CN110321291A - Test cases intelligent extraction system and method - Google Patents
Test cases intelligent extraction system and method Download PDFInfo
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- 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 present invention provides a kind of test cases intelligent extraction system and methods.The system comprises: information module is for storing various information；Policy module is according to the information stored in information module, test cases and program, problem, demand and the model of user feedback are established respectively, test cases, which is generated, using depth collaborative filtering and recurrent neural network recommends sequence, it determines the similarity of each test cases, generates test cases recommendation results；Data module screens test cases and is sorted according to the essential information and its similarity of test cases, determines consequently recommended test cases；Display module shows consequently recommended test cases, receives field feedback.The present invention can sufficiently promote the effect in test cases library, promote the accumulation and multiplexing of test assets, and tester is helped to carry out Test Case Design, reduce Test Case Design and omit, while reducing the time of tester's screening test case.
Description
Technical field
The present invention relates to computer software technical field, espespecially a kind of test cases intelligent extraction system and method.
Background technique
Test cases is one group of test input, execution condition and the expected results worked out for some special target,
To test some Program path or to verify whether meet some particular demands.The importance of test cases is mainly reflected in following
Several aspects: test cases constitutes design and formulates the basis of test process；The depth of test and the quantity of test cases at
Ratio can more accurately estimate the arrangement of time in test period in each stage according to comprehensive and refinement test cases；Test is set
The type and required resource of meter and exploitation are mainly all controlled by test cases.
Test cases in content design of the tester according to project or the test cases library from storage mainly by selecting
It selects existing test cases and does new data combination.Due to tester the contents of a project are not known about or to storage test cases it is not yet done
It knows, is easy to cause test cases to omit, matches the generation of situations such as deficiency with project demands, and the test cases assets of storage
It is unable to get sufficient utilization.On the other hand, Test Case Design omission is easy to cause the programing change after project delivery to obtain not
To fully testing.Currently, lacking a kind of method for carrying out test cases extraction according to the contents of a project and existing test cases library
And system.
Summary of the invention
To solve the above-mentioned problems, the embodiment of the present invention provides a kind of test cases intelligent extraction system, the system packet
It includes: information module, policy module, data module and display module；
The information module is for storing test cases library, system program node flow chart, system testing problem base, project
Appellative function description information and user's test cases housing choice behavior information；
The policy module according to the information stored in the information module, establish respectively test cases and program, problem,
Demand and the model of user feedback, and generate test cases using depth collaborative filtering and recurrent neural network and recommend sequence
Column determine that the test cases recommends the similarity of each test cases in sequence, generate test cases recommendation results；
The data module according to the essential information and its similarity of the test cases in the test cases recommendation results,
Test cases in the test cases recommendation results is screened and sorted, determines consequently recommended test cases；
The display module receives field feedback for showing the consequently recommended test cases.
Optionally, in an embodiment of the present invention, the policy module includes test cases and procedural model unit, is used for
Text classification is carried out to the text description of test cases in test cases library, by the text classification of test cases and system program section
The text description of point flow chart is matched, and is determined test cases and procedure match relationship, is obtained test cases and procedural model.
Optionally, in an embodiment of the present invention, the policy module includes test cases and problem model unit, is used for
Text classification is carried out to the text description of test cases in test cases library, and to test problem in system testing problem base
Text description carries out text classification, and the text classification of test cases is matched with the text classification of test problem, determines and surveys
Case and problem matching relationship are tried, test cases and problem model are obtained.
Optionally, in an embodiment of the present invention, the policy module includes test cases and demand model unit, is used for
Text classification is carried out to the text description of test cases in test cases library, and to software in project demands functional circuit information
The text description of statement of requirements book carries out text classification, by the text of the text classification of test cases and software requirements specification point
Class is matched, and is determined test cases and demand matching relationship, is obtained test cases and demand model.
Optionally, in an embodiment of the present invention, the policy module includes test cases and user feedback model unit,
Text classification, and analysis user's test cases housing choice behavior are carried out for the text description to test cases in test cases library
User feedback behavior in information, obtains user feedback feature, and the text classification of test cases and user feedback feature are carried out
Matching, determines test cases and user feedback characteristic matching relationship, obtains test cases and user feedback model.
