preset threshold value can be determined by actual service requirements, wherein the target test case not only meets the service requirements, but also meets the test cases of the test standard, and the test scene is fully covered, so that the product quality is improved.
In this embodiment, the process of predicting the target test case by the model is described in detail, the training samples are encoded, so that the features are extracted, the features are classified based on the features and the matching probability of each class, the test case with the largest matching probability is output, and the accuracy of prediction is further improved by performing quantization calculation on unstructured text data.
The method for constructing the test case in the embodiment of the present invention is described above, and the apparatus for constructing the test case in the embodiment of the present invention is described below, referring to fig. 5, where an embodiment of the apparatus for constructing the test case in the embodiment of the present invention includes:
the industry keyword generation module 501 is configured to obtain a plurality of test standard tags from a preset test standard library according to a type of a product to be tested, and classify the test standard tags according to a preset industry category to obtain a plurality of industry keywords, where each category of test standard tag corresponds to one industry keyword;
The service keyword generation module 502 is configured to generate a service keyword set according to the original data of the product to be tested, where the original data of the product to be tested includes demand data and a plurality of original test cases, and the service keyword set includes at least one service keyword;
the test standard detection module 503 is configured to obtain a plurality of initial test cases from the test standard library according to the industry keywords and the service keywords in the service keyword set, and perform test standard detection on each initial test case to obtain a plurality of candidate test cases, where the candidate test cases are initial test cases that are qualified in detection;
the training data set construction module 504 is configured to construct an initial training data set based on the original test case and the candidate test case, and normalize each test case in the initial training data set to obtain a target training data set;
the model prediction module 505 is configured to invoke a preset language model, and predict target test cases included in the target training data set based on the test standard label, so as to obtain a plurality of target test cases, where a matching probability between the target test cases and the test standard label is greater than a preset threshold.
In the embodiment, the related test cases are matched through the industry keywords and the business keywords of the product to be tested, so that the test cases which can meet the business requirements and meet the industry test standards are obtained. Meanwhile, the method and the device integrate the original test cases of the product to be tested and the crawled candidate test cases into the standard test cases of the product by combining machine learning, so that the coverage rate of a test scene is improved, and the test accuracy is further improved.
Referring to fig. 6, another embodiment of a test case constructing apparatus according to an embodiment of the present invention includes:
the industry keyword generation module 501 is configured to obtain a plurality of test standard tags from a preset test standard library according to a type of a product to be tested, and classify the test standard tags according to a preset industry category to obtain a plurality of industry keywords, where each category of test standard tag corresponds to one industry keyword;
the service keyword generation module 502 is configured to generate a service keyword set according to the original data of the product to be tested, where the original data of the product to be tested includes demand data and a plurality of original test cases, and the service keyword set includes at least one service keyword;
The test standard detection module 503 is configured to obtain a plurality of initial test cases from the test standard library according to the industry keywords and the service keywords in the service keyword set, and perform test standard detection on each initial test case to obtain a plurality of candidate test cases, where the candidate test cases are initial test cases that are qualified in detection;
the training data set construction module 504 is configured to construct an initial training data set based on the original test case and the candidate test case, and normalize each test case in the initial training data set to obtain a target training data set;
the model prediction module 505 is configured to invoke a preset language model, and predict target test cases included in the target training data set based on the test standard label, so as to obtain a plurality of target test cases, where a matching probability between the target test cases and the test standard label is greater than a preset threshold.
The business keyword generating module 502 specifically includes:
the word segmentation unit 5021 is used for performing word segmentation on the original data of the product to be tested based on a preset word segmentation tool to obtain target data containing a plurality of words;
A statistics unit 5022, configured to perform word segmentation statistics on the target data including the plurality of words to obtain word segmentation frequency distribution, where the word segmentation frequency distribution includes an initial frequency value corresponding to each word segment, and the initial frequency value is used to represent the occurrence number of each word segment in the target data;
the construction unit 5023 is configured to determine at least one service keyword according to the word segmentation frequency distribution, and construct a service keyword set according to the service keyword, where the service keyword is a word segment corresponding to an initial frequency value greater than a preset threshold.
