CN116257459A - Form UI walk normalization detection method and device - Google Patents

Form UI walk normalization detection method and device Download PDF

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CN116257459A
CN116257459A CN202310546298.5A CN202310546298A CN116257459A CN 116257459 A CN116257459 A CN 116257459A CN 202310546298 A CN202310546298 A CN 202310546298A CN 116257459 A CN116257459 A CN 116257459A
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walkthrough
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张辉
吴正中
张云飞
杨春成
刘敏青
刘喆
王晓东
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Beijing Urban Construction Intelligent Control Technology Co ltd
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    • G06F11/36—Preventing errors by testing or debugging software
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    • G06F11/3616—Software analysis for verifying properties of programs using software metrics
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

A form UI walk checking standardization detection method and device, through regarding the content related to standardization as the training set, regard the content to be detected as the test set, form a sub form UI detection method, and form UI interface integral scoring method, and give and modify the suggestion, reduce the repeated work of the tester; therefore, the invention can overcome the defects of the prior art, automatically check the human-computer interface specifications specified in the prior form UI design, reduce the workload of testers and better improve the working efficiency.

Description

Form UI walk normalization detection method and device
Technical Field
The invention relates to the technical field of automatic detection, in particular to a method and a device for detecting normalization of form UI walk.
Background
In the prior art, a lot of automatic testing methods are available, for example, an automatic checking method for associating all components through clear height and position relation based on IFC standard is proposed in paper BIM data standardization checking method research based on IFC standard, and the method is a checking method for BIM model in building industry.
In the patent "method for realizing general inspection based on automatic test virtual machine technology", it is proposed to realize the function of testing by an automatic script, and realize extensible general inspection by deploying a keyword script in a virtual machine, focusing on the development related content of the underlying hardware.
The automatic test method proposed in the patent UI automatic test method and device mainly tests the functions of UI controls, such as the test of completing man-machine interaction by driving a browser through a script and simulating the operation of a mouse and a keyboard.
A test method for pixel level comparison of an HTML page and a UI design page is mentioned in a patent 'an automatic test method for the reduction degree of a UI interface'.
An error processing method for absolute coordinates of UI effect picture elements is provided in a walking method, a device, equipment and a medium of UI design.
In the method, the existing automatic test mainly comprises man-machine interaction test, such as mouse click, keyboard input and the like, mainly comprises functional test, and the form interface typesetting normalization check does not find out related cases of the automatic test. The communication cost exists between the interface designer and the tester, the tester needs to test again after each interface modification, the tester needs to respond to the design requirement piece by piece through the specification in the whole process, and an automatic detection method is lacked.
Therefore, in view of the above-mentioned drawbacks, the designer of the present invention, through intensive research and design, combines experience and achievements of related industries for many years, and researches and designs a form UI walk normalization detection method and device to overcome the above-mentioned drawbacks.
Disclosure of Invention
The invention aims to provide a method and a device for detecting the normalization of form UI walk check, which overcome the defects of the prior art, automatically check the human-computer interface normalization specified in the prior form UI design, reduce the workload of testers and better improve the working efficiency.
In order to achieve the above purpose, the invention discloses a form UI walk-checking normalization detection method, which is characterized by comprising the following steps:
step 1: inputting a specification and defining a form UI specification category;
step 2: forming the characteristics of different types of forms UI according to the specifications;
step 3: acquiring a form UI interface to be analyzed;
step 4: carrying out standard conformity classification on each sub-form UI and giving the accuracy of classification;
step 5: scoring the whole list UI interface;
step 6: and outputting reasonable modification suggestions according to the scoring result.
Wherein: step 1 further comprises: the designer inputs the specification, defines the form category, takes the form alignment mode as an example, inputs different alignment modes, and gives corresponding patterns thereof as a training set.