Optionally, in an embodiment of the present invention, the policy module includes policy calculation unit, for according to test case
Example and program, problem, demand and the model of user feedback generate test using depth collaborative filtering and recurrent neural network
Case recommends sequence, determines that the test cases recommends the similarity of each test cases in sequence, generates test cases and recommends knot
Fruit.
Optionally, in an embodiment of the present invention, the policy calculation unit is determined according to test cases similarity formula
The test cases recommends the similarity of each test cases in sequence, the test cases similarity formula are as follows:
Wherein,It is time attenuation function, α is adjustment factor, ti、tjRespectively indicate user select test cases i,
The time of test cases j, NiIndicate the number of users of selection test cases i, NjIndicate the number of users of selection test cases j.
Optionally, in an embodiment of the present invention, the data module is also used to utilize recurrence according to programing change information
Neural network is merged and is reset to the test cases recommendation results, and online recommendation results are generated, to the test cases
Test cases in recommendation results and the online recommendation results is screened and is sorted, and determines consequently recommended test cases.
Optionally, in an embodiment of the present invention, the data module recommends the test cases according to sort formula
As a result the test cases in is ranked up, wherein the sort formula are as follows:
Wherein, NuIt is the set of the test cases of user's selection, S (j, k) is the K test most like with test cases j
The set of case, WijIt is the similarity of test cases j and i,The probability of user u selection test cases i, β be weight because
Son, 0≤β≤1.
The embodiment of the present invention also provides a kind of test cases intelligent extract method, and the method utilizes the test cases intelligence
Energy extraction system, carries out test cases intelligent extraction.
The present invention can sufficiently promote the effect in test cases library, promote the accumulation and multiplexing of test assets, help to test
Personnel carry out Test Case Design, reduce Test Case Design and omit, while reducing the time of tester's screening test case.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, embodiment will be described below
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only of the invention some
Embodiment for those of ordinary skill in the art without any creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is a kind of structural schematic diagram of test cases intelligent extraction system of the embodiment of the present invention；
Fig. 2 is the structural schematic diagram of policy module in the embodiment of the present invention；
Fig. 3 is the structural schematic diagram of test cases and procedural model unit in the embodiment of the present invention；
Fig. 4 is the structural schematic diagram of test cases and problem model unit in the embodiment of the present invention:
Fig. 5 is the structural schematic diagram of test cases and demand model unit in the embodiment of the present invention:
Fig. 6 is the schematic diagram of recurrent neural network and depth collaborative filtering in the embodiment of the present invention:
Fig. 7 is to recommend test cases collection product process figure in the embodiment of the present invention.
Specific embodiment
The embodiment of the present invention provides a kind of test cases intelligent extraction system and method.
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
It is as shown in Figure 1 a kind of structural schematic diagram of test cases intelligent extraction system of the embodiment of the present invention, as shown in the figure
System includes: information module 1, policy module 2, data module 3 and display module 4；
The information module 1 is for storing test cases library, system program node flow chart, system testing problem base, item
Mesh appellative function description information and user's test cases housing choice behavior information；
The policy module 2 is established test cases and program respectively, is asked according to the information stored in the information module
Topic, demand and the model of user feedback, and generate test cases using depth collaborative filtering and recurrent neural network and recommend
Sequence determines that the test cases recommends the similarity of each test cases in sequence, generates test cases recommendation results；
The data module 3 is according to the essential information of the test cases in the test cases recommendation results and its similar
Degree, is screened and is sorted to the test cases in the test cases recommendation results, determine consequently recommended test cases；
The display module 4 receives field feedback for showing the consequently recommended test cases.
In the present embodiment, present system joined at natural language on the basis of being based on Collaborative Filtering Recommendation Algorithm
The deep learning method of reason method and recurrent neural network has been effectively combined collaborative filtering method and deep learning method respectively
The advantages of, more accurate test cases is provided for user and is recommended, and improves the efficiency of Test Case Design to a certain extent,
The time cost of Test Case Design is saved.
As an embodiment of the present invention, policy module includes test cases and procedural model unit, for test
The text description of test cases carries out text classification in case library, by the text classification of test cases and system program node process
The text description of figure is matched, and is determined test cases and procedure match relationship, is obtained test cases and procedural model.