The test standard detection module 503 specifically includes:
an obtaining unit 5031, configured to obtain a plurality of initial test cases according to the industry keyword and the service keyword;
the classifying unit 5032 is configured to classify the initial test cases according to the industry keywords, so as to obtain a plurality of test rules, where each test rule includes a plurality of initial test cases, and each test rule corresponds to at least one industry keyword;
and the verification unit 5033 is configured to verify the test cases in each test rule based on the test standard content corresponding to the test standard label, and determine a plurality of candidate test cases according to the verification result.
The model prediction module 505 specifically includes:
the coding unit 5051 is configured to invoke a multi-layer translation network in a preset prediction model, and code each test case in the target training data set to obtain an effective word vector corresponding to each training data set;
the convolution unit 5052 is configured to invoke an embedded layer network in the prediction model, and convolve an effective word vector corresponding to each piece of training data to obtain a low-dimensional and dense text vector;
a calculating unit 5053, configured to calculate a matching probability between each test case in the target training data set and the test standard label based on the valid word vector corresponding to each training data, so as to obtain a multi-classification matching probability distribution;
and a screening unit 5054, configured to screen test cases with matching probabilities greater than a preset threshold from the multi-classification matching probability distribution, so as to obtain a target test case.
In the embodiment of the invention, the modularized design enables the hardware of each part of the test case constructing device to concentrate on the realization of a certain function, the performance of the hardware is realized to the maximum extent, and meanwhile, the modularized design also reduces the coupling among the modules of the device, thereby being more convenient for maintenance.
The above-described construction device of the test case in the embodiment of the present invention is described in detail from the point of view of the modularized functional entity in fig. 5 and 6, and the following describes the construction device of the test case in the embodiment of the present invention in detail from the point of view of hardware processing.
Fig. 7 is a schematic structural diagram of a construction device for a test case according to an embodiment of the present invention, where the construction device 700 for the test case may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (e.g., one or more processors) and a memory 720, and one or more storage mediums 730 (e.g., one or more mass storage devices) storing application programs 733 or data 732. Wherein memory 720 and storage medium 730 may be transitory or persistent. The program stored in the storage medium 730 may include one or more modules (not shown), each of which may include a series of instruction operations in the build device 700 of the test case. Still further, the processor 710 may be configured to communicate with the storage medium 730 and execute a series of instruction operations in the storage medium 730 on the build device 700 of the test case.
The test case build device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input/output interfaces 760, and/or one or more operating systems 731, such as Windows Serve, mac OS X, unix, linux, freeBSD, and the like. Those skilled in the art will appreciate that the build device structure of the test case illustrated in FIG. 7 does not constitute a limitation of the build device of the test case, and may include more or fewer components than illustrated, or may combine certain components, or may be a different arrangement of components.
The invention also provides a test case construction device, which comprises a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the test case construction method in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, or may be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, when the instructions run on a computer, cause the computer to execute the steps of the method for constructing a test case.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The method for constructing the test cases is characterized by comprising the following steps of:
according to the type of a product to be tested, a plurality of test standard labels are obtained from a preset test standard library, and the test standard labels are classified according to preset industry categories to obtain a plurality of industry keywords, wherein each category of test standard labels corresponds to one industry keyword;
generating a service keyword set according to the original data of the product to be tested, wherein the original data of the product to be tested comprises demand data and a plurality of original test cases, and the service keyword set comprises at least one service keyword;
Acquiring a plurality of initial test cases from the test standard library according to the industry keywords and the business keywords in the business keyword set, and performing test standard detection on each initial test case to obtain a plurality of candidate test cases, wherein the candidate test cases are initial test cases which are qualified in detection;
constructing an initial training data set based on the original test cases and the candidate test cases, and carrying out normalization processing on each test case in the initial training data set to obtain a target training data set;
and calling a preset language model, and predicting target test cases contained in the target training data set based on the test standard labels to obtain a plurality of target test cases, wherein the matching probability between the target test cases and the test standard labels is larger than a preset threshold value.
2. The method for building test cases according to claim 1, wherein the generating a service keyword set according to the raw data of the product to be tested includes:
based on a preset word segmentation tool, carrying out word segmentation on the original data of the product to be tested to obtain target data containing a plurality of words;
Performing word segmentation statistics on the target data containing a plurality of words to obtain word segmentation frequency distribution, wherein the word segmentation frequency distribution comprises an initial frequency value corresponding to each word segment, and the initial frequency value is used for representing the occurrence frequency of each word segment in the target data;
and determining at least one business keyword according to the word segmentation frequency distribution, and constructing a business keyword set according to the business keyword, wherein the business keyword is a word segmentation corresponding to an initial frequency value larger than a preset threshold.
