CN106649122A - Model construction method and device for terminal application - Google Patents
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- CN106649122A CN106649122A CN201611236979.8A CN201611236979A CN106649122A CN 106649122 A CN106649122 A CN 106649122A CN 201611236979 A CN201611236979 A CN 201611236979A CN 106649122 A CN106649122 A CN 106649122A
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Abstract
The invention is applicable to the technical field of software tests, and provides a model construction method and device for a terminal application. The model construction method comprises the steps of obtaining status sequences from application starting to application closing of the terminal application when the terminal application is running normally, wherein the state in the status sequences comprises an operation order input by a user and an interface pack name which is skipped in the operating process; forming a behavioral training set through the obtained multiple state sequences, wherein any state of the terminal application is included in at least one state sequence of the multiple state sequences; constructing a Markov test model related to the operation of the application based on the behavioral training set. The construction procedure of the model is simple, by adding the state sequence into the behavioral training set, an original test model can be improved, thus the situation that if the functions of the application are modified, the model need to be reconstructed completely is avoided, and the model construction efficiency of the terminal application is improved.
Description
Technical field
The invention belongs to software testing technology field, more particularly to a kind of model building method and device of terminal applies.
Background technology
With the arrival in mobile Internet epoch, terminal application software is developed rapidly.For application developer
Speech, rapidly releasing application contributes to dominating the market, and so as to win more users, therefore the test period applied is shorter.But
Unknown exception and mistake will likely be included without the application of careful test, the function of application software can not be realized.Application and development
The a great problem of person is how the application to developing carries out efficiently sufficiently test, so as to reduce using exception that may be present
And mistake, lift the quality of application product.
Relative to manual testing, automatic test is the method that efficient test is carried out to application.Wherein, the survey based on model
Method for testing can pass through the test that model is automated to application, and good test effect can be played to application.But at present
The model construction process of terminal applies is complex, and after developer carries out some function supplements to application program, model
Needs rebuild.
The content of the invention
The purpose of the embodiment of the present invention is the model building method and device for providing a kind of terminal applies, it is intended to solved eventually
The model construction process of end application is complex, and after developer carries out some function supplements to application program, model is needed
The problem to be rebuild.
The embodiment of the present invention is achieved in that a kind of model building method of terminal applies, including：
Obtain and started to using the status switch between closing, the status switch from application when terminal applies are normally run
In state including user input operational order and running in the interface bag name that redirects；
The multiple status switches for getting are constituted into Behavioral training collection, any state of terminal applies is included at least described
Among one of status switch of multiple status switches；
Based on the Behavioral training collection, the Markov test model with regard to application operation is built.
The another object of the embodiment of the present invention is the model construction device for providing a kind of terminal applies, including：
Acquiring unit, start to using the state sequence between closing from application when terminal applies are normally run for obtaining
Row, the state in the status switch includes the interface bag name redirected in the operational order and running of user input；
Component units, for the multiple status switches for getting to be constituted into Behavioral training collection, any state of terminal applies
Including at least among one of status switch of the plurality of status switch；
Construction unit, for based on the Behavioral training collection, building the Markov test mould with regard to application operation
Type.
In the embodiment of the present invention, the operational order in terminal applies running constitutes shape with the interface bag name for redirecting
State sequence, by multiple status switches training set is constituted, and builds the Markov test model of terminal applies test.The present invention is implemented
The model construction process of example terminal applies is simple, and can be by adding status switch in behavior training set, in original survey
Improved in die trial type, it is to avoid if application function has been changed, it is necessary to the situation of whole reconstruction models, improve terminal should
Model construction efficiency.
Description of the drawings
Fig. 1 is the flowchart of the model building method of terminal applies provided in an embodiment of the present invention；
Fig. 2 is that the model building method S103 of terminal applies provided in an embodiment of the present invention implements flow chart；
Fig. 3 is the structured flowchart of the model construction device of terminal applies provided in an embodiment of the present invention.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, and
It is not used in the restriction present invention.
In the embodiment of the present invention, the operational order in terminal applies running constitutes shape with the interface bag name for redirecting
State sequence, by multiple status switches training set is constituted, and builds the Markov test model of terminal applies test.The present invention is implemented
The model construction process of example terminal applies is simple, and can be by adding status switch in behavior training set, in original survey
Improved in die trial type, it is to avoid if application function has been changed, it is necessary to the situation of whole reconstruction models, improve terminal should
Model construction efficiency.