As an embodiment of the present invention, policy module includes test cases and problem model unit, for test
The text description of test cases carries out text classification in case library, and retouches to the text of test problem in system testing problem base
Carry out text classification is stated, the text classification of test cases is matched with the text classification of test problem, determines test cases
With problem matching relationship, test cases and problem model are obtained.
As an embodiment of the present invention, policy module includes test cases and demand model unit, for test
The text description of test cases carries out text classification in case library, and says to software requirement in project demands functional circuit information
The text description of bright book carries out text classification, and the text classification of test cases and the text classification of software requirements specification are carried out
Matching, determines test cases and demand matching relationship, obtains test cases and demand model.
As an embodiment of the present invention, policy module includes test cases and user feedback model unit, for pair
The text description of test cases carries out in text classification, and analysis user's test cases housing choice behavior information in test cases library
User feedback behavior, obtain user feedback feature, the text classification of test cases matched with user feedback feature, really
Determine test cases and user feedback characteristic matching relationship, obtains test cases and user feedback model.
As an embodiment of the present invention, policy module includes policy calculation unit, for according to test cases and journey
Sequence, problem, demand and the model of user feedback generate test cases using depth collaborative filtering and recurrent neural network and push away
Sequence is recommended, determines that the test cases recommends the similarity of each test cases in sequence, generates test cases recommendation results.
In the present embodiment, policy calculation unit determines that the test cases is recommended according to test cases similarity formula
The similarity of each test cases in sequence, the test cases similarity formula are as follows:
Wherein,It is time attenuation function, α is adjustment factor, ti、tjRespectively indicate user select test cases i,
The time of test cases j, NiIndicate the number of users of selection test cases i, NjIndicate the number of users of selection test cases j.
As an embodiment of the present invention, data module is also used to utilize recurrent neural net according to programing change information
Network is merged and is reset to the test cases recommendation results, and online recommendation results are generated, and recommends to tie to the test cases
Test cases in fruit and the online recommendation results is screened and is sorted, and determines consequently recommended test cases.
As an embodiment of the present invention, data module is according to sort formula in the test cases recommendation results
Test cases is ranked up, wherein the sort formula are as follows:
Wherein, NuIt is the set of the test cases of user's selection, S (j, k) is the K test most like with test cases j
The set of case, WijIt is the similarity of test cases j and i,The probability of user u selection test cases i, β be weight because
Son, 0≤β≤1.
The present invention overcomes the disadvantages of the prior art, using natural language processing method and based on recurrent neural network
Deep learning method is the system that tester carries out test cases recommendation.The present invention is independent of the experience of tester and soft
The particularity of part system itself can be suitable for universality the test of the software systems of all iterative developments.Pass through natural language
Processing is test cases associated services demand and test problem, thus set up test cases and test problem, test cases with
The correlation model of demand entry, gives full play to the test assets effect of test cases, and is dug by the method for recurrent neural network
Dig test cases and hide feature and user and hide feature, so for tester recommend more comprehensively, the higher test case of the degree of correlation
Example, provides efficient solution for the Test Case Design during iterative development, compensates for by tester's experience not
Sufficient bring tests risk.
In a specific embodiment of the invention, as shown in Figure 1, information module 1 mainly stores test cases library, system journey
The information such as sequence node flow chart, system testing problem base, the description of project demands function and user's test cases housing choice behavior.Its
In, test cases library is the test cases library of system full dose function, is the test cases collection that tester's history is write.System journey
Sequence node flow chart, refer to by the method for code static analysis generate between system program and program, program internal component it
Between flow chart.System testing problem base refers to that the test problem information of system storage, including problem are associated with test cases, problem
The information such as description, problem raiser, Resolving probiems people, problem types.Project demands function describes the requirement description for referring to project
With the realization information of function.User's test cases housing choice behavior refers to that disparity items and function realize lower choosing of the user to test cases
It selects.
Policy module 2 be mainly model calculate by way of for recommendation test cases result set generation provide decision according to
According to.As shown in Fig. 2, the module includes test cases-procedural model unit 21, test cases-problem model unit 22, test case
Example-demand model unit 23 and test cases-user feedback model unit 24, and pass through policy calculation unit 25 from test case
Learn the hiding feature of test cases in example, program, test problem and project demands and user feedback eigenmatrix and user is hidden
Hide feature, that is, the timing information being hidden in above data.Because often preference selects new test cases to user in practice,
And the age of consideration test cases and the age of user are also important to correction of deviation, so this system passes through policy calculation
Test cases selection forecasting problem is converted to sequence prediction problem by unit 25.