Wherein: step 2 further comprises: form UI features of different types according to the specification, extract feature quantity in the input specification style according to the following method, firstly, gridding the input specification style to form a grid chart with an abscissa and an ordinate, extracting grid elements, representing with black and white (1, 0), and enabling the grid size to be corresponding to the size of a single character.
Wherein: extracting the distribution of the number of words in different patterns in X and Y directions, and recording as
Figure SMS_1
，
Figure SMS_2
Wherein->
Figure SMS_3
Includes statistics of maximum value, minimum value, median and quartile of each sample in X direction, and is marked as +.>
Figure SMS_4
And normalize to->
Figure SMS_5
Similarly, the->
Figure SMS_6
Comprises and->
Figure SMS_7
Corresponding to the feature quantity.
Wherein: separately calculate
Figure SMS_8
Correlation of characteristic quantities of (a)>
Figure SMS_9
The correlation of the characteristic quantities of (a) by the formula +.>
Figure SMS_10
For->
Figure SMS_11
Characteristic quantity +.>
Figure SMS_12
And reserving the characteristic quantity as the subsequent input.
Wherein: step 3 further comprises: and (3) acquiring a form UI interface to be analyzed, taking all the sub-forms UI contained in the form UI interface as a test set, extracting the characteristics in the step (2), and inputting the extracted characteristic quantity into the step (4) for category judgment.
Wherein: and 4, classifying the form to be checked by adopting a classification algorithm, wherein: the distance calculation formula is as follows formula 1:
Figure SMS_13
（1）
the center point calculation formula is as follows formula 2:
Figure SMS_14
（2）
the distance between the test set and the center point of each category and the probability calculation formula of the category are as follows:
Figure SMS_15
（3）
Figure SMS_16
（4）
Figure SMS_17
（5）
firstly training the feature quantity of the training set in the step 1 through the algorithm to obtain a sample center point, and then inputting the feature quantity of the testing set in the step 3 into the algorithm to obtain the category and the probability P thereof in the category.
Wherein: step 5 further comprises: scoring is carried out on a UI interface of a form to be analyzed, the scoring is based on the principle of overall consistency, and the specific scoring method is as follows:
counting the classified sub-forms UI, wherein the most classified sub-forms are as follows
Figure SMS_18
The probability corresponding to the method is +.>
Figure SMS_19
The overall form UI score is: />
Figure SMS_20
N is the number of all sub-forms in the UI form.
Wherein: in the step 6, when the score is more than 90 minutes, the consistency of the form is considered to be better, great modification is not needed, only sub-forms which are not classified into a plurality of categories in the classification process are needed to be checked, when the score is 60-90 minutes, the form is considered to have the consistency problem, manual review is needed, and when the score is less than 60 minutes, the algorithm is considered to be inapplicable, and manual detection is needed to be changed.
The invention also discloses a form UI walk normalization detection device which realizes the method.
As can be seen from the above, the form UI walk normalization detection method and device of the invention have the following effects:
1. the invention is different from UI interface function test, is a normalization test of the form UI, forms a sub form UI detection method and a form UI interface integral scoring method by taking contents related to the normalization as a training set and taking contents to be detected as a test set, gives a modification suggestion and reduces repeated work of testers.
2. From the aspect of standardization, the invention realizes testing from standardization item to output result, and detects the UI of the form, thereby greatly improving the efficiency.
The details of the present invention can be found in the following description and the accompanying drawings.
Drawings
FIG. 1 shows a flow chart of a form UI walk normalization detection method of the present invention.
Fig. 2 shows a schematic diagram of the present invention for its style as a training set.
Fig. 3A shows a diagram of the invention before extraction, and fig. 3B shows a diagram of the invention after extraction of grid elements.
FIG. 4 shows a schematic diagram of the sorting of forms in the present invention.
Detailed Description
Referring to FIG. 1, a flow chart of a form UI walk normalization detection method of the present invention is shown.
