CN108197183B - Android application-based control layout recommendation method and system - Google Patents

Android application-based control layout recommendation method and system Download PDF

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CN108197183B
CN108197183B CN201711425492.9A CN201711425492A CN108197183B CN 108197183 B CN108197183 B CN 108197183B CN 201711425492 A CN201711425492 A CN 201711425492A CN 108197183 B CN108197183 B CN 108197183B
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黄姝仪
陈湘萍
林格
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Sun Yat Sen University
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Abstract

The embodiment of the invention discloses a control layout recommendation method and a control layout recommendation system based on android application. Wherein, the method comprises the following steps: acquiring a data set of a dynamic interface during operation, analyzing and extracting to form a control attribute database, and then establishing an index; carrying out matching retrieval, extraction and discretization processing on keywords provided by a user to obtain an alternative layout scheme; and scoring the alternative layout schemes, and recommending the corresponding layout to the user according to the score from high to low. By implementing the embodiment of the invention, the method for quantifying the representativeness and stability of the control layout recommendation is realized, so that the user can more clearly know the recommendation result according to the two indexes and the comprehensive score, and the method has a better auxiliary effect on designers than that only frequency recommendation is used.

Description

Android application-based control layout recommendation method and system
Technical Field
The invention relates to the technical field of information identification processing, android application and layout recommendation, in particular to a control layout recommendation method and system based on the android application.
Background
After the rapid development of the mobile internet, mobile applications are gradually becoming daily needs of people, wherein android applications account for a large proportion of the mobile application market. The application prototype is generated quickly, and development efficiency is improved to meet the requirements of a plurality of development teams. In android application development, interface design directly affects the interaction experience of users, and therefore, the participation of designers is often required. At present, for example, a 'dragging type' interface development mode is provided in an Android Studio development environment, and a corresponding code can be automatically generated only by dragging a control to a certain position of a canvas. This allows designers without coding knowledge to generate interfaces quickly. The large amount of data accumulated by the android application market of the android platform provides reference possibility for android application designers and developers. However, acquiring design cases and analyzing trends from the cases is a time-consuming process, and therefore, a data-based control layout recommendation can reduce the time for a designer to collect cases and analyze data, and the designer can be dedicated to the process of creating and organizing inspiration.
At present, an automatic crawling and management platform for interface data includes a retrieval platform for establishing webpage design, a resource library related to the webpage design is generated by analyzing a crawled webpage, and a JSOND-based query API is provided. There is also a search platform for interface design for android applications. Existing researchers have also proposed statistical analysis methods to provide insight into design trends and to provide design decision assistance to user interface designers. For the analysis of the interface data, the analysis of the control with the highest use frequency and the combination of the controls in the android application, the relationship between the interface complexity of the application and the download amount and the score, and the change trend of the design mode in version updating are related.
There are currently two approaches to layout recommendation for controls, the first being model-based and the second being data-based. The model-based method recommends that control layout effect is mainly influenced by the design specification of selection, and the design mode of embodiment is more single, and to how to choose the design rule to need the designer to have abundant design experience or priori knowledge, it is higher to the beginner that experience is little to use the degree of difficulty. The data-based method recommends that the control layout is mainly influenced by the collected data, and only collects the application with good user feedback by setting the filtering condition, so that the design mode finally embodied from the data is market-certified and has high acceptance. And the design modes are currently accepted by users and accord with the use habits of the users.
Model-based approaches are generally based on object-oriented ideas, building an inheritance hierarchy that establishes classes according to the desired functionality of the system and the characteristics of the involved personnel, and representing the behavior of each class by defining attributes or methods. On the basis of well-constructed system model, the interface control and the corresponding function which need to be defined are mapped, and then the design guidance defined by experts or human-computer interaction communities is applied as heuristic rules to automatically layout the control.
The data-based method is recommendation based on historical design preference, and under the condition that personal preference input of designers is not considered, the basic method is to count the occurrence frequency of a given layout control at different positions based on the idea that visibility of a good layout in the market is higher, and select the first N positions with the highest frequency as recommendation options. Therefore, the invention adopts a data-based method to recommend the control layout.