Wherein, test cases-procedural model unit 21 mainly establishes group inside between system program and program, program
The multi-to-multi matching relationship between flow chart and test cases between part.As shown in figure 3, test cases 1-211, test cases
2-212, test cases N-213 indicate a series of test cases that tester formulates according to the contents of a project, program 1-215, journey
Sequence 2-216, program N-217 then indicate to realize the collection of programs of a project concrete function.
One test cases can correspond to multiple programs, and different test cases can also correspond to the same program, therefore
Test cases-procedural model had not only established the mapping relations of test cases and program, but also provided support for the integrality of test.
The mode for establishing the mapping relations of test cases and program can be such as under type.First to storage test case in test cases library
Example text description pre-processed, remove test cases text describe medium-high frequency occur and to text classification it is meaningless
Then word carries out word segmentation processing to the text description of test cases according to certain strategy, and by the result set after word segmentation processing
According to perhaps theme in given document, class label predetermined is distributed automatically, to realize that test cases text describes
Text classification, then with the text of system program node flow chart description matched, finally obtain test cases-program
With relationship 214.
Test cases-problem model unit 22 is mainly to carry out text to test problem by the method for natural language processing
Classification, the incidence relation between Construct question descriptive semantics and test cases.As shown in figure 4, test problem 1-221, test are asked
Topic 2-222, test problem N-223 indicate the bugs that tester has found during project testing, test cases 1-
225, test cases 2-226, test cases N-227 then indicate a series of test cases that tester formulates according to the contents of a project
Example.The text description of storage test problem in test problem library is pre-processed first, removes and is described in test problem text
Medium-high frequency occurs and to the meaningless word of text classification, then describes to carry out according to text of certain strategy to test problem
Word segmentation processing, and the result set after word segmentation processing is distributed to class predetermined automatically according to perhaps theme in given document
Then distinguishing label is matched, finally to realize the text classification of test problem text description with test cases text classification
Obtain test problem-case matching relationship 224.Therefore test cases-problem model be it is a kind of from historical problem angle to survey
Try the semantic model that case increases problem and test cases combination, by the model can construct test problem and test cases it
Between multi-to-multi incidence relation.
Test cases-demand model unit 23 mainly by natural language processing-text snippet method, constructs demand
The incidence relation of description and test cases.As shown in figure 5, demand 1-231, demand 2-232, demand N-233 indicate the tool of project
Body demand information, test cases 1-235, test cases 2-236, test cases N-237 then indicate tester according in project
Hold a series of test cases formulated.Software requirements specification is pre-processed first, is removed in software requirements specification
High frequency occurs and to the meaningless word of text classification, then carries out at participle according to certain strategy to software requirements specification
Reason, and the result set after word segmentation processing is distributed to class label predetermined automatically according to perhaps theme in given document,
To realize the text classification of software requirements specification, is then matched with test cases text classification, finally obtain demand-
Test cases matching relationship 234.Test cases-demand model ensure that test cases and project demands and function realize content
Semantic association.
Test cases-user feedback model unit 24 mainly according to the actual result of user feedback Case retrieval, use by analysis
The behavior at family constructs the feature of user.The test cases of tester's history is selected according to certain keywords, such as test cases
It chooses rate, test cases to select time, test cases selection preference etc. recently, filters out the higher feature of occurrence frequency, then
It is matched with test cases text classification, to set up the incidence relation between feature and test cases.
According to test cases-procedural model unit 21, test cases-problem model unit 22, test cases-demand model
Unit 23 and test cases-user feedback model unit 24, the policy calculation unit 25 that system uses are that depth collaborative filtering is calculated
Method is trained, automatically from test using recurrent neural network as a kind of unsupervised learning method and collaborative filtering
Learn test cases in case, program, problem and demand and user feedback eigenmatrix and hide feature and the hiding feature of user,
Differentiation processing is carried out to specific test cases, test cases similarity calculation is carried out according to the attribute of model and feature, it is raw
At the high test cases Candidate Set of correlation.
