CN114721936A

CN114721936A - Data processing method, electronic device, medium, and program product

Info

Publication number: CN114721936A
Application number: CN202210190359.4A
Authority: CN
Inventors: 王浩杰
Original assignee: Alibaba China Co Ltd
Current assignee: Alibaba China Co Ltd
Priority date: 2022-02-28
Filing date: 2022-02-28
Publication date: 2022-07-08

Abstract

The embodiment of the disclosure discloses a data processing method, an electronic device, a medium and a program product, wherein the method comprises the following steps: acquiring initial information of a test case, wherein the initial information comprises demand data, description data and input/output data; or, the initial information further includes application data used for executing the test case; carrying out structuralization processing on the initial information of the test case to obtain structuralized initial information; and performing feature extraction on the structured initial information to obtain a feature fingerprint of the test case. According to the technical scheme, the features can be automatically extracted, the labor cost is reduced, the extracted features are more and more standard, and the difference reason determined based on the extracted features is more accurate.

Description

Data processing method, electronic device, medium, and program product

Technical Field

The disclosed embodiments relate to the technical field of application testing, and in particular, to a data processing method, an electronic device, a medium, and a program product.

Background

When an application program is tested, after functions in the application program are iterated, differences may exist between output results of the cases after the test cases are executed and expected results, and the differences may be differences caused by bug (defect) occurring during the function iteration and normal differences allowed to occur during the function iteration, so that a tester needs to perform case differentiation analysis to analyze reasons for the differences. At present, most of conventional case differentiation analysis adopts manual work to analyze and summarize difference reasons corresponding to differentiation characteristics, so that when a difference exists between a case output result after a case is executed and an expected result, differentiation characteristics are summarized manually, and the difference reasons are analyzed through the differentiation characteristics. However, the labor cost is high, only the characteristics of fields which are considered to be important by people can be focused on by manually summarizing the differentiation characteristics, thousands of people exist, and the determined differentiation characteristics are lack of specifications and standards; however, the fields in the test cases are hundreds of thousands, the manually extracted features of the fields cannot completely cover all the features in the test cases, and the analysis of the difference reasons only by manually extracted fields can cause inaccurate analysis of the difference reasons.

Disclosure of Invention

The disclosed embodiments provide a data processing method, an electronic device, a medium, and a program product.

In a first aspect, an embodiment of the present disclosure provides a data processing method.

Specifically, the data processing method includes:

acquiring initial information of a test case, wherein the initial information comprises demand data, description data and input and output data; or, the initial information further includes application data used for executing the test case;

carrying out structuralization processing on the initial information of the test case to obtain structuralized initial information;

and performing feature extraction on the structured initial information to obtain a feature fingerprint of the test case.

With reference to the first aspect, in a first implementation manner of the first aspect, the structuring the initial information of the test case to obtain the structured initial information includes:

performing word segmentation processing on the requirement data and the description data of the test case to obtain requirement word segmentation and description word segmentation;

matching the demand participles and the description participles with participles in preset corpus data to obtain structured demand data and description data, wherein the participles in the corpus data correspond to the structured data.

In a second aspect, a data processing method is provided in an embodiment of the present disclosure.

Specifically, the data processing method includes:

acquiring initial information of an existing use case and a corresponding output result thereof;

extracting the characteristics of the initial information of the existing use case to obtain the existing characteristic fingerprint of the existing use case, and generating the fingerprint library, wherein the existing characteristic fingerprint and the corresponding output result thereof are recorded in the fingerprint library;

training an initial fingerprint matching algorithm based on the fingerprint database, and adjusting parameters of the initial fingerprint matching algorithm to obtain the pre-stored fingerprint matching algorithm.

With reference to the second aspect, embodiments of the present disclosure are in a first implementation manner of the second aspect, wherein the fingerprint matching algorithm includes a nearest neighbor NN algorithm, a K nearest neighbor KNN algorithm, or a weighted K nearest neighbor WKNN algorithm.

In a third aspect, an embodiment of the present disclosure provides a data processing method.