The design mode embodied by the model-based method is single, and the designer needs to have abundant design experience or prior knowledge for selecting the design rule, so that the difficulty in using the model-based method for less-experienced beginners is high. The layout of the recommendation control based on the data method only utilizes basic frequency information in the data, and three problems cannot be solved, wherein the first problem is that the homogeneity of a plurality of recommendation layouts cannot be solved, and very similar design intents of general layouts with consistent sources cannot be solved; the second problem is that the representativeness of the recommended layouts cannot be judged, and when the frequency difference between the recommended layouts is not large, the recommended result of the highest frequency is not necessarily representative; the third problem is that the reliability of the recommended layout of the control cannot be judged, and if the layout with the highest occurrence frequency is used as the recommended layout of the control, the recommended result may be unstable due to the randomness of the collected data when the frequencies of different layouts of the control are similar.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a control layout recommendation method and system based on android application, which are used for scoring layout data and sequencing the layout data to obtain the recommended layout of top N. Therefore, the technical problems to be solved by the invention include how to increase the recommendation efficiency for interface data processing, what indexes are selected to quantify the representativeness and stability of the layout scheme, and how to solve the problem of the homogeneity of the recommended layout.
In order to solve the above problems, the present invention provides a control layout recommendation method based on an android application, where the method includes:
acquiring a data set of a dynamic interface during operation, analyzing and extracting to form a control attribute database, and then establishing an index;
carrying out matching retrieval, extraction and discretization processing on keywords provided by a user to obtain an alternative layout scheme;
and scoring the alternative layout schemes, and recommending the corresponding layout to the user according to the score from high to low.
Preferably, the analyzing and extracting process specifically includes:
acquiring a single interface data set during operation, analyzing and extracting to obtain the attributes of all controls;
analyzing and classifying the attributes of all the controls, and extracting and obtaining control attributes related to control text information, control types and control layout;
extracting keywords from the control text information to serve as retrieval keywords of a control attribute database;
for the control without text information, taking the control type as a retrieval keyword;
meanwhile, the control type and the keywords of the control text information are combined to be used as retrieval keywords;
and combining the three conditions to form a control attribute database, and establishing a corresponding index.
Preferably, the step of extracting the keywords from the control text information is to model the text information of all the controls by using an LDA model so as to obtain keyword distribution in the topics, and then select a plurality of keywords with higher frequency from the topics according to the topics of the text as keyword indexes of the retrieval controls.
Preferably, the matching retrieval, extraction and discretization processing of the keywords provided by the user specifically includes:
according to type keywords or control text information keywords for describing the type of the target control provided by a user, searching corresponding indexes through a matching control attribute database to obtain a layout data set related to the target control;
discretizing the layout data set of the target control to obtain a layout cluster set, wherein each layout cluster is an alternative layout scheme;
preferably, the discretization processing is performed on the layout data set of the target control, and the DBSCAN clustering algorithm is mainly used, so that the domain radius is set to 10, and the density is 1. The method comprises the steps of clustering the layout data of the close target controls, calling the subset of the layout data of the target controls which are clustered into one class as a layout cluster, and then updating the layout mean value of the layout cluster into the layout of all samples in the cluster, so that the samples of the layout data of the close target controls are reduced to be in the same layout condition, and the purpose of discretizing the layout data set is achieved.
Preferably, the obtained layout cluster set is visually displayed to a user through a thermodynamic diagram, wherein one layout of the target control is represented by a rectangular box, the perimeter of the rectangular box is the number of the pixel points occupied by the rectangular box, the brightness of the pixel points reflects the frequency of the rectangular box repeatedly appearing at the pixel points, and if a certain area in the diagram is brighter, the more frequent the layout of the area is selected.
Preferably, the scoring the alternative layout schemes, and recommending the corresponding layout to the user according to the score from high to low specifically includes:
obtaining alternative layout schemes, and carrying out quantitative calculation processing to obtain scores of the alternative layout schemes;
and obtaining the score of each alternative layout scheme, sequencing each alternative layout scheme according to the score from high to low, obtaining a plurality of previous alternative layout schemes and recommending the previous alternative layout schemes to a user.