Fig. 6 is recurrent neural network-depth collaborative filtering schematic diagram in the embodiment of the present invention.As shown in fig. 6, recurrence is refreshing
It include input layer X-253, hidden layer S-252 and output layer O-251 and hidden layer S-252 through network-depth collaborative filtering figure
Expanded form St-1-255、St-256、St+1-257.The input set of input layer X-253 is labeled as { X0,X1,...,Xt,
Xt+1... }, the output collection of hidden layer S-252 is marked as { S0,S1,...,St,St+1... }, the output collection of output layer O-251
It is marked as { O0,O1,...,Ot,Ot+1... }, St-1-255、St-256、St+1- 257 respectively indicate moment t-1, moment t, when
The output state of hidden layer under t+1 is carved, wherein hidden layer is responsible for completing most important work.As can be seen from Figure 6 a list
Hidden layer S-252 is flowed to from input layer X-253 to the information flow of flowing, while having the information flow of an one-way flow from hidden layer
S-252 flows into output layer O-251, wherein W-254 is circulation layer.But in some cases, recurrent neural network can break the latter
Limitation, guidance information from output layer return hidden layer, and hidden layer input also include a upper hidden layer state, i.e., it is hidden
Node in hiding layer can certainly or interconnect.By taking t moment as an example, can according to the main contents for inscribing project when this and
Last moment tester selects history in the test cases under similar terms background to predict that tester may under t moment
The test cases collection of selection.
In test cases intelligent extraction system, it is to solve the test cases prediction according to the selection of tester's history
Tester's test cases to be selected in future.In order to solve problem above, the selection of test cases is gone through according to tester
History can be his (x)={ X with the training data of structural model1,X2,...,Xn}.Wherein, X indicates the survey of tester's selection
Try case, X1,X2The time series of test cases is selected for tester.It, be first to tester simultaneously for the accuracy of data
The test cases selection historical data of member is cleaned.There is input data above, next just needs the model according to Fig. 6
It is trained, so that test cases selection forecasting problem is converted to sequence prediction problem, is finally carried out according to the probability of prediction
Sequence obtains final recommendation results.
Meanwhile the method that system is also introduced into the calculating of 25 cosine similarity of policy calculation unit, according to test cases-program mould
It is similar to calculate cosine for type, test cases-problem model, test cases-demand model and test cases-user feedback model
Property matching entries code, test problem and project demands and user feedback eigenmatrix, recommend the test case that correlation is high out
Example.Therefore, this system had both considered influence of the test cases content to recommendation, it is contemplated that influence of the user characteristics to recommendation,
Thus so preferable that improve the correlation that test cases is recommended.
It is the test cases similarity formula that this system proposes as shown in formula (1):
Wherein,It is a time attenuation function, α is an adjustment factor, ti、tjRespectively indicate user's selection case
The time of example i, case j, time attenuation function indicates that user selects case i, the time of case j more close, then between them
Correlation it is higher.NiIndicate the user of selection test cases i, NjThe user for indicating selection test cases j, so molecule contains
Justice is the number of users of simultaneous selection test cases i and test cases j.Above-mentioned formula can be understood as the use of selection test cases i
The user of how many ratio also selects test cases j in family.
Data module 3 mainly recommend test cases result set offline and recommend test cases result set in line computation by storage.
It is offline that test cases result set is recommended to be primarily referred to as the recommendation results being calculated by policy calculation unit 25.It is offline to recommend
Mainly contain set, the algorithm engine of algorithm, be responsible for the integration of test cases data, the extraction of feature, the training of model with
And the assessment under line.Test cases result set is recommended to extract corresponding mainly according to Xiang Mingda procedure set in line computation
Feature, and rearrangement of being merged and given a mark to result set using recurrent neural network algorithm, ultimately generate the recommendation in line computation
Test cases.It is sky in the recommendation test cases collection of line computation when not providing programing change inventory.