The form UI walk normalization detection method comprises the following steps: inputting a specification and defining a form UI specification category; forming the characteristics of different types of forms UI according to the specifications; acquiring a form UI interface to be analyzed; carrying out standard conformity classification on each sub-form UI and giving the accuracy of classification; scoring the whole list UI interface; and outputting reasonable modification suggestions according to the scoring result.
Referring to fig. 1, the present invention includes the steps of:
step 1: inputting a specification and defining a form UI specification category;
step 2: forming the characteristics of different types of forms UI according to the specifications;
step 3: acquiring a form UI interface to be analyzed;
step 4: carrying out standard conformity classification on each sub-form UI and giving the accuracy of classification;
step 5: scoring the whole list UI interface;
step 6: and outputting reasonable modification suggestions according to the scoring result.
Specifically, the details of each step are as follows:
step 1: the designer inputs the specification, defines the form category, takes the form alignment mode as an example, inputs different alignment modes, and gives corresponding patterns thereof as a training set, as shown in fig. 2: title left-justified, title right-justified, overall left-justified, top-justified.
Step 2: according to the specification, form UI features of different categories are formed, feature quantities in an input specification pattern are extracted as input features of a subsequent step 4 according to the following method, firstly, the input specification pattern is gridded to form a grid chart with an abscissa and an ordinate, grid elements are extracted, black and white (1, 0) are used for representing, as shown in extraction of FIG. 3A in FIG. 3B, the grid size corresponds to the size of a single character.
Extracting the distribution of the number of words in different patterns in X and Y directions, and recording as
Figure SMS_27
，
Figure SMS_29
Wherein->
Figure SMS_31
Includes statistics of maximum value, minimum value, median, quartile, etc. of each sample in X direction, and is marked as +.>
Figure SMS_22
And normalize to->
Figure SMS_28
Similarly, the->
Figure SMS_30
Comprises and->
Figure SMS_32
Corresponding to the feature quantity. Separately calculate->
Figure SMS_21
Correlation of characteristic quantities of (a)>
Figure SMS_23
The correlation of the characteristic quantities of (a) by the formula +.>
Figure SMS_25
For->
Figure SMS_26
Characteristic quantity +.>
Figure SMS_24
And reserving the characteristic quantity as the subsequent input.
In the above-mentioned formula(s),
Figure SMS_36
representing the word number distribution of the 1 st form to the m-th form in the X direction,/or->
Figure SMS_38
Representing the word number distribution of the 1 st form to the m-th form in the Y direction. />
Figure SMS_40
Representing the word count distribution of the ith form in the X direction, +.>
Figure SMS_35
Representing the word count distribution of the ith form in the Y-direction, +.>
Figure SMS_39
Statistics representing the distribution of the number of words in the X direction of the ith formQuantity (S)>
Figure SMS_42
Statistic normalization value representing the distribution of the number of words of the ith form in the X direction, +.>
Figure SMS_44
For calculating the relationship between different features in the ith form, 1<j<n，/>
Figure SMS_33
For pressing->
Figure SMS_37
Ordering of->
Figure SMS_41
Rank correlation coefficient of statistics>
Figure SMS_43
And the serial number difference value between every two features after the sequence is ordered according to the normalized value. />
Figure SMS_34
As a default, the adjustment can be made according to the actual.
Step 3: and (3) acquiring a form UI interface to be analyzed, taking all the sub-forms UI contained in the form UI interface as a test set, extracting the characteristics in the step (2), and inputting the extracted characteristic quantity into the step (4) for category judgment.
Step 4: the form to be inspected is classified by using a classification algorithm, the specific classification algorithm is shown in fig. 4.
Wherein: the distance calculation formula is as follows formula 1:
Figure SMS_45
（1）
the center point calculation formula is as follows formula 2:
Figure SMS_46
（2）
the distance between the test set and the center point of each category and the probability calculation formula of the category are as follows:
Figure SMS_47
（3）
Figure SMS_48
（4）/>
Figure SMS_49
（5）
training the feature quantity of the training set in the step 1 through the algorithm to obtain a sample center point, and inputting the feature quantity of the testing set in the step 3 into the algorithm to obtain a possible category and probability P thereof in the category.