Preferably, the quantitative calculation processing is a scoring method using the representativeness and stability of the quantitative layout scheme. By LAAnd (4) representing the layout (x, y, h and w) of the target control A, wherein x and y are coordinates of the target control A, and h and w are the length and width of the target control A. This is a random variable in four dimensions and,
Figure GDA0003135177810000041
represents LAThe sample set of the layout cluster of (1). The layout cluster after clustering is
Figure GDA0003135177810000042
A subset ofiAnd (4) showing. S represents and aiAll cluster sets that overlap. a ispIs represented byiThe largest number of all clusters that overlap. E (a)i) Denotes aiAverage layout of clusters, Min (| a)is| represents the minimum value of the number of clusters in S, Max (| a)isI) represents the maximum value of the number of clusters in S, then cluster aiIs ofiThis can be calculated by:
CSi＝(|ap|-Min(|ais|))/Max(|ais|)-Min(|ais|)
preferably, the quantization calculation process is to measure the stability by the inverse of the information entropy measure in a scoring method for quantifying the representativeness and stability of the layout scheme. PiIndicating the frequency of occurrence of a certain layout cluster, i.e. the number of samples of the cluster as a proportion of the total layout sample set. The layout stability H of the target control is calculated by:
H＝1/∑Pi*logPi
for a certain layout cluster, the higher its local saliency is, the lower the overall randomness is, the more representative it is, the higher the score should be, so the final scoring formula is as follows:
Score＝CSi+H
correspondingly, the invention provides a control layout recommendation system based on an android application, which comprises:
the preprocessing module is used for acquiring a data set of a dynamic interface during operation, and analyzing and extracting the data set;
the retrieval module is used for performing matching retrieval, extraction and discretization processing on the keywords provided by the user;
the grading module is used for grading the alternative layout scheme;
and the recommending module is used for recommending the corresponding layout to the user according to the grade from high to low.
Preferably, the system further comprises a presentation module for visually presenting the set of discretized layout clusters to the user through thermodynamic diagrams.
Preferably, the preprocessing module comprises:
the analysis unit is used for acquiring a single interface data set during operation, analyzing and extracting attributes of all controls;
the classification unit is used for analyzing and classifying the attributes of all the controls and extracting and obtaining control text information, control types and control attributes related to control layout;
the extraction keyword unit is used for extracting keywords from the control text information to serve as retrieval keywords of the control attribute database;
and the index establishing unit is used for combining the control text information, the control type and the control attribute related to the control layout to form a control attribute database and establishing a corresponding index.
In the embodiment of the invention, the method for quantifying the representativeness and stability of the control layout recommendation is realized, so that a user can more clearly know the recommendation result according to the two indexes and the comprehensive score, and the method has a better auxiliary effect on designers than that only frequency recommendation is used.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a control layout recommendation method based on an android application according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of distributed original heating power of a target control and a schematic diagram of clustered distributed heating power in an embodiment of the present invention;
fig. 3 is a schematic structural composition diagram of a control layout recommendation system based on an android application according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flowchart of a control layout recommendation method based on an android application according to an embodiment of the present invention, and as shown in fig. 1, the method includes:
s1, acquiring a data set of the dynamic interface during operation, analyzing, extracting, forming a control attribute database, and establishing an index;
s2, performing matching retrieval, extraction and discretization processing on the keywords provided by the user to obtain an alternative layout scheme;
and S3, scoring the alternative layout schemes, and recommending the corresponding layout to the user according to the score from high to low.
S1 further includes:
s11, acquiring a single interface data set during operation, analyzing, extracting and acquiring attributes of all controls;
s12, analyzing and classifying the attributes of all controls, extracting and obtaining control text information, control types and control attributes related to control layout;
s13, extracting keywords from the control text information to serve as retrieval keywords of the control attribute database;
for the control without text information, taking the control type as a retrieval keyword;
meanwhile, the control type and the keywords of the control text information are combined to be used as retrieval keywords;
and S14, combining the three conditions to form a control attribute database, and establishing a corresponding index.
Specifically, the data set of the runtime dynamic interface in S1 is derived from data of the uniform speed driving interface obtained from the black box test, and includes: the method comprises the steps of instant screenshot of a dynamic interface during running, hierarchical structure data of the interface and attributes of each control.
