CN116257459A - Form UI walk normalization detection method and device - Google Patents
Form UI walk normalization detection method and device Download PDFInfo
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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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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
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, Wherein->Includes statistics of maximum value, minimum value, median and quartile of each sample in X direction, and is marked as +.>And normalize to->Similarly, the->Comprises and->Corresponding to the feature quantity.
Wherein: separately calculateCorrelation of characteristic quantities of (a)>The correlation of the characteristic quantities of (a) by the formula +.>For->Characteristic quantity +.>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:
the center point calculation formula is as follows formula 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:
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 followsThe probability corresponding to the method is +.>The overall form UI score is: />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, Wherein->Includes statistics of maximum value, minimum value, median, quartile, etc. of each sample in X direction, and is marked as +.>And normalize to->Similarly, the->Comprises and->Corresponding to the feature quantity. Separately calculate->Correlation of characteristic quantities of (a)>The correlation of the characteristic quantities of (a) by the formula +.>For->Characteristic quantity +.>And reserving the characteristic quantity as the subsequent input.
In the above-mentioned formula(s),representing the word number distribution of the 1 st form to the m-th form in the X direction,/or->Representing the word number distribution of the 1 st form to the m-th form in the Y direction. />Representing the word count distribution of the ith form in the X direction, +.>Representing the word count distribution of the ith form in the Y-direction, +.>Statistics representing the distribution of the number of words in the X direction of the ith formQuantity (S)>Statistic normalization value representing the distribution of the number of words of the ith form in the X direction, +.>For calculating the relationship between different features in the ith form, 1<j<n,/>For pressing->Ordering of->Rank correlation coefficient of statistics>And the serial number difference value between every two features after the sequence is ordered according to the normalized value. />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:
the center point calculation formula is as follows formula 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:
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)Center point coordinates obtained after distance calculation for all forms, +.>、X-coordinate for two points furthest apart, +.>、/>Is the Y coordinate of the two points furthest apart.
In the formula (3)For testing set and center point distanceLeave, go up>And feature quantity of the test set after feature extraction, normalization and correlation analysis.
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 followsThe probability corresponding to the method is +.>The overall form UI score is: />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, />Wherein->Includes statistics of maximum value, minimum value, median and quartile of each sample in X direction, and is marked as +.>And normalize to->Similarly, the->Comprises and->Corresponding to the feature quantity.
5. The form UI walkthrough normalization detection method of claim 4, wherein: separately calculateCorrelation of characteristic quantities of (a)>The correlation of the characteristic quantities of (a) by the formula +.>For a pair ofCharacteristic quantity +.>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:
the center point calculation formula is as follows formula 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:
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:
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)
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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 |
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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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