Fig. 7 is a kind of recommendation test cases collection product process figure of Test Case Design intelligent extraction system.Such as Fig. 7 institute
Show, it is online that test cases collection 307 is recommended to be according to change procedure set 1-301, change procedure set 2-302, change procedure set N-303
It is calculated Deng change procedure set by recurrent neural network algorithm, offline recommendation test cases collection 308 is drawn according to recommendation
Hold up what the recommended engines such as 1-304, recommended engine 2-305, recommended engine N-306 were obtained by policy calculation.It is counted to by strategy
Obtained offline recommendation test cases result set and line computation recommend test cases result set according to test cases similarity,
After the age of test cases, test cases carry out screening 309 with indexs such as the correlations of project, this system proposes following formula pair
Test cases result set carries out TopK sort recommendations 310 and 311, and obtains consequently recommended test cases 312.
Wherein, NuIt is the set of the test cases of user's selection, S (j, k) is the K test most like with test cases j
The set of case, WijIt is the similarity of test cases j and i,The probability of user u selection test cases i, β be weight because
Son, 0≤β≤1.The formula be meant that with the more similar test cases of test cases that was once selected in user's history, more
It is possible that obtaining relatively high ranking in the recommendation list of user.
Display module 4 mainly provide interface to user check recommend test cases information, test cases problem of management information,
Operation button selects test cases, importing and shows to change program etc..The module had not only provided the user with the entrance of information displaying, but also
Provide the user with test cases selection and feedback conduit.
Present system establishes test cases and test problem, test cases using natural language processing method and needs
The correlation model of entry is sought, provides strong decision-making foundation for the recommendation of subsequent test cases, and fully play test
The test assets of case act on；This system proposes the correlating method of building test cases and test program, subtest personnel
The test cases of design alteration program combines, and promotes the coverage rate and accuracy rate of test cases, meets project iteratively faster, quickly
The demand of delivery；This system proposes a kind of recommender system based on deep learning, is excavated by the method for recurrent neural network
Test cases hides feature and user hides feature, effectively utilizes traditional collaborative filtering recommending technology and deep learning method is each
From the advantages of, obtain long-term (overall situation) between tester and test cases and (part) be associated in short term, give full play to the two
Complementarity in recommender system.
The present invention can sufficiently promote the effect in test cases library, promote the accumulation and multiplexing of test assets, help to test
Personnel carry out Test Case Design, reduce Test Case Design and omit, while reducing the time of tester's screening test case.
With application prospect below and advantage: by the natural language processing method of Text similarity computing, analytic function modification is retouched
The text semantic stated establishes function description and the semantic association in storage test cases library, survey that can function is same or similar
Examination case is associated with out；It is associated with test cases according to existing program, test cases can be reasonably and comprehensively associated with, prevent test case
Example design is omitted；When carrying out recommending to calculate to test cases collection, recurrent neural network-depth collaborative filtering side is mainly used
Method establishes combinations of attributes and screening by the various attributive character of analyzing influence project testing case, provides the survey for recommending reference
Try casebook.When project delivery need to send out patch, suitable test cases can be provided in the shortest time, promote test effect
Rate.
The embodiment of the present invention also provides a kind of test cases intelligent extract method, and the method utilizes above-mentioned test cases
Intelligent extraction system carries out test cases intelligent extraction.
Conceived based on application identical with a kind of above-mentioned test cases intelligent extraction system, the present invention also provides above-mentioned one
Kind test cases intelligent extract method.Due to a kind of principle that test cases intelligent extract method solves the problems, such as and a kind of test
Case intelligent extraction system is similar, therefore a kind of implementation of test cases intelligent extract method may refer to a kind of test cases
The implementation of intelligent extraction system, overlaps will not be repeated.
The present invention can sufficiently promote the effect in test cases library, promote the accumulation and multiplexing of test assets, help to test
Personnel carry out Test Case Design, reduce Test Case Design and omit, while reducing the time of tester's screening test case.
With application prospect below and advantage: by the natural language processing method of Text similarity computing, analytic function modification is retouched
The text semantic stated establishes function description and the semantic association in storage test cases library, survey that can function is same or similar
Examination case is associated with out；It is associated with test cases according to existing program, test cases can be reasonably and comprehensively associated with, prevent test case
Example design is omitted；When carrying out recommending to calculate to test cases collection, recurrent neural network-depth collaborative filtering side is mainly used
Method establishes combinations of attributes and screening by the various attributive character of analyzing influence project testing case, provides the survey for recommending reference
Try casebook.When project delivery need to send out patch, suitable test cases can be provided in the shortest time, promote test effect
Rate.