In S12, the control attributes are analyzed and classified, and the control attributes are mainly classified into two types:
one type is attributes related to the control functionality, including the type of the control, such as common button, textview, clickable property of the control, visibility of the control, text content of the control, etc.; since the type of control generally determines the clickability and visibility of the control, we index the control with the control type and textual information.
One type is control properties related to the layout of the control, including coordinates, length, width.
In a specific embodiment, the control type name collected from the runtime is generally a class name combination with hierarchical properties, such as "android widget Button", and the last class name can represent the type and function of the control, and is taken and lowercase to be used as a type index of the control for storage and retrieval. The text of the control is typically short text, often even just a word, so a few keywords are sufficient to express the semantic information of the control.
In S13, the extracting the keywords from the control text information is to model the text information of all controls by using the LDA model, so as to obtain the keyword distribution in the topics, and then select a plurality of keywords with higher frequency from the topics as the keyword indexes of the retrieval controls according to the topics possessed by the text.
S2 further includes:
s21, according to type keywords or control text information keywords for describing the type of the target control provided by the user, searching corresponding indexes through a matching control attribute database to obtain a layout data set related to the target control;
s22, discretizing the layout data set of the target control to obtain a layout cluster set, wherein each layout cluster is an alternative layout scheme;
the control corresponding to the index retrieved by matching the control attribute database in S21 is the target control sought by the user. And after the retrieval is finished, extracting the layout data of each record to form a layout data set of the target control.
Discretizing the layout data set of the target control in S22, mainly using a DBSCAN clustering algorithm, and setting the field radius to be 10 and the density to be 1 in order to make the clustering effect more compact. The neighborhood radius determines the range of each point searching for the same cluster, and the density determines whether the layout data set sample domain of a target control can be considered as a cluster class, namely the layout data set sample size threshold of the target control forming the cluster. These two parameters need to be specific to the sample data, but there is a principle that any data cannot be filtered as noise, so the density parameter should be lower, and the density is set to 1. In addition, in order to make the clustering effect more compact, the radius of the neighborhood should be set to be 10 if it is set to be low.
The method comprises the steps of clustering the layout data of the close target controls, calling the subset of the layout data of the target controls which are clustered into one class as a layout cluster, and then updating the layout mean value of the layout cluster into the layout of all samples in the cluster, so that the samples of the layout data of the close target controls are reduced to be in the same layout condition, and the purpose of discretizing the layout data set is achieved.
Further, the layout cluster set obtained in S22 is visually displayed to the user through thermodynamic diagram, wherein a layout of the target control is represented by a rectangular box, the perimeter of the rectangular box is the number of pixels occupied by the rectangular box, and the brightness of the pixels reflects the frequency of the rectangular box appearing repeatedly at the pixels, as shown in fig. 2, if a certain area in the diagram is brighter, the more frequently the layout of the area is selected.
S3 further includes:
s31, acquiring alternative layout schemes, and performing quantitative calculation processing to obtain scores of the alternative layout schemes;
and S32, obtaining the score of each alternative layout scheme, sorting each alternative layout scheme according to the score from high to low, obtaining a plurality of previous alternative layout schemes and recommending the previous alternative layout schemes to the user.
The quantization calculation process in S31 is a scoring method that uses the representativeness and stability of the quantization layout scheme. The representativeness of the quantitative layout scheme is to solve the problem that whether the layout with the highest frequency has representativeness under the condition that the occurrence frequencies are close to each other cannot be judged according to the recommended layout with the highest frequency. Representativeness is particularly concentrated in one area in some layouts and occurs more frequently. We use local distribution saliency as a representative metric. The reason for considering the local distribution saliency is that different layouts of the control elements are often nested in a region, and if the number of clusters in a region is high compared with the number of other clusters, the control elements are more representative in the region.