Terminal includes that mobile phone, panel computer, notebook, intelligent television etc. can be installed and run the terminal of application program and sets
It is standby.Operational order refers to that user that terminal receives, using the instruction triggered by input operation when applying, is such as returned, set
The instruction such as put, enter.Input operation includes button operation, slide, clicking operation etc..Terminal is according to received operation
Instruction is made using actions such as the corresponding changing interface of execution, window ejections.
The interface of application program is made up of various UI components.UI components include file, picture, animation display module, press
Button, choice box component etc..Each interface is according to its interface to there is unique interface bag name.Can be according to interface Bao Mingqu
Divide different interfaces.
Markov (Markov) model is based on the probabilistic model in a kind of markovian mathematical statistics.Wherein,
Markov Chain is used for describing state and time all discrete Markov random processes.It is defined as follows：
Define one：If { Xn, n=0,1,2 ... it is a discrete random process, its state set is Ω={ θ1,
θ2,...,θN, then for any k >=1, n0<n1<...<nk-1<m<N, has random sequence to be s in n moment state in whichnIt is general
Rate is only with it in m moment state in which smIt is relevant, and it is unrelated with its state in which before the m moment.I.e.：
Wherein, s1,s2,...,sm,sn∈ Ω={ θ1,θ2,...,θN, then claim { Xn, n=0,1,2 ... } it is Markov
Chain.
Define two：For the Markov chain { X of N number of staten, n=0,1,2 ..., N }, claim Pij(n, n+k)=P (Xn+k=
θj|Xn=θi), 1≤i, j≤N walk transition probability for the k of Markov chain；If Pij(n, n+k) is unrelated with n, then claim the Markov
Chain is homogeneous Markov chains, now Pij(n, n+k)=Pij(k).Particularly, as k=1, Pij(1) it is referred to as a step transition probability,
Abbreviation transition probability, is designated as aij,0≤aij≤ 1, and claim A=(aij)N×NFor state transition probability matrix, i.e. state metastasis sequence.
Single order homogeneous Markov chains are adopted herein.
Define three：For the Markov chain { X of N number of staten, n=0,1,2 ..., N }, remember πi=P (Xi=θi) and claim arrow
Amount π=(π1,π2,...,πN) it is initial state probabilities vector.
What Fig. 1 showed the model building method of terminal applies provided in an embodiment of the present invention realizes flow process, and details are as follows：
In S101, obtain and started to using the status switch between closing, institute from application when terminal applies are normally run
The state in status switch is stated including the interface bag name redirected in the operational order and running of user input.
In the present embodiment, the normal operation of terminal applies is referred to be redirected in user operation and interface and all do not occur operation mistake
By mistake.Status switch is one group of state that the sequencing redirected according to application is arranged.Each state is by an operation
Interface bag name after instruction and the operational order redirect application interface is constituted.The initial state of status switch is to open application
Operational order and the main interface of application, the end-state of status switch be close application operational order and application exit boundary
Face.
Obtaining for status switch can be redirected by the interface that obtain the terminal applies of designer's offer and can realize
Multiple states are arranged as state sequence by the interface bag name that the operational order and application interface in journey is redirected according to time order and function order
Row.Or application software is operated by reality, record and jumped using the operational order in running and application interface
The interface bag name for turning, is combined as after state being arranged as status switch according to the sequencing of record time.
For example, status switch：OPEN, using main interface, arranges instruction, arranges interface, security setting instruction, safety
Interface is set, and exit instruction exits interface.Wherein, OPEN and using main interface be a state, arrange instruction and arrange
Interface is a state, and security setting instruction and security setting interface are a state, and exit instruction is one with interface is exited
State.Whole status switch is formed by four different states according to time order and function arrangement.
In S102, the multiple status switches for getting are constituted into Behavioral training collection, any state of terminal applies is at least
Among being included in one of status switch of the plurality of status switch.
In the present embodiment, multiple status switches refer to multiple different status switches, and contain in these sequences
All states that terminal applies can be realized.That is, any one state that terminal applies can be realized all in status switch extremely
It is few to occur once.These status switches form together Behavioral training collection.Behavioral training collection is used to build Markov test model.
Behavioral training concentrate status switch number it is more, the project that constructed model can be tested is more, test it is also more smart
It is accurate.
For convenience of describing, the interface bag name composition redirected refer to the operational order of user input and the instruction herein after
State be referred to as an atomic operation.As OPEN and using main interface be an atomic operation.If the behavior of terminal applies
Training set includes M status switch, and R is designated as respectively1,R2,R3,...RM.Wherein i-th status switch is designated asRiInRepresent individual according to the jth (1≤j≤r (i)) of time relative ranks arrangement
Atomic operation.