3. The method for building test cases according to claim 2, further comprising, after the generating a service keyword set according to the raw data of the product to be tested:
receiving an approval instruction input by a user, identifying each business keyword in the business keyword set according to the approval instruction, and determining a target business keyword qualified for approval according to the identification result;
and processing the target business keywords based on a preset keyword expansion tool to obtain expanded business keywords, and adding the expanded business keywords into the business keyword set.
4. The method for constructing test cases according to claim 1, wherein the performing test standard detection on each initial test case to obtain a plurality of candidate test cases includes:
classifying the initial test cases according to the industry keywords to obtain a plurality of test rules, wherein each test rule comprises a plurality of initial test cases, and each test rule corresponds to at least one industry keyword;
and verifying the test cases in each test rule based on the test standard content corresponding to the test standard label, and determining a plurality of candidate test cases according to the verification result.
5. The method for constructing test cases according to claim 1, wherein the calling a preset language model, and predicting the target test cases contained in the target training dataset based on the test standard label, to obtain a plurality of target test cases includes:
calling a multi-layer translation network in a preset prediction model, and coding each test case in the target training data set to obtain an effective word vector corresponding to each training data;
calculating the matching probability between each test case in the target training data set and the test standard label based on the effective word vector corresponding to each training data to obtain multi-classification matching probability distribution;
And screening test cases with matching probability larger than a preset threshold value from the multi-classification matching probability distribution to obtain target test cases.
6. The method for constructing test cases according to claim 5, wherein after coding each test case in the target training data set to obtain an effective word vector corresponding to each training data in the multi-layer translation network in the call preset prediction model, calculating a matching probability between each test case in the target training data set and the test standard label based on the effective word vector corresponding to each training data, and before obtaining a multi-class matching probability distribution, further comprises:
and calling an embedded layer network in the prediction model, and convolving the effective word vector corresponding to each piece of training data to obtain a low-dimensional and dense text vector.
7. The method for building test cases according to claim 6, wherein after calculating a matching probability between each test case in the target training data set and the test standard label based on the valid word vector corresponding to each training data to obtain a multi-classification matching probability distribution, before screening test cases with matching probabilities greater than a preset threshold from the multi-classification matching probability distribution to obtain a target test case, further comprises:
Calculating a loss value corresponding to the multi-classification matching probability distribution according to a preset loss function;
and updating the multi-classification matching probability distribution based on the loss value corresponding to the multi-classification matching probability distribution.
8. The device for constructing the test cases is characterized by comprising the following components:
the system comprises an industry keyword generation module, a test standard label generation module and a test standard label generation module, wherein the industry keyword generation module is used for acquiring a plurality of test standard labels from a preset test standard library according to the type of a product to be tested, and classifying the test standard labels according to preset industry categories to obtain a plurality of industry keywords, wherein each category of test standard labels corresponds to one industry keyword;
the business keyword generation module is used for generating a business keyword set according to the original data of the product to be tested, wherein the original data of the product to be tested comprises demand data and a plurality of original test cases, and the business keyword set comprises at least one business keyword;
the test standard detection module is used for acquiring a plurality of initial test cases from the test standard library according to the industry keywords and the business keywords in the business keyword set, and carrying out test standard detection on each initial test case to obtain a plurality of candidate test cases, wherein the candidate test cases are initial test cases which are qualified in detection;
The training data set construction module is used for constructing an initial training data set based on the original test cases and the candidate test cases, and carrying out normalization processing on each test case in the initial training data set to obtain a target training data set;
the model prediction module is used for calling a preset language model, and predicting target test cases contained in the target training data set based on the test standard labels to obtain a plurality of target test cases, wherein the matching probability between the target test cases and the test standard labels is larger than a preset threshold value.
9. A test case building apparatus, characterized in that the test case building apparatus includes: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the test case build device to perform the test case build method of any of claims 1-7.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor implement the method of building test cases according to any of claims 1-7.
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| CN115828638B (en) * | 2023-01-09 | 2023-05-23 | 西安深信科创信息技术有限公司 | Automatic driving test scene script generation method and device and electronic equipment |
| CN117271373B (en) * | 2023-11-21 | 2024-03-01 | 太平金融科技服务(上海)有限公司深圳分公司 | Automatic construction method and device for test cases, electronic equipment and storage medium |
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