CN108132887B - User interface method of calibration, device, software testing system, terminal and medium - Google Patents
User interface method of calibration, device, software testing system, terminal and medium Download PDFInfo
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- CN108132887B CN108132887B CN201810024168.4A CN201810024168A CN108132887B CN 108132887 B CN108132887 B CN 108132887B CN 201810024168 A CN201810024168 A CN 201810024168A CN 108132887 B CN108132887 B CN 108132887B
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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
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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
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
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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
- G06F11/3692—Test management for test results analysis
Abstract
The present invention proposes a kind of user interface method of calibration, device, software testing system, terminal and computer readable storage medium, and wherein user interface method of calibration includes: the picture of acquisition user interface to be verified；Construct user interface Knowledge Verification Model；And the picture of the user interface to be verified of acquisition is inputted into the user interface Knowledge Verification Model and is verified, output verification result, wherein, the check results include: whether the picture of the user interface to be verified is abnormal, and the anomaly classification information in the case where picture exception.Embodiment provided by the invention improves the checking feature of automatic test, is particularly suitable for the serious compatibility automated test scene of fragmentation, significantly improves compared to more existing people sensory testing or screenshot descendant sensory testing, testing efficiency.
Description
Technical field
The present invention relates to information technology field more particularly to a kind of user interface method of calibration, device, software test systems
System, terminal and computer readable storage medium.
Background technique
With the development of electronic information technology, there are a variety of different versions and machine for current software product and hardware product
Type.By taking android (Android) platform as an example, a maximum disadvantage of Android platform is exactly fragmentation, is embodied in and sets
Standby various, brand is numerous, and version is different, resolution ratio disunity etc.., it is apparent that android fragmentation from data
Be mainly reflected on type, subdivision go down also concerning system version, customization rom (Read Only Memory image, it is read-only to deposit
Reservoir mirror image), several dimensions such as resolution ratio, to sum up, very strange, quantity is various.
At present since android fragmentation is than more serious, the compatibility test of app occupies larger in daily test job
Specific gravity.Lengthy and jumbled Android intelligent machine brand and huge type quantity in market bring very big be stranded to compatibility test
Difficulty carries out compatibility test to a plurality of mobile devices, it means that huge workload and purchase machine cost.The prior art is commonly adopted
Employment sensory testing or the verifying of screenshot descendant's sense organ will do compatibility test, the efficiency of this test method for numerous types
It is lower.How to improve the testing efficiency of compatibility is current problem to be solved.
Summary of the invention
The embodiment of the present invention provides a kind of user interface method of calibration, device, software testing system, terminal and computer can
Storage medium is read, at least to solve one or more technical problems in the prior art.
In a first aspect, the embodiment of the invention provides a kind of user interface methods of calibration, comprising: acquire user to be verified
The picture at interface；Construct user interface Knowledge Verification Model；And the picture of the user interface to be verified of acquisition is inputted into institute
It states user interface Knowledge Verification Model to be verified, output verification result, wherein the check results include: the use to be verified
Whether the picture at family interface is abnormal, and the anomaly classification information in the case where picture exception.
With reference to first aspect, the present invention is in the first embodiment of first aspect, the building user interface verification
Model, comprising: the picture of collection user interface；The picture of the user interface of acquisition is labeled, the letter of the mark
Breath includes: whether the picture of user interface is abnormal, and the anomaly classification information in the case where picture exception；It is described different
Normal classification information includes abnormal type and abnormal position and the abnormal type includes text truncation, control covering and control
Any one of dislocation and combinations thereof；Using the picture training of the user interface after mark, to generate the user interface verification
Model.
The first embodiment with reference to first aspect, the present invention are described in second of embodiment of first aspect
The picture of collection user interface, comprising: the picture of collection user interface during the test, and excavate user interface collected
Picture the anomaly classification information；And/or the anomaly classification information according to prediction, simulate the exception with various predictions
The picture of the corresponding user interface of classification information.