Specifically, the data processing method includes:

receiving a test case analysis message of a map application sent by a client, wherein the test case analysis message is used for indicating a test case to be analyzed;

executing the test case to be analyzed to obtain a test result, and obtaining initial information of a target case of the target case, wherein the test result is different from an expected result;

extracting the characteristics of the initial information of the target use case to obtain a target characteristic fingerprint of the target use case;

matching the target characteristic fingerprint with the existing characteristic fingerprint recorded in a prestored fingerprint library by a prestored fingerprint matching algorithm to obtain an existing characteristic fingerprint matched with the target characteristic fingerprint, wherein the existing characteristic fingerprint and a corresponding output result are recorded in the fingerprint library;

comparing the output result corresponding to the matched existing characteristic fingerprint with the test result of the target case, and determining the output result as the difference reason of the target case;

and sending the difference reason corresponding to the target use case to the client.

With reference to the third aspect, embodiments of the present disclosure are directed to the first implementation manner of the third aspect, wherein,

the matching of the target characteristic fingerprint with the existing characteristic fingerprint recorded in a pre-stored fingerprint library through a pre-stored fingerprint matching algorithm to obtain the existing characteristic fingerprint matched with the target characteristic fingerprint comprises the following steps:

calculating the similarity between the target characteristic fingerprint and each existing characteristic fingerprint in the fingerprint library through the prestored fingerprint matching algorithm;

and determining the existing characteristic fingerprint with the highest similarity to the target characteristic fingerprint as the existing characteristic fingerprint matched with the target characteristic fingerprint.

With reference to the third aspect and the first implementation manner of the third aspect, in a second implementation manner of the first aspect, the comparing the output result corresponding to the matched existing feature fingerprint with the test result of the target case to determine that the output result is the cause of the difference of the target case includes:

when the output result corresponding to the matched existing characteristic fingerprint is the same as the test result of the target case, determining that the difference reason of the target case is not iteration defect;

and when the output result corresponding to the matched existing characteristic fingerprint is different from the test result of the target case, determining that the difference reason of the target case is iteration defect.

With reference to the third aspect and the foregoing implementation manners of the third aspect, the present disclosure is in a third implementation manner of the third aspect, wherein the method further includes:

acquiring matching similarity between the target characteristic fingerprint and an existing characteristic fingerprint matched with the target characteristic fingerprint;

determining the reliability of the target case for testing the defects based on the matching similarity and the difference reason of the target case;

and sending prompt information to the client, wherein the prompt information comprises the reliability of the target case for testing the defects.

With reference to the third aspect and the foregoing implementation manners of the third aspect, the present disclosure is in a fourth implementation manner of the third aspect, wherein the method further includes:

receiving a verification result sent by the client, wherein the verification result comprises that the difference reason of the target use case is that an iterative program of the map application has effective defects or does not have defects;

recording the difference reason of the target characteristic fingerprint and the target use case in the verification result as new data in a fingerprint library;

and acquiring the new data, and updating and iterating the pre-stored fingerprint matching algorithm according to the new data.

In a fourth aspect, a data processing method is provided in an embodiment of the present disclosure.

Specifically, the data processing method includes:

sending a test case analysis message of a map application to a server, wherein the test case analysis message is used for indicating a test case to be analyzed so that the server can analyze a difference reason corresponding to a target case with a difference between a test result and an expected result in the test case;

and receiving a difference reason corresponding to the target use case sent by the server.

With reference to the fourth aspect, embodiments of the present disclosure are directed to the first implementation manner of the fourth aspect, wherein,

receiving and displaying prompt information sent by the server, wherein the prompt information comprises the credibility of the target case for testing the defects;

receiving a verification instruction input aiming at the prompt message, wherein the verification instruction is used for indicating a verification result, and the verification result comprises that the difference reason of the target use case is that the map application has a valid defect or does not have a defect;

and sending the verification result to a server, so that the server can record the difference reason of the target characteristic fingerprint and the target use case in the verification result into a fingerprint library.

In a fifth aspect, a data processing apparatus is provided in an embodiment of the present disclosure.

Specifically, the data processing apparatus includes:

the test case testing system comprises a first acquisition module, a second acquisition module and a testing module, wherein the first acquisition module is configured to acquire initial information of a test case, and the initial information comprises demand data, description data and input/output data; or, the initial information further includes application data used for executing the test case;

the processing module is configured to perform structural processing on the initial information of the test case to obtain structural initial information;

and the first extraction module is configured to perform feature extraction on the structured initial information to obtain a feature fingerprint of the test case.