By LAAnd (4) representing the layout (x, y, h and w) of the target control A, wherein x and y are coordinates of the target control A, and h and w are the length and width of the target control A. This is a random variable in four dimensions and,
Figure GDA0003135177810000081
represents LAThe sample set of the layout cluster of (1). The layout cluster after clustering is
Figure GDA0003135177810000082
A subset ofiAnd (4) showing. S represents and aiAll cluster sets that overlap. a ispIs represented byiThe largest number of all clusters that overlap. E (a)i) Denotes aiAverage layout of clusters, Min (| a)is| represents the minimum value of the number of clusters in S, Max (| a)isI) represents the maximum value of the number of clusters in S, then cluster aiIs ofiThis can be calculated by:
CSi＝(|ap|-Min(|ais|))/Max(|ais|)-Min(|ais|)
the purpose of quantifying the stability of the layout of the target control is to solve the problem that the reliability of the alternative layout scheme cannot be judged. The stability is mainly reflected in that the layout of the target control has a relatively stable centralized trend, and conversely, if the layout of the target control is distributed in many different coordinates and shapes, and the frequency of the target control is not relatively dispersed, the target control is unstable and random.
Since the randomness of the whole is easier to quantify, the information entropy and the occurrence frequency of different layout clusters are adopted, and the stability is inversely proportional to the randomness, the stability of the layout distribution of the whole layout data set of the control is measured by the reciprocal of the information entropy.
We measure the randomness of the distribution of layout clusters by the entropy of information. The larger the entropy, the greater the randomness. Since stability is inversely proportional to randomness, stability is measured by the inverse of entropy. PiIndicating the frequency of occurrence of a certain layout cluster, i.e. the number of samples of the cluster as a proportion of the total layout sample set. The layout stability H of the target control is calculated by:
H＝1/∑Pi*logPi
for a certain layout cluster, the higher its local saliency is, the lower the overall randomness is, the more representative it is, the higher the score should be, so the final scoring formula is as follows:
Score＝CSi+H
calculating scores corresponding to all layout clusters of the target control, then sorting the scores, and recommending the top layout clusters serving as recommended layout schemes to the user.
Correspondingly, the embodiment of the invention discloses a control layout recommendation system based on android application, which comprises the following steps:
the preprocessing module is used for acquiring a data set of a dynamic interface during operation, and analyzing and extracting the data set;
the retrieval module is used for performing matching retrieval, extraction and discretization processing on the keywords provided by the user;
the grading module is used for grading the alternative layout scheme;
and the recommending module is used for recommending the corresponding layout to the user according to the grade from high to low.
The system further comprises a presentation module, which is used for visually presenting the scattered layout cluster set to a user through thermodynamic diagrams.
The pre-processing module further comprises:
the analysis unit is used for acquiring a single interface data set during operation, analyzing and extracting attributes of all controls;
the classification unit is used for analyzing and classifying the attributes of all the controls and extracting and obtaining control text information, control types and control attributes related to control layout;
the extraction keyword unit is used for extracting keywords from the control text information to serve as retrieval keywords of the control attribute database;
and the index establishing unit is used for combining the control text information, the control type and the control attribute related to the control layout to form a control attribute database and establishing a corresponding index.
The functions of each functional module in the system embodiment of the present invention may refer to the flow processing in the method embodiment of the present invention, and are not described herein again.
In the embodiment of the invention, the implementation of the embodiment of the invention realizes a method for quantifying the representativeness and stability of the control layout recommendation, so that a user can more clearly know the recommendation result according to the two indexes and the comprehensive score, and the method has a better auxiliary effect on designers than that of only frequency recommendation.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
In addition, the detailed description is given above on the control layout recommendation method and system based on the android application provided in the embodiment of the present invention, and a specific example is applied herein to explain the principle and implementation manner of the present invention, and the description of the above embodiment is only used to help understanding the method and core ideas of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (5)

1. A control layout recommendation method based on android application is characterized by comprising the following steps:
acquiring a data set of a dynamic interface during operation, analyzing and extracting to form a control attribute database, and then establishing an index;
carrying out matching retrieval, extraction and discretization processing on keywords provided by a user to obtain an alternative layout scheme;
grading the alternative layout schemes, and recommending corresponding layouts to the user according to the grades from high to low;
wherein, the analyzing and extracting treatment specifically comprises:
acquiring a single interface data set during operation, analyzing and extracting to obtain the attributes of all controls;
analyzing and classifying the attributes of all the controls, and extracting and obtaining control attributes related to control text information, control types and control layout;
extracting keywords from the control text information to serve as retrieval keywords of a control attribute database;
for the control without text information, taking the control type as a retrieval keyword;
meanwhile, the control type and the keywords of the control text information are combined to be used as retrieval keywords;
combining the three conditions to form a control attribute database, and establishing a corresponding index;
the matching retrieval, extraction and discretization processing of the keywords provided by the user specifically comprises the following steps:
according to type keywords or control text information keywords for describing the type of the target control provided by a user, searching corresponding indexes through a matching control attribute database to obtain a layout data set related to the target control;
discretizing the layout data set of the target control to obtain a layout cluster set, wherein each layout cluster is an alternative layout scheme;
wherein, the scoring the alternative layout schemes and recommending the corresponding layout to the user according to the score from high to low specifically comprises:
obtaining alternative layout schemes, and carrying out quantitative calculation processing to obtain scores of the alternative layout schemes;
and obtaining the score of each alternative layout scheme, sequencing each alternative layout scheme according to the score from high to low, obtaining a plurality of previous alternative layout schemes and recommending the previous alternative layout schemes to a user.