In S103, based on the Behavioral training collection, the Markov test model with regard to application operation is built.
That is, all of status switch is concentrated according to Behavioral training, builds the Markov test for test terminal application
Model.
As one embodiment of the present of invention, as shown in Fig. 2 in S103, collected based on the Behavioral training, build Maguan in
The Er Kefu test models of the application operation, including：
In S201, all states in the plurality of status switch are constituted into a set, identical state in set
With a state representation, the state set of Markov test model is formed.
Wherein, the state set of Markov test model is all different conditions occurred in these status switches
Set.The multiple states of identical are only used as the state set that a state adds Markov test model in these sequences.This
Place is designated as state set
In S202, based on the Behavioral training collection, the initial state probabilities vector of Markov test model is calculated
State transition probability matrix.
In the present embodiment, Behavioral training collection includes multiple status switches, and according to these multiple status switches horse is calculated
The model parameter of Er Kefu test models.Model parameter includes state set, initial state probabilities vector state transition probability
Matrix.
Wherein, initial state probabilities vector is the probability that each state occurs in initial time in Markov test model.
For the Markov chain { X of N number of staten, n=0,1,2 ..., N }, remember πi=P (Xi=θi) and claim vector π=(π1,π2,...,
πN) it is initial state probabilities vector.
Initial state probabilities vector π=(π1,π2,...,πN) be used to describe what each atomic operation occurred in initial time
Probability, initial state probabilities vector π=(π1,π2,...,πN) concrete calculation it is as follows：
Statistic behavior setIn each state (i.e. each atomic operation) in behavior training set R
={ R1,R2,R3,...,RMIn occur number of times and between them mutual phase transfer number of times.If any one first stateThe number of times occurred in behavior training set R is Ci, the number of times that all first states occur in behavior training set R
For r, then
State transition probability matrix A=(aij)N×NTiming dependence between each atomic operation is described.State transfer is general
Rate matrix A=(aij)N×NCalculation it is as follows：
In being located at Behavioral training collection R, atomic operationToThe number of times of transfer is Zij, i.e. sequence
RowThe number of times occurred in behavior training set, and it is located at Behavioral training concentration atomic operationTurn to each atomic operation
The total degree of shifting is Yi, then
As one embodiment of the present of invention, when all shapes that the status switch and the Behavioral training of test generation are concentrated
When state sequence is different from, then the status switch is added into the Behavioral training and is concentrated.
In the present embodiment, the status switch that the status switch for test being produced and Behavioral training are concentrated is contrasted.Such as
Any state sequence that the status switch and Behavioral training that fruit test is produced is concentrated is different from, then be added to the status switch
Behavioral training is concentrated.
Because Markov test model is Behavior-based control training set being built.The status switch that Behavioral training is concentrated
Number is more, it is constructed go out test of the test model to terminal applies it is more accurate.By the new behavior produced in test process
Sequence is added to the Behavioral training of test model and concentrates, and recycles behavior training set to change original test model afterwards
It is kind.Test model can so further improved in test process, with testing time and the status switch of Self -adaptive
Increase, further improve the test accuracy of test model.
When the designer of terminal applies is improved to the interface turn function applied, can be in behavior training set
Delete and apply after improving after irrealizable status switch, addition are improved using the newly-increased status switch that can be realized.So
Test model can be also carried out to improve, test model during so as to avoiding application function from improving when application function is improved
Need situation about rebuilding completely to occur, improve the structure efficiency of test model.
Used as one embodiment of the present of invention, the Markov test model includes probability database, the probability number
It is the corresponding probability of status switch that the Behavioral training is concentrated according to the probability in storehouse.
In the present embodiment, according to initial state probabilities vector state transition probability matrix come calculate Behavioral training concentrate
Status switch probability, and these probability are added in the probability database of Markov test model.So when carrying out
During the test of terminal applies, the state of Self -adaptive can be calculated according to initial state probabilities vector state transition probability matrix
The probability of sequence.By the way that the probability for calculating is contrasted with the probability in probability database, the shape of Self -adaptive is judged
Whether state sequence is correct.
If the probability for calculating is identical with a certain probability in probability database, show the state sequence of the Self -adaptive
Normal sequence is classified as, the operational order of the user input in test process and the interface for redirecting are all normal.
As one embodiment of the present of invention, when all shapes that the status switch and the Behavioral training of test generation are concentrated
When state sequence is different from, the corresponding probability of behavior sequence is calculated, in being added to the probability database.