Second of embodiment of the first embodiment, first aspect with reference to first aspect, the present invention is in first party
In the third embodiment in face, after the picture to the user interface of acquisition is labeled, and after using mark
User interface picture training before, further includes: the picture of the user interface after the mark is subjected to data processing,
In, the data processing includes gray processing processing and/or normalized.
Second of embodiment of the first embodiment, first aspect with reference to first aspect, the present invention is in first party
In the 4th kind of embodiment in face, further includes: generate user interface Knowledge Verification Model using the picture training of multiple user interfaces；With
And by the user interface Knowledge Verification Model of generation according to the picture output verification result of a user interface of input.
Second of embodiment of the first embodiment, first aspect with reference to first aspect, the present invention is in first party
In the 5th kind of embodiment in face, further includes: user interface Knowledge Verification Model is generated using the picture training of multiple groups user interface,
In, the picture of the user interface after every group of mark includes the picture that multiple similarities are higher than the user interface of predetermined threshold；Pass through
The user interface Knowledge Verification Model generated is according to the picture output verification of one group of user interface of input as a result, the verification is tied
Fruit includes whether the picture of every user interface in the picture of one group of user interface is abnormal, and abnormal in the picture
In the case where anomaly classification information.
With reference to first aspect, the first embodiment of first aspect, second of embodiment of first aspect, are generating
After user interface Knowledge Verification Model, further includes: setting loss function, adjustment model parameter minimize loss function.
Second aspect, the embodiment of the invention provides a kind of user interface calibration equipments, comprising: acquisition unit, for adopting
Collect the picture of user interface to be verified；Model construction unit, for constructing user interface Knowledge Verification Model；Verification unit is used for:
The picture of the user interface to be verified of acquisition is inputted the user interface Knowledge Verification Model to verify, output verification knot
Fruit, wherein the check results include: whether the picture of the user interface to be verified is abnormal and different in the picture
Anomaly classification information in the case where often.
In conjunction with second aspect, in the first embodiment of second aspect, the model construction unit also wraps the present invention
It includes: acquisition subelement, the picture for collection user interface；Mark subelement, the figure for the user interface to acquisition
Piece is labeled, and the information of the mark includes: whether the picture of user interface is abnormal, and in the situation of the picture exception
Under anomaly classification information；The anomaly classification information includes abnormal type and abnormal position and the abnormal type includes
Any one of text truncation, control covering and control dislocation and combinations thereof；Training subelement, for using the user after marking
The picture training at interface, to generate the user interface Knowledge Verification Model.
In conjunction with the first embodiment of second aspect, the present invention is described in second of embodiment of second aspect
Acquisition subelement is also used to: the picture of collection user interface during the test, and excavates the picture of user interface collected
The anomaly classification information；And/or the anomaly classification information according to prediction, it simulates and believes with the anomaly classification of various predictions
The picture of the corresponding user interface of manner of breathing.
The first embodiment, second of embodiment of second aspect in conjunction with second aspect, the present invention is in second party
In the third embodiment in face, the model construction unit further includes processing subelement, is used for: by the user after the mark
The picture at interface carries out data processing, and the data processing includes gray processing processing and/or normalized.
The first embodiment, second of embodiment of second aspect in conjunction with second aspect, the present invention is in second party
In the 4th kind of embodiment in face, the model construction unit is also used to: being generated and is used using the picture training of multiple user interfaces
Family interface Knowledge Verification Model；And the verification unit is also used to: the picture of a user interface is inputted the user interface school
It tests model to be verified, output verification result.
The first embodiment, second of embodiment of second aspect in conjunction with second aspect, the present invention is in second party
In the 5th kind of embodiment in face, the model construction unit is also used to: being generated and is used using the picture training of multiple groups user interface
Family interface Knowledge Verification Model, wherein the picture of the user interface after every group of mark includes the use that multiple similarities are higher than predetermined threshold
The picture at family interface；The verification unit is also used to: the picture of one group of user interface is inputted the user interface Knowledge Verification Model
It is verified, output verification is as a result, the check results include every user interface in the picture of one group of user interface
Picture it is whether abnormal, and the anomaly classification information in the case where picture exception.