Those of ordinary skill in the art will appreciate that implementing the method for the above embodiments can lead to
Program is crossed to instruct relevant hardware and complete, which can be stored in a computer readable storage medium, such as
ROM/RAM, magnetic disk, CD etc..
Particular embodiments described above has carried out further in detail the purpose of the present invention, technical scheme and beneficial effects
Describe in detail it is bright, it should be understood that the above is only a specific embodiment of the present invention, the guarantor being not intended to limit the present invention
Range is protected, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in this
Within the protection scope of invention.
Claims (10)
1. a kind of test cases intelligent extraction system, which is characterized in that the system comprises: information module, policy module, data
Module and display module；
The information module is for storing test cases library, system program node flow chart, system testing problem base, project demands
Functional circuit information and user's test cases housing choice behavior information；
The policy module establishes test cases and program, problem, demand according to the information stored in the information module respectively
And the model of user feedback, and generate test cases using depth collaborative filtering and recurrent neural network and recommend sequence, really
The fixed test cases recommends the similarity of each test cases in sequence, generates test cases recommendation results；
The data module is according to the essential information and its similarity of the test cases in the test cases recommendation results, to institute
It states the test cases in test cases recommendation results to be screened and sorted, determines consequently recommended test cases；
The display module receives field feedback for showing the consequently recommended test cases.
2. system according to claim 1, which is characterized in that the policy module includes test cases and procedural model list
Member, for in test cases library test cases text description carry out text classification, by the text classification of test cases be
System program node flow chart text description matched, determine test cases and procedure match relationship, obtain test cases and
Procedural model.
3. system according to claim 1, which is characterized in that the policy module includes test cases and problem model list
Member carries out text classification for the text description to test cases in test cases library, and to surveying in system testing problem base
The text description why inscribed carries out text classification, by the text classification of test cases and the text classification of test problem progress
Match, determines test cases and problem matching relationship, obtain test cases and problem model.
4. system according to claim 1, which is characterized in that the policy module includes test cases and demand model list
Member carries out text classification for the text description to test cases in test cases library, and describes to believe to project demands function
The text description of software requirements specification carries out text classification in breath, by the text classification of test cases and software requirements specification
Text classification matched, determine test cases and demand matching relationship, obtain test cases and demand model.
5. system according to claim 1, which is characterized in that the policy module includes test cases and user feedback mould
Type unit carries out text classification, and analysis user's test cases for the text description to test cases in test cases library
User feedback behavior in housing choice behavior information, obtains user feedback feature, by the text classification and user feedback of test cases
Feature is matched, and is determined test cases and user feedback characteristic matching relationship, is obtained test cases and user feedback model.
6. system according to claim 1, which is characterized in that the policy module includes policy calculation unit, is used for root
According to test cases and program, problem, demand and the model of user feedback, depth collaborative filtering and recurrent neural network are utilized
It generates test cases and recommends sequence, determine that the test cases recommends the similarity of each test cases in sequence, generate test case
Example recommendation results.
7. system according to claim 6, which is characterized in that the policy calculation unit is public according to test cases similarity
Formula determines that the test cases recommends the similarity of each test cases in sequence, the test cases similarity formula are as follows:
Wherein,It is time attenuation function, α is adjustment factor, ti、tjIt respectively indicates user and selects test cases i, test
The time of case j, NiIndicate the number of users of selection test cases i, NjIndicate the number of users of selection test cases j.
8. system according to claim 1, which is characterized in that the data module is also used to according to programing change information,
The test cases recommendation results are merged and reset using recurrent neural network, online recommendation results are generated, to described
Test cases in test cases recommendation results and the online recommendation results is screened and is sorted, and determines consequently recommended survey
Try case.
9. system according to claim 1, which is characterized in that the data module is according to sort formula to the test case
Test cases in example recommendation results is ranked up, wherein the sort formula are as follows:
Wherein, NuIt is the set of the test cases of user's selection, S (j, k) is the K test cases most like with test cases j
Set, WijIt is the similarity of test cases j and i,It is the probability of user u selection test cases i, β is weight factor, 0≤β
≤1。
10. a kind of test cases intelligent extract method, which is characterized in that the method utilizes any right in claim 1-9
It is required that the test cases intelligent extraction system, carries out test cases intelligent extraction.
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