In the present embodiment, when test produce status switch and the Behavioral training concentrate all status switches not
When identical, then according to initial state probabilities vector state transition probability matrix calculating the corresponding probability of behavior sequence, and
The probability for calculating is added in probability database.
As test is carried out, the status switch of Self -adaptive will increase.Some status switches may be not belonging to behavior instruction
Practice collection.The probability that these status switches can now be calculated is added in probability database.Such entering with test process
OK, the testing efficiency and measuring accuracy of the test model can be effectively improved.
Used as one embodiment of the present of invention, methods described also includes：
The interface redirected in the operational order and running of the user input of terminal applies during snoopy test
Bag name, generates the test mode sequence being arranged in by test mode；The operational order of the test mode including user input and
The interface bag name redirected in running.
According to the initial state probabilities vector state transition probability matrix of the Markov test model, calculate described
The corresponding probability of test mode sequence.
The corresponding probability of the test mode sequence and the probability in the probability database are matched, according to matching
As a result, judge whether the interface in the test mode sequence redirects normal.
In the present embodiment, monitor and record in test process and start to the behaviour using user input between closing from application
Make the interface bag name redirected in instruction and running.To occur in the operational order and running of the user input
The interface bag name for redirecting is combined into test mode.Test mode is arranged in sequentially in time test mode sequence.Then root
According to the initial state probabilities vector state transition probability matrix of the Markov test model, the test mode sequence is calculated
Arrange corresponding probability.
It is described to judge whether the interface in the test mode sequence redirects normal according to matching result, including：If institute
State the corresponding probability of test mode sequence identical with certain probability in the probability database, then in the test mode sequence
All interfaces redirect all normal.If the corresponding probability of test mode sequence and the probability in the probability database are not
Identical, then at least once interface redirects exception in the test mode sequence.
When the probability in the corresponding probability of the test mode sequence and the probability database is different from, institute is contrasted
The status switch that test mode sequence and the Behavioral training are concentrated is stated, determines that the test mode sequence median surface redirects generation
The operational order and interface bag name of exception.So can find which interface jump procedure is sent out in tested application by test model
Raw mistake.Designer can be adjusted correspondingly accordingly, improve testing efficiency.
In the embodiment of the present invention, the operational order in terminal applies running constitutes shape with the interface bag name for redirecting
State sequence, by multiple status switches training set is constituted, and builds the Markov test model of terminal applies test.The present invention is implemented
The model construction process of example terminal applies is simple, and can be by adding status switch in behavior training set, in original survey
Improved in die trial type, it is to avoid if application function has been changed, it is necessary to the situation of whole reconstruction models, improve terminal should
Model construction efficiency.
It should be understood that in embodiments of the present invention, the size of the sequence number of above-mentioned each process is not meant to the elder generation of execution sequence
Afterwards, the execution sequence of each process should determine with its function and internal logic, and should not be to the implementation process structure of the embodiment of the present invention
Into any restriction.
The model building method of the terminal applies provided corresponding to the embodiment of the present invention, Fig. 3 shows enforcement of the present invention
The structured flowchart of the model construction device of the terminal applies that example is provided.For convenience of description, illustrate only related to the present embodiment
Part.
With reference to Fig. 3, the model construction device of the terminal applies includes：
Acquiring unit 31, start to using the state sequence between closing from application when terminal applies are normally run for obtaining
Row, the state in the status switch includes the interface bag name redirected in the operational order and running of user input.
Component units 32, for the multiple status switches for getting to be constituted into Behavioral training collection, arbitrary shape of terminal applies
State is included at least among one of status switch of the plurality of status switch.
Construction unit 33, for based on the Behavioral training collection, building the Markov test with regard to application operation
Model.
Preferably, the construction unit 33 is used for：
All states in the plurality of status switch are constituted into a set, identical state is with a state in set
Represent, form the state set of Markov test model.
Based on the Behavioral training collection, the initial state probabilities vector state transfer for calculating Markov test model is general
Rate matrix.
Preferably, described device also includes adding device, and the adding device is used for：
When all status switches that status switch and the Behavioral training that test is produced are concentrated are different from, then should
Status switch is added to the Behavioral training and concentrates.
Preferably, the Markov test model includes probability database, and the probability in the probability database is institute
State the corresponding probability of status switch of Behavioral training concentration.
Preferably, described device also includes computing unit, and the computing unit is used for：
When all status switches that status switch and the Behavioral training that test is produced are concentrated are different from, calculate
The corresponding probability of behavior sequence, in being added to the probability database.