With reference to the fifth aspect, an embodiment of the present disclosure is implemented in a first implementation manner of the fifth aspect, wherein the first processing module is configured to:

In a sixth aspect, a data processing apparatus is provided in an embodiment of the present disclosure.

Specifically, the data processing apparatus includes:

the second acquisition module is configured to acquire initial information of an existing use case and a corresponding output result;

the second extraction module is configured to perform feature extraction on the initial information of the existing use case to obtain an existing feature fingerprint of the existing use case, and generate the fingerprint library, wherein the existing feature fingerprint and a corresponding output result thereof are recorded in the fingerprint library;

and the training module is configured to train an initial fingerprint matching algorithm based on the fingerprint database, adjust parameters of the initial fingerprint matching algorithm and obtain the pre-stored fingerprint matching algorithm.

With reference to the sixth aspect, the present disclosure embodiment is implemented in a first implementation manner of the sixth aspect, wherein the fingerprint matching algorithm includes a nearest neighbor NN algorithm, a K-nearest neighbor KNN algorithm, or a weighted K-nearest neighbor WKNN algorithm.

In a seventh aspect, a data processing apparatus is provided in the embodiments of the present disclosure.

Specifically, the data processing apparatus includes:

the system comprises a first receiving module, a second receiving module and a third receiving module, wherein the first receiving module is configured to receive a test case analysis message of a map application sent by a client, and the test case analysis message is used for indicating a test case to be analyzed;

the execution module is configured to execute the test case to be analyzed to obtain a test result, and obtain initial information of a target case of the target case, wherein the test result is different from an expected result;

the third extraction module is configured to perform feature extraction on the initial information of the target use case to obtain a target feature fingerprint of the target use case;

the matching module is configured to match the target characteristic fingerprint with an existing characteristic fingerprint recorded in a pre-stored fingerprint library through a pre-stored fingerprint matching algorithm to obtain an existing characteristic fingerprint matched with the target characteristic fingerprint, and the existing characteristic fingerprint and a corresponding output result are recorded in the fingerprint library;

the comparison module is configured to compare the output result corresponding to the matched existing characteristic fingerprint with the test result of the target case and determine the output result as a difference reason of the target case;

a first sending module configured to send the difference reason corresponding to the target use case to the client.

With reference to the seventh aspect, in a first implementation manner of the seventh aspect, the embodiment of the present disclosure is configured to:

With reference to the seventh aspect and the first implementation manner of the seventh aspect, in a second implementation manner of the fifth aspect, an embodiment of the present disclosure is configured to:

With reference to the seventh aspect and the foregoing implementation manner of the seventh aspect, the present disclosure is in a third implementation manner of the seventh aspect, wherein the apparatus further includes:

a third obtaining module configured to obtain matching similarity between the target feature fingerprint and an existing feature fingerprint matched with the target feature fingerprint;

the determining module is configured to determine the reliability of the target case for testing the defects based on the matching similarity and the difference reason of the target case;

and the second sending module is configured to send prompt information to the client, wherein the prompt information comprises the credibility of the target use case for testing the defects.

With reference to the seventh aspect and the foregoing implementation manner of the seventh aspect, the present disclosure is in a fourth implementation manner of the seventh aspect, wherein the apparatus further includes:

a second receiving module configured to receive a verification result sent by the client, where the verification result includes that the reason for the difference of the target use case is that an iterator of the map application has a valid defect or no defect;

a recording module configured to record the target feature fingerprint and the reason for the difference between the target use case and the verification result as new data in a fingerprint library;

and the updating module is configured to acquire the new data and perform updating iteration on the pre-stored fingerprint matching algorithm according to the new data.

In an eighth aspect, a data processing apparatus is provided in the embodiments of the present disclosure.

Specifically, the data processing apparatus includes:

the third sending module is configured to send a test case analysis message of the map application to the server, wherein the test case analysis message is used for indicating a test case to be analyzed, so that the server analyzes a difference reason corresponding to a target case in which a test result is different from an expected result in the test case;

and the third receiving module is configured to receive the difference reason corresponding to the target use case sent by the server.