2. The android-application-based control layout recommendation method of claim 1, wherein the discretization of the layout data set of the target control is performed by mainly using a DBSCAN clustering algorithm, setting a domain radius to 10 and a density to 1; the method comprises the steps of clustering the layout data of the close target controls, calling the subset of the layout data of the target controls which are clustered into one class as a layout cluster, and then updating the layout mean value of the layout cluster into the layout of all samples in the cluster, so that the samples of the layout data of the close target controls are reduced to be in the same layout condition, and the purpose of discretizing the layout data set is achieved.
3. The android application-based control layout recommendation method of claim 1, wherein the quantitative calculation processing is a scoring method for representativeness and stability of a quantitative layout scheme; by LARepresenting the layout (x, y, h, w) of the target control A, which is a four-dimensional random variable, wherein x and y are coordinates of the target control A, and h and w are the length and width of the target control A;
Figure FDA0003160194610000021
represents LAThe layout cluster sample set of (1); the layout cluster after clustering is
Figure FDA0003160194610000022
A subset ofiRepresents; s represents and aiAll cluster sets that overlap; a ispIs represented byiThe largest number of all clusters that overlap; e (a)i) Denotes aiAverage layout of clusters, Min (| a)is| represents the minimum value of the number of clusters in S, Max (| a)isI) represents the maximum value of the number of clusters in S, then cluster aiIs ofiThis can be calculated by:
CSi＝(|ap|-Min(|ais|))/Max(|ais|)-Min(|ais|)。
4. the android application-based control layout recommendation method of claim 3, wherein the quantitative calculation processing is carried out by measuring stability, P, by inverse of information entropy measure in a scoring method for representing and stability of quantitative layout schemeiRepresenting the occurrence frequency of a certain layout cluster, namely the proportion of the number of samples of the cluster to the total layout sample set, and calculating the layout stability H of the target control by the following method:
H＝1/ΣPi*log Pi
for a certain layout cluster, the higher its local saliency is, the lower the overall randomness is, the more representative it is, the higher the score should be, so the final scoring formula is as follows:
Score＝CSi+H。
5. an android application-based control layout recommendation system, the system comprising:
the preprocessing module is used for acquiring a data set of a dynamic interface during operation, and analyzing and extracting the data set;
the retrieval module is used for performing matching retrieval, extraction and discretization processing on the keywords provided by the user;
the grading module is used for grading the alternative layout scheme;
the recommendation module is used for recommending corresponding layouts to the user according to the grades from high to low;
the display module is used for visually displaying the scattered layout cluster set to a user through thermodynamic diagrams;
wherein the preprocessing module comprises:
the analysis unit is used for acquiring a single interface data set during operation, analyzing and extracting attributes of all controls;
the classification unit is used for analyzing and classifying the attributes of all the controls and extracting and obtaining control text information, control types and control attributes related to control layout;
the extraction keyword unit is used for extracting keywords from the control text information to serve as retrieval keywords of the control attribute database;
and the index establishing unit is used for combining the control text information, the control type and the control attribute related to the control layout to form a control attribute database and establishing a corresponding index.
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