The first embodiment, second of embodiment of second aspect in conjunction with second aspect, second aspect further include
Model tuning unit, is used for: setting loss function, and adjustment model parameter minimizes loss function.
It is described to deposit including processor and memory in the structure of user interface calibration equipment in a possible design
Reservoir is used to store the program for supporting user interface calibration equipment to execute user interface method of calibration in above-mentioned first aspect, described
Processor is configurable for executing the program stored in the memory.
The third aspect, the embodiment of the invention provides institutes any in a kind of software testing system, including above-mentioned second aspect
The user interface calibration equipment stated.
Fourth aspect, the embodiment of the invention provides a kind of terminal, the terminal includes: one or more processors；It deposits
Storage device, for storing one or more programs；When one or more of programs are executed by one or more of processors
When, so that one or more of processors realize the method as described in any in above-mentioned first aspect.
Fourth aspect, the embodiment of the invention provides a kind of computer readable storage mediums, are stored with computer program,
The program realizes any method in above-mentioned first aspect when being executed by processor.
A technical solution in above-mentioned technical proposal has the following advantages that or the utility model has the advantages that embodiment provided by the invention
The checking feature for improving automatic test is particularly suitable for the serious compatibility automated test scene of fragmentation, applicable
In different editions, different language environment, different type of machines, different resolution and brush machine packet；Compared to more existing people sensory testing or
Screenshot descendant sensory testing, testing efficiency significantly improve.
Above-mentioned general introduction is merely to illustrate that the purpose of book, it is not intended to be limited in any way.Except foregoing description
Schematical aspect, except embodiment and feature, by reference to attached drawing and the following detailed description, the present invention is further
Aspect, embodiment and feature, which will be, to be readily apparent that.
Detailed description of the invention
In the accompanying drawings, unless specified otherwise herein, otherwise indicate the same or similar through the identical appended drawing reference of multiple attached drawings
Component or element.What these attached drawings were not necessarily to scale.It should be understood that these attached drawings depict only according to the present invention
Disclosed some embodiments, and should not serve to limit the scope of the present invention.
Fig. 1 is the general frame figure of the user interface method of calibration of the embodiment of the present invention；
Fig. 2 is a kind of step flow chart of preferred embodiment of user interface method of calibration provided by the invention；
Fig. 3 is the general frame figure of the user interface calibration equipment of the embodiment of the present invention；
Fig. 4 is a kind of structural schematic diagram of preferred embodiment of user interface calibration equipment provided by the invention.
Specific embodiment
Hereinafter, certain exemplary embodiments are simply just described.As one skilled in the art will recognize that
Like that, without departing from the spirit or scope of the present invention, described embodiment can be modified by various different modes.
Therefore, attached drawing and description are considered essentially illustrative rather than restrictive.
The embodiment of the invention provides a kind of user interface methods of calibration.Fig. 1 is the user interface school of the embodiment of the present invention
The general frame figure of proved recipe method.As shown in Figure 1, the user interface method of calibration of the embodiment of the present invention includes: step S110, acquisition
The picture of user interface to be verified；Step S120 constructs user interface Knowledge Verification Model, can be given birth to by the training of machine learning frame
At user interface Knowledge Verification Model；The picture of the user interface to be verified of acquisition is inputted user circle by step S130
Face Knowledge Verification Model is verified, output verification result；The check results include: that the picture of the user interface to be verified is
No exception, and the anomaly classification information in the case where picture exception.
At present since there are a variety of different versions and type for software product and hardware product, for example, the fragment of android
Change than more serious, the compatibility test of app occupies biggish specific gravity in daily test job, improves the testing efficiency of compatibility
It is particularly important；Conventional compatibility test includes type, resolution ratio, android version, rom and multilingual several aspects.