With reference to the eighth aspect, embodiments of the present disclosure in a first implementation manner of the eighth aspect, wherein,

the fourth receiving module is configured to receive and display prompt information sent by the server, wherein the prompt information comprises the credibility of the target case for testing the defects;

a fifth receiving module configured to receive a verification instruction for the prompt information input, wherein the verification instruction is used for indicating a verification result, and the verification result comprises that the difference reason of the target use case is that the map application has a valid defect or does not have a defect;

a fourth sending module, configured to send the verification result to a server, so that the server records the difference reason between the target feature fingerprint and the target use case in the verification result into a fingerprint library.

In a ninth aspect, the present disclosure provides an electronic device, which includes a memory for storing one or more computer instructions for supporting a data processing apparatus to execute the data processing method, and a processor configured to execute the computer instructions stored in the memory. The data processing apparatus may further comprise a communication interface for the data processing apparatus to communicate with other devices or a communication network.

In a tenth aspect, the disclosed embodiments provide a computer-readable storage medium for storing computer instructions for a data processing apparatus, which includes computer instructions for executing the data processing method described above as a data processing apparatus.

In an eleventh aspect, the disclosed embodiments provide a computer program product comprising a computer program/instructions, wherein the computer program/instructions, when executed by a processor, implement the method steps of the above-mentioned data processing method.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

the technical scheme can perform feature extraction after the initial information of the test case is subjected to structural processing. According to the technical scheme, all information in the test case can be subjected to feature extraction, manual participation is not needed, the labor cost can be reduced, the feature extraction is automatic extraction of a machine and is not influenced by manpower, and the features extracted based on all the information of the test case are more and more standard, so that the feature matching is more accurate, and the difference reason determined based on the extracted features is more accurate.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of embodiments of the disclosure.

Drawings

Other features, objects, and advantages of embodiments of the disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:

fig. 1 shows a flow chart of a data processing method according to an embodiment of the present disclosure.

Fig. 2 shows a flow schematic block diagram of a data processing method according to an embodiment of the present disclosure.

FIG. 3 shows a flow diagram of another data processing method according to an embodiment of the present disclosure.

FIG. 4 shows a flow schematic block diagram of another data processing method according to an embodiment of the present disclosure.

FIG. 5 shows a flow diagram of yet another data processing method according to an embodiment of the present disclosure.

FIG. 6 shows a flow schematic block diagram of yet another data processing method according to an embodiment of the present disclosure.

Fig. 7 illustrates a flowchart of yet another data processing method according to an embodiment of the present disclosure.

Fig. 8 shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure.

Fig. 9 shows a block diagram of another data processing apparatus according to an embodiment of the present disclosure.

Fig. 10 shows a block diagram of a further data processing apparatus according to an embodiment of the present disclosure.

Fig. 11 shows a block diagram of a further data processing apparatus according to an embodiment of the present disclosure.

Fig. 12 shows a block diagram of an electronic device according to an embodiment of the present disclosure.

FIG. 13 is a schematic block diagram of a computer system suitable for use in implementing a data processing method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, exemplary embodiments of the disclosed embodiments will be described in detail with reference to the accompanying drawings so that they can be easily implemented by those skilled in the art. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.

In the disclosed embodiments, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.

It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

The technical scheme provided by the embodiment of the disclosure can be used for carrying out feature extraction after the initial information of the test case is subjected to structural processing. According to the technical scheme, all information in the test case can be subjected to feature extraction, manual participation is not needed, the labor cost can be reduced, the feature extraction is automatic extraction and maintenance of a machine and is not influenced by manpower, and the features extracted based on all information of the test case are more and more standard, so that the feature matching is more accurate, and the difference reason determined based on the extracted features is more accurate.

Fig. 1 shows a flowchart of a data processing method according to an embodiment of the present disclosure, which includes the following steps S101-S103, as shown in fig. 1.

In step S101, obtaining initial information of a test case, where the initial information includes demand data, description data, and input/output data; or, the initial information further includes application data used for executing the test case;

in step S102, performing a structuring process on the initial information of the test case to obtain structured initial information;

in step S103, feature extraction is performed on the structured initial information to obtain a feature fingerprint of the test case.