D in the formula (1) is the distance between each feature quantity in the two forms.
In the formula (2)
Figure SMS_50
Center point coordinates obtained after distance calculation for all forms, +.>
Figure SMS_51
、
Figure SMS_52
X-coordinate for two points furthest apart, +.>
Figure SMS_53
、/>
Figure SMS_54
Is the Y coordinate of the two points furthest apart.
In the formula (3)
Figure SMS_55
For testing set and center point distanceLeave, go up>
Figure SMS_56
And feature quantity of the test set after feature extraction, normalization and correlation analysis.
In the formula (4)
Figure SMS_57
Is the probability that the ith feature is near the center point.
In the formula (5)
Figure SMS_58
Is the probability of the ith form being near the center point.
Step 5: scoring is carried out on a UI interface of a form to be analyzed, the scoring is based on the principle of overall consistency, and the specific scoring method is as follows:
counting the classified sub-forms UI, wherein the most classified sub-forms are as follows
Figure SMS_59
The probability corresponding to the method is +.>
Figure SMS_60
The overall form UI score is: />
Figure SMS_61
N is the number of all sub-forms in the UI form.
Step 6: and analyzing the consistency according to the score obtained in the scoring module and giving reasonable advice. When the score is above 90 minutes, the form consistency is considered to be better, great modification is not needed, only sub-forms which are not classified into most categories in the classification process are needed to be checked, when the score is 60-90 minutes, the form is considered to have consistency problems, manual review is needed, when the score is below 60 minutes, the algorithm is considered to be inapplicable, and manual detection is needed.
The invention also relates to a form UI walk normalization detection device which can realize the method.
It follows that the advantages of the invention are:
1. the invention is different from UI interface function test, is a normalization test of the form UI, forms a sub form UI detection method and a form UI interface integral scoring method by taking contents related to the normalization as a training set and taking contents to be detected as a test set, gives a modification suggestion and reduces repeated work of testers.
2. From the aspect of standardization, the invention realizes testing from standardization item to output result, and detects the UI of the form, thereby greatly improving the efficiency.
It is to be clearly understood that the above description and illustration is made only by way of example and not as a limitation on the disclosure, application or use of the invention. Although embodiments have been described in the embodiments and illustrated in the accompanying drawings, the invention is not limited to the specific examples illustrated by the drawings and described in the embodiments as the best mode presently contemplated for carrying out the teachings of the invention, and the scope of the invention will include any embodiments falling within the foregoing specification and the appended claims.

Claims (10)

1. A form UI walk-through normalization detection method is characterized by comprising the following steps:
step 1: inputting a specification and defining a form UI specification category;
step 2: forming the characteristics of different types of forms UI according to the specifications;
step 3: acquiring a form UI interface to be analyzed;
step 4: carrying out standard conformity classification on each sub-form UI and giving the accuracy of classification;
step 5: scoring the whole list UI interface;
step 6: and outputting reasonable modification suggestions according to the scoring result.
2. The form UI walkthrough normalization detection method of claim 1, wherein: step 1 further comprises: the designer inputs the specification, defines the form category, takes the form alignment mode as an example, inputs different alignment modes, and gives corresponding patterns thereof as a training set.
3. The form UI walkthrough normalization detection method of claim 1, wherein: step 2 further comprises: form UI features of different types according to the specification, extract feature quantity in the input specification style according to the following method, firstly, gridding the input specification style to form a grid chart with an abscissa and an ordinate, extracting grid elements, representing with black and white (1, 0), and enabling the grid size to be corresponding to the size of a single character.