Grown form of the invention is the mobile phone terminal testing tool without test platform, and plug and play is, it can be achieved that more hands
Machine carries out test operation simultaneously, these mobile phones can be respectively different vendor, the rings such as the various versions of android, different language
Border, to achieve the purpose that compatibility test.The embodiment of the present invention is suitable for different editions, different language environment, different type of machines, no
It with resolution ratio and brush machine packet, therefore overcomes and leads to the problem of compatibility test difficulty since fragmentation is serious, solve and show
There is compatibility test existing for technology to automate UI (User Interface, user interface) checking feature deficiency, people under scene
Sensory testing or the low problem of screenshot descendant's sense organ verification efficiency.
Fig. 2 is a kind of step flow chart of preferred embodiment of user interface method of calibration provided by the invention.Such as Fig. 2 institute
Show, according to the present invention a kind of embodiment of product defects detection method, the building user interface Knowledge Verification Model can pass through machine
The training of device learning framework generates user interface Knowledge Verification Model, comprising: step S210, the picture of collection user interface；Step S220,
The picture of the user interface of acquisition is labeled, the information of the mark includes: whether the picture of user interface is abnormal,
And the anomaly classification information in the case where picture exception；The anomaly classification information includes abnormal type and exception bits
It sets, the exception type includes text truncation, control covering and/or control dislocation；Step S230, uses user circle after mark
The picture training in face, to generate the user interface Knowledge Verification Model.
The embodiment of the present invention uses AI (Artificial Intelligence, artificial intelligence) scheme, passes through machine learning
Frame such as uses paddlepaddle (Parallel Distributed Deep Learning, parallel distributed depth
Practise) Open Framework, training pattern progress picture verification.PaddlePaddle is the platform for solving deep learning training problem.
PaddlePaddle provides API abundant (Application Programming for deep learning researcher
Interface, application programming interface), it can easily complete neural network configuration, the tasks such as model training.
PaddlePaddle in terms of deep learning frame, cover search, image recognition, the understanding of voice semantics recognition, sentiment analysis,
The multi-field business and technology such as machine translation, user's portrait recommendation.It supports mass data training, to cope with large-scale number
According to training.With the help of PaddlePaddle, the design of deep learning model is easy as writing pseudocode, designer
The high-level structure that model need to only be paid close attention to, without worrying any trifling bottom problem.
Specifically, after the picture for acquiring user interface to be marked, the picture of user interface is labeled, judges to use
Whether the picture at family interface is abnormal, is labeled anomaly classification information, including abnormal type (text to the picture that there is a problem of exception
This truncation, control covering, control dislocation etc.) and abnormal position (abnormal point or circle)；Not having abnormal marking is normal classification.Make
User interface Knowledge Verification Model is generated with the picture training of the user interface after mark, trained model can be directed to any one
Picture recognition goes out Exception Type and coordinate.
A kind of embodiment of product defects detection method according to the present invention, the figure of acquisition user interface to be marked
Piece, comprising: the picture of collection user interface during the test, and the anomaly classification information is excavated wherein；And/or root
It is predicted that anomaly classification information, simulate the picture of user interface corresponding with the anomaly classification information of various predictions.
Data acquisition includes that data mining (mined information from truthful data) or digital simulation (are asked according to what is be likely to occur
Topic simulates the abnormal picture come).In the first embodiment, the screenshot of collection user interface during the test, at these
It is excavated in truthful data and there is a problem of abnormal picture, various abnormal types are labeled, including text truncation, control are covered
Lid and control dislocation etc..In the second embodiment, according to predicted anomaly classification information the problem of being likely to occur, simulation is generated
Exception picture corresponding with various anomaly classification information.The mode of digital simulation can effectively make up the deficiency of data mining,
Keep labeled data more comprehensive, to reach ideal training result.