In the embodiment of the present invention, the operational order in terminal applies running constitutes shape with the interface bag name for redirecting
State sequence, by multiple status switches training set is constituted, and builds the Markov test model of terminal applies test.The present invention is implemented
The model construction process of example terminal applies is simple, and can be by adding status switch in behavior training set, in original survey
Improved in die trial type, it is to avoid if application function has been changed, it is necessary to the situation of whole reconstruction models, improve terminal should
Model construction efficiency.
Presently preferred embodiments of the present invention is the foregoing is only, not to limit the present invention, all essences in the present invention
Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.
Claims (10)
1. a kind of model building method of terminal applies, it is characterised in that include：
Obtain and started to using the status switch between closing, in the status switch from application when terminal applies are normally run
State includes the interface bag name redirected in the operational order and running of user input；
The multiple status switches for getting are constituted into Behavioral training collection, any state of terminal applies is included at least the plurality of
Among one of status switch of status switch；
Based on the Behavioral training collection, the Markov test model with regard to application operation is built.
2. the method for claim 1, it is characterised in that described to be collected based on the Behavioral training, builds Maguan in described
Using operation Er Kefu test models, including：
All states in the plurality of status switch are constituted into a set, identical state is with a state table in set
Show, form the state set of Markov test model；
Based on the Behavioral training collection, the initial state probabilities vector state transition probability square of Markov test model is calculated
Battle array.
3. the method for claim 1, it is characterised in that methods described also includes：
When all status switches that status switch and the Behavioral training that test is produced are concentrated are different from, then by the state
Sequence is added to the Behavioral training and concentrates.
4. the method for claim 1, it is characterised in that the Markov test model includes probability database, institute
State the corresponding probability of status switch that the probability in probability database is that the Behavioral training is concentrated.
5. method as claimed in claim 4, it is characterised in that methods described also includes：
When all status switches that status switch and the Behavioral training that test is produced are concentrated are different from, the row is calculated
For the corresponding probability of sequence, in being added to the probability database.
6. the model construction device of a kind of terminal applies, it is characterised in that include：
Acquiring unit, start to using the status switch between closing, institute from application when terminal applies are normally run for obtaining
The state in status switch is stated including the interface bag name redirected in the operational order and running of user input；
Component units, for the multiple status switches for getting to be constituted into Behavioral training collection, any state of terminal applies is at least
Among being included in one of status switch of the plurality of status switch；
Construction unit, for based on the Behavioral training collection, building the Markov test model with regard to application operation.
7. device as claimed in claim 6, it is characterised in that the construction unit is used for：
All states in the plurality of status switch are constituted into a set, identical state is with a state table in set
Show, form the state set of Markov test model；
Based on the Behavioral training collection, the initial state probabilities vector state transition probability square of Markov test model is calculated
Battle array.
8. device as claimed in claim 6, it is characterised in that described device also includes adding device, the adding device is used
In：
When all status switches that status switch and the Behavioral training that test is produced are concentrated are different from, then by the state
Sequence is added to the Behavioral training and concentrates.
9. device as claimed in claim 6, it is characterised in that the Markov test model includes probability database, institute
State the corresponding probability of status switch that the probability in probability database is that the Behavioral training is concentrated.
10. device as claimed in claim 9, it is characterised in that described device also includes computing unit, the computing unit is used
In：
When all status switches that status switch and the Behavioral training that test is produced are concentrated are different from, the row is calculated
For the corresponding probability of sequence, in being added to the probability database.
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| CN109947497A (en) * | 2017-12-20 | 2019-06-28 | 广东欧珀移动通信有限公司 | Application program preloads method, apparatus, storage medium and mobile terminal |
| CN109947497B (en) * | 2017-12-20 | 2021-06-08 | Oppo广东移动通信有限公司 | Application program preloading method and device, storage medium and mobile terminal |
| CN108304324B (en) * | 2018-01-22 | 2022-07-19 | 百度在线网络技术（北京）有限公司 | Test case generation method, device, equipment and storage medium |
| CN109783381A (en) * | 2019-01-07 | 2019-05-21 | 中国银行股份有限公司 | A kind of test data generating method, apparatus and system |
| CN109783381B (en) * | 2019-01-07 | 2021-11-09 | 中国银行股份有限公司 | Test data generation method, device and system |
| CN109977029A (en) * | 2019-04-09 | 2019-07-05 | 科大讯飞股份有限公司 | A kind of training method and device of page jump model |
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