4. The form UI walkthrough normalization detection method of claim 3, wherein: extracting the distribution of the number of words in different patterns in X and Y directions, and recording as
Figure QLYQS_1
， />
Figure QLYQS_2
Wherein->
Figure QLYQS_3
Includes statistics of maximum value, minimum value, median and quartile of each sample in X direction, and is marked as +.>
Figure QLYQS_4
And normalize to->
Figure QLYQS_5
Similarly, the->
Figure QLYQS_6
Comprises and->
Figure QLYQS_7
Corresponding to the feature quantity.
5. The form UI walkthrough normalization detection method of claim 4, wherein: separately calculate
Figure QLYQS_8
Correlation of characteristic quantities of (a)>
Figure QLYQS_9
The correlation of the characteristic quantities of (a) by the formula +.>
Figure QLYQS_10
For a pair of
Figure QLYQS_11
Characteristic quantity +.>
Figure QLYQS_12
And reserving the characteristic quantity as the subsequent input.
6. The form UI walkthrough normalization detection method of claim 1, wherein: step 3 further comprises: and (3) acquiring a form UI interface to be analyzed, taking all the sub-forms UI contained in the form UI interface as a test set, extracting the characteristics in the step (2), and inputting the extracted characteristic quantity into the step (4) for category judgment.
7. The form UI walkthrough normalization detection method of claim 1, wherein: and 4, classifying the form to be checked by adopting a classification algorithm, wherein: the distance calculation formula is as follows formula 1:
Figure QLYQS_13
（1）
the center point calculation formula is as follows formula 2:
Figure QLYQS_14
（2）
the distance between the test set and the center point of each category and the probability calculation formula of the category are as follows:
Figure QLYQS_15
（3）/>
Figure QLYQS_16
（4）
Figure QLYQS_17
（5）
firstly training the feature quantity of the training set in the step 1 through the algorithm to obtain a sample center point, and then inputting the feature quantity of the testing set in the step 3 into the algorithm to obtain the category and the probability P thereof in the category.
8. The form UI walkthrough normalization detection method of claim 1, wherein: step 5 further comprises: scoring is carried out on a UI interface of a form to be analyzed, the scoring is based on the principle of overall consistency, and the specific scoring method is as follows:
counting the classified sub-forms UI, wherein the most classified sub-forms are as follows
Figure QLYQS_18
The probability corresponding to the method is +.>
Figure QLYQS_19
The overall form UI score is: />
Figure QLYQS_20
N is the number of all sub-forms in the UI form.
9. The form UI walkthrough normalization detection method of claim 1, wherein: in the step 6, when the score is more than 90 minutes, the consistency of the form is considered to be better, great modification is not needed, only sub-forms which are not classified into a plurality of categories in the classification process are needed to be checked, when the score is 60-90 minutes, the form is considered to have the consistency problem, manual review is needed, and when the score is less than 60 minutes, the algorithm is considered to be inapplicable, and manual detection is needed to be changed.
10. A form UI walkthrough normalization detection device implementing the method of claim 1.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111062259A (en) * 2019-11-25 2020-04-24 泰康保险集团股份有限公司 Form recognition method and device
DE102019128009A1 (en) * 2019-10-17 2020-09-10 Audi Ag Method and device for modifying a vehicle outer shell
CN113435240A (en) * 2021-04-13 2021-09-24 北京易道博识科技有限公司 End-to-end table detection and structure identification method and system
CN115934559A (en) * 2022-12-22 2023-04-07 广东技术师范大学 Testing method of intelligent form testing system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102019128009A1 (en) * 2019-10-17 2020-09-10 Audi Ag Method and device for modifying a vehicle outer shell
CN111062259A (en) * 2019-11-25 2020-04-24 泰康保险集团股份有限公司 Form recognition method and device
CN113435240A (en) * 2021-04-13 2021-09-24 北京易道博识科技有限公司 End-to-end table detection and structure identification method and system
CN115934559A (en) * 2022-12-22 2023-04-07 广东技术师范大学 Testing method of intelligent form testing system

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