A kind of embodiment of product defects detection method according to the present invention, in the picture of the user interface to acquisition
After being labeled, and before the picture training using the user interface after mark, further includes: by the user after the mark
The picture at interface carries out data processing, and the data processing includes gray processing processing and/or normalized.It will be by data
The picture input user interface Knowledge Verification Model of user interface after reason is trained.
A kind of embodiment of product defects detection method according to the present invention, further includes: use the user after multiple marks
The picture training at interface generates user interface Knowledge Verification Model, and the user interface Knowledge Verification Model of generation is according to a use of input
The picture output verification result at family interface；Alternatively, the picture training of the user interface after being marked using multiple groups generates user interface
Knowledge Verification Model, wherein the picture of the user interface after every group of mark includes the user interface that multiple similarities are higher than predetermined threshold
Picture；The user interface Knowledge Verification Model generated is according to the picture output verification of one group of user interface of input as a result, described
Check results include that whether the picture of every user interface in the picture of this group of user interface abnormal and anomaly classification letter
Breath.
In one embodiment, N automatic test course screenshots of input, are labeled anomaly classification to problem picture
Information, including abnormal type (text truncation, control covering, control dislocation etc.) and abnormal position (abnormal point or circle), it is not different
Normal label is classification；Model is generated after training, inputs any one picture, and model can recognize that Exception Type and seat
Mark.
In another embodiment, N group automatic test course screenshot is inputted, includes similar picture in every group, this
Wherein may be abnormal with the presence of a picture or plurality of pictures, there are abnormal mark anomaly classification information, including abnormal type
(text truncation, control covering, control dislocation etc.) and abnormal position (abnormal point or circle), abnormal label is not point
Class；Model is generated after training, inputs any one group of picture, and model can recognize that whether each picture in this group of picture is abnormal
And Exception Type and coordinate.
A kind of embodiment of product defects detection method according to the present invention, after generating user interface Knowledge Verification Model,
Further include: setting loss function, adjustment model parameter minimize loss function.The essence of most machine learning algorithm is all
It is to establish Optimized model, loss function is optimized by optimal method, to trains best model.Model tuning
Process be exactly by adjusting model parameter, to improve the accuracy rate of verification.
On the other hand, the embodiment of the invention provides a kind of user interface calibration equipments.Fig. 3 is the use of the embodiment of the present invention
The general frame figure of family interface calibration equipment.As shown in figure 3, the user interface calibration equipment of the embodiment of the present invention includes: acquisition
Unit 100, for acquiring the picture of user interface to be verified；Model construction unit 200, for constructing user interface calibration mode
Type can generate user interface Knowledge Verification Model by the training of machine learning frame；Verification unit 300, is used for: by described in acquisition to
The picture of the user interface of verification inputs the user interface Knowledge Verification Model and is verified, output verification result；The verification knot
Fruit includes: that whether the picture of the user interface to be verified is abnormal, and the exception in the case where picture exception is divided
Category information.
The embodiment of the present invention is suitable for different editions, different language environment, different type of machines, different resolution and brush machine packet,
Therefore overcoming leads to the problem of compatibility test difficulty since fragmentation is serious, and it is automatic to solve previous compatibility test
Change under scene the problem that UI checking feature is insufficient, people sensory testing or screenshot descendant's sense organ verification efficiency are low.
Fig. 4 is a kind of structural schematic diagram of preferred embodiment of user interface calibration equipment provided by the invention.Such as Fig. 4 institute
Show, according to the present invention a kind of embodiment of product defects detection device, the model construction unit 200 further include: acquisition
Unit 210, for acquiring the picture of user interface to be marked；Subelement 220 is marked, for the described to be marked of acquisition
The picture of user interface is labeled, and the information of the mark includes: whether the picture of user interface is abnormal, and in the figure
Anomaly classification information in the case where piece exception；The anomaly classification information includes abnormal type and abnormal position, the exception
Type includes text truncation, control covering and/or control dislocation；Training subelement 230, for using the user interface after marking
Picture training generate user interface Knowledge Verification Model.The embodiment of the present invention uses AI scheme, by machine learning frame, such as uses
Paddlepaddle Open Framework, training pattern carry out picture verification.