In an embodiment of the present disclosure, the data processing method may be applied to a computer, a computing device, an electronic device, a server, a service cluster, and the like, which may perform data processing.

In an embodiment of the present disclosure, the Test Case (Test Case) refers to a description of a Test task performed on a specific software product, and embodies a Test scheme, a method, a technique, and a policy. It is understood that the test case is a set of test inputs, execution conditions, and expected results tailored for a particular purpose to verify that a particular software requirement is met. By way of example, the test case may be a test case for testing a map application.

In an embodiment of the present disclosure, the initial information of the test case includes requirement data of the test case, description data of the test case, and input/output data of the test case; or, the initial information of the test case further includes application data used for executing the test case. The required data of the test case refers to literal data describing the execution logic or execution strategy of the test case, the description data of the test case refers to literal data describing the specific input, expected output and execution logic of the case, and the input and output data of the target case refers to the input data of the test case and the output data after the test case is executed. When a test case is executed, application data in an application is sometimes needed to be used, and at this time, initial information of the test case includes application data used for executing the test case. For example, when the test case is a case for a map application, the application data may be base map data.

In an example, a test case is described as an example for map application, when the test case is used for testing a method for modifying the function of an electronic eye on a map, the required data of the test case can be that the electronic eye is erected in front of a tunnel under a high-speed scene, the erected rod is a fixed gun type and short rod, and is manufactured into a violation electronic eye; the description data of the test case can be high speed, vertical rod, fixed gun type and short rod in the road grade of the base map, and the electronic eye for identifying data is the vertical rod; the type of the expected manufactured electronic eye is violation; the input data of the test case can be actual data of the electronic eye collected at the corresponding collection point, such as the identification type, the erection mode, the appearance, the position relation with the road surface, the width-height ratio, the height of the rod body, the shooting angle, the coordinate position, the separation point, the merging point, the crossing, the wall of the rod body, the wall of the cross rod, the entrance guard, the toll gate, the front of the tunnel and the like; the output data of the test case may be an output result after the test case is executed, such as whether the production is successful or not, the type of the electronic eye generated under the successful condition, and the like; the base map data refers to relevant data of various facilities on the electronic map in map application, such as basic road layer data: chinese name, road grade, driving direction, road composition, road state, road type, vehicle passing road type, road characteristic identification, road ownership, data source, average lane number, road laying state, whether parking for a long time, whether occupying the road for management, actual passing capacity, lane width, road width and other data; electronic eye diagram layer data: the electronic eye type, the electronic eye type source, the appearance characteristic, the electronic eye shooting state, the platform type, the electronic eye number, the aspect ratio, the reference position, the relationship with the road surface position, the relationship with the intersection, the scene type, the passenger car speed limit value, the truck speed limit value and the like.

In an embodiment of the present disclosure, in order to provide more uniform and regular input for the feature extraction step and make the extracted features more accurate and standard, after the initial information of the test cases is obtained, the initial information of the test cases needs to be structured to obtain structured initial information. The term "structuring" as used herein refers to the quantitative representation of information, which can be represented by symbols or a uniform structure.

In this embodiment, as shown in fig. 2, after the structured initial information is acquired, a feature extraction algorithm may be used to perform feature extraction on the structured initial information to obtain a feature fingerprint of the test case. Features are used to express distinguishing and similar attributes between transactions, and the feature fingerprint refers to a combination of several features, which is a nonlinear combination that accurately expresses transactions. The initial information of the test case can be analyzed and quantized, various characteristics in the target case information are extracted, the characteristics have pertinence in the analysis of differentiation reasons, the characteristics are different like human fingerprints, and the characteristics are regularly displayed, so that various characteristics extracted from the initial information of the test case can be marked as characteristic fingerprints.

For example, the feature extraction algorithm may extract keywords in the structured test case information, count word frequencies of the keywords, and use the keywords with the word frequencies exceeding a preset value as features. Still taking the test case described above as an example for map application, the extracted features may include road features (such as road types and speed limit values), scene types (such as tunnels), platform types (