A kind of embodiment of product defects detection device according to the present invention, the acquisition subelement 210 are also used to: being surveyed
The picture of collection user interface during examination, and the anomaly classification information is excavated wherein；And/or the exception according to prediction
Classification information simulates the picture of user interface corresponding with the anomaly classification information of various predictions.The mode of digital simulation
The deficiency that data mining can effectively be made up, keeps labeled data more comprehensive, to reach ideal training result.
A kind of embodiment of product defects detection device according to the present invention, the model construction unit 200 further include place
Subelement 225 is managed, is used for: the picture of the user interface after the mark being subjected to data processing, the data processing includes ash
Degreeization processing and/or normalized.
A kind of embodiment of product defects detection device according to the present invention, further includes: the model construction unit 200 is also
For: user interface Knowledge Verification Model is generated using the picture training of the user interface after multiple marks；The verification unit 300 is also
For: the picture of a user interface is inputted into the user interface Knowledge Verification Model and is verified, output verification result；Alternatively,
The model construction unit 200 is also used to: the picture training of the user interface after being marked using multiple groups generates user interface verification
Model, wherein the picture of the user interface after every group of mark includes the figure that multiple similarities are higher than the user interface of predetermined threshold
Piece；The verification unit 300 is also used to: the picture of one group of user interface being inputted the user interface Knowledge Verification Model and carries out school
Test, output verification as a result, the check results include every user interface in the picture of this group of user interface picture whether
It is abnormal, and the anomaly classification information in the case where picture exception.
A kind of embodiment of product defects detection device according to the present invention further includes model tuning unit, is used for: setting
Loss function, adjustment model parameter minimize loss function.
It is described to deposit including processor and memory in the structure of user interface calibration equipment in a possible design
Reservoir is used to store the program for supporting user interface calibration equipment to execute user interface method of calibration in above-mentioned first aspect, described
Processor is configurable for executing the program stored in the memory.
Another aspect, the embodiment of the invention provides institutes any in a kind of software testing system, including above-mentioned second aspect
The user interface calibration equipment stated.
In another aspect, the terminal includes: one or more processors the embodiment of the invention provides a kind of terminal；It deposits
Storage device, for storing one or more programs；When one or more of programs are executed by one or more of processors
When, so that one or more of processors realize the method as described in any in above-mentioned first aspect.
In another aspect, it is stored with computer program the embodiment of the invention provides a kind of computer readable storage medium,
The program realizes any method in above-mentioned first aspect when being executed by processor.
A technical solution in above-mentioned technical proposal has the following advantages that or the utility model has the advantages that embodiment provided by the invention
The checking feature for improving automatic test is particularly suitable for the serious compatibility automated test scene of fragmentation, applicable
In different editions, different language environment, different type of machines, different resolution and brush machine packet；Compared to more existing people sensory testing or
Screenshot descendant sensory testing, testing efficiency significantly improve.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.Moreover, particular features, structures, materials, or characteristics described
It may be combined in any suitable manner in any one or more of the embodiments or examples.In addition, without conflicting with each other, this
The technical staff in field can be by the spy of different embodiments or examples described in this specification and different embodiments or examples
Sign is combined.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic." first " is defined as a result, the feature of " second " can be expressed or hidden
It include at least one this feature containing ground.In the description of the present invention, the meaning of " plurality " is two or more, unless otherwise
Clear specific restriction.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing specific logical function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, Lai Zhihang function, this should be of the invention
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (such as computer based system, including the system of processor or other can be held from instruction
The instruction fetch of row system, device or equipment and the system executed instruction) it uses, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium ", which can be, any may include, stores, communicates, propagates or pass
Defeated program is for instruction execution system, device or equipment or the dress used in conjunction with these instruction execution systems, device or equipment
It sets.The more specific example (non-exhaustive list) of computer-readable medium include the following: there is the electricity of one or more wirings
Interconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable read-only memory
(CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other suitable Jie
Matter, because can then be edited, be interpreted or when necessary with other for example by carrying out optical scanner to paper or other media
Suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the invention can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, software that multiple steps or method can be executed in memory and by suitable instruction execution system with storage
Or firmware is realized.It, and in another embodiment, can be under well known in the art for example, if realized with hardware
Any one of column technology or their combination are realized: having a logic gates for realizing logic function to data-signal
Discrete logic, with suitable combinational logic gate circuit specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carries
It suddenly is that relevant hardware can be instructed to complete by program, the program can store in a kind of computer-readable storage medium
In matter, which when being executed, includes the steps that one or a combination set of embodiment of the method.Wherein device embodiments and method
Embodiment is corresponding, therefore the embodiment description of device is simpler, and associated description can refer to the embodiment of method
Description.
It, can also be in addition, each functional unit in each embodiment of the present invention can integrate in a processing module
It is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized and when sold or used as an independent product in the form of software function module, also can store in a computer
In readable storage medium storing program for executing.The storage medium can be read-only memory, disk or CD etc..
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in its various change or replacement,
These should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with the guarantor of the claim
It protects subject to range.
Claims (15)
1. a kind of user interface method of calibration characterized by comprising
Acquire the picture of user interface to be verified；
Construct user interface Knowledge Verification Model；And
The picture of the user interface to be verified of acquisition is inputted the user interface Knowledge Verification Model to verify, exports school
Test result, wherein the check results include: whether the picture of the user interface to be verified is abnormal, and in the figure
Anomaly classification information in the case where piece exception,
Wherein, the building user interface Knowledge Verification Model includes:
The picture of collection user interface；
The picture of the user interface of acquisition is labeled, the information of the mark include: user interface picture whether
It is abnormal, and the anomaly classification information in the case where picture exception；The anomaly classification information include abnormal type and
Abnormal position and the abnormal type include any one of text truncation, control covering and control dislocation and combinations thereof；
Using the picture training of the user interface after mark, to generate the user interface Knowledge Verification Model.
2. the method according to claim 1, wherein the picture of the collection user interface, comprising:
The picture of collection user interface during the test, and excavate the anomaly classification of the picture of user interface collected
Information；And/or
According to the anomaly classification information of prediction, the figure of user interface corresponding with the anomaly classification information of various predictions is simulated
Piece.
3. method according to claim 1 or 2, which is characterized in that carried out in the picture of the user interface to acquisition
After mark, and before the picture training using the user interface after mark, further includes:
The picture of user interface after the mark is subjected to data processing, wherein the data processing includes gray processing processing
And/or normalized.
4. method according to claim 1 or 2, which is characterized in that further include:
User interface Knowledge Verification Model is generated using the picture training of multiple user interfaces；And
By the user interface Knowledge Verification Model of generation according to the picture output verification result of a user interface of input.
5. method according to claim 1 or 2, which is characterized in that further include:
User interface Knowledge Verification Model is generated using the picture training of multiple groups user interface, wherein the picture packet of every group of user interface
Include picture of multiple similarities higher than the user interface of predetermined threshold；
By the user interface Knowledge Verification Model of generation according to the picture output verification of one group of user interface of input as a result, institute
Whether the picture for stating every user interface in the picture that check results include one group of user interface is abnormal, and described
Anomaly classification information in the case where picture exception.
6. method according to claim 1 or 2, which is characterized in that after generating user interface Knowledge Verification Model, also wrap
Include: setting loss function, adjustment model parameter minimize loss function.
7. a kind of user interface calibration equipment characterized by comprising
Acquisition unit, for acquiring the picture of user interface to be verified；
Model construction unit, for constructing user interface Knowledge Verification Model；
Verification unit is used for: the picture of the user interface to be verified of acquisition is inputted the user interface Knowledge Verification Model
It is verified, output verification result, wherein the check results include: whether the picture of the user interface to be verified is different
Often, and the anomaly classification information in the case where picture exception,
Wherein, the model construction unit further include:
Acquire subelement, the picture for collection user interface；
Subelement is marked, the picture for the user interface to acquisition is labeled, and the information of the mark includes: user
Whether the picture at interface is abnormal, and the anomaly classification information in the case where picture exception；The anomaly classification information
It include appointing in text truncation, control covering and control dislocation including abnormal type and abnormal position and the abnormal type
One and combinations thereof；
Training subelement, for using the picture training of the user interface after marking, to generate the user interface Knowledge Verification Model.
8. device according to claim 7, which is characterized in that the acquisition subelement is also used to:
The picture of collection user interface during the test, and excavate the anomaly classification of the picture of user interface collected
Information；And/or
According to the anomaly classification information of prediction, the figure of user interface corresponding with the anomaly classification information of various predictions is simulated
Piece.
9. device according to claim 7 or 8, which is characterized in that the model construction unit further includes processing subelement,
For: the picture of the user interface after the mark is subjected to data processing, the data processing include gray processing processing and/or
Normalized.
10. device according to claim 7 or 8, which is characterized in that
The model construction unit is also used to: generating user interface Knowledge Verification Model using the picture training of multiple user interfaces；With
And
The verification unit is also used to: the picture of a user interface inputted into the user interface Knowledge Verification Model and is verified,
Output verification result.
11. device according to claim 7 or 8, which is characterized in that
The model construction unit is also used to: user interface Knowledge Verification Model is generated using the picture training of multiple groups user interface,
In, the picture of every group of user interface includes the picture that multiple similarities are higher than the user interface of predetermined threshold；
The verification unit is also used to: the picture of one group of user interface inputted into the user interface Knowledge Verification Model and is verified,
Output verification as a result, the check results include every user interface in the picture of one group of user interface picture whether
It is abnormal, and the anomaly classification information in the case where picture exception.
12. device according to claim 7 or 8, which is characterized in that further include model tuning unit, be used for: setting loss
Function, adjustment model parameter minimize loss function.
13. a kind of software testing system, which is characterized in that including the user interface school as described in any one of claim 7-12
Experiment device.
14. a kind of terminal, which is characterized in that the terminal includes:
One or more processors；
Storage device, for storing one or more programs；
When one or more of programs are executed by one or more of processors, so that one or more of processors
Realize such as method as claimed in any one of claims 1 to 6.
15. a kind of computer readable storage medium, is stored with computer program, which is characterized in that the program is held by processor
Such as method as claimed in any one of claims 1 to 6 is realized when row.
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| CN109144868B (en) * | 2018-08-15 | 2022-02-01 | 无线生活（杭州）信息科技有限公司 | Page judgment method and device |
| CN109240931A (en) * | 2018-09-21 | 2019-01-18 | 百度在线网络技术（北京）有限公司 | Problem feedback information treating method and apparatus |
| CN111739541B (en) * | 2019-03-19 | 2021-11-05 | 上海云思智慧信息技术有限公司 | Conference assistance method and system based on voice, storage medium and terminal |
| CN110851349B (en) * | 2019-10-10 | 2023-12-26 | 岳阳礼一科技股份有限公司 | Page abnormity display detection method, terminal equipment and storage medium |
| CN111198815B (en) * | 2019-12-24 | 2023-11-03 | 中移（杭州）信息技术有限公司 | Compatibility testing method and device for user interface |
| CN111372042B (en) * | 2020-01-16 | 2021-09-03 | 上海眼控科技股份有限公司 | Fault detection method and device, computer equipment and storage medium |
| CN111797007A (en) * | 2020-06-18 | 2020-10-20 | 中国科学院软件研究所 | Automatic detection and positioning method for application program user interface defects and electronic device |
| CN113448868B (en) * | 2021-07-16 | 2022-11-01 | 网易（杭州）网络有限公司 | Game software compatibility testing method, device, equipment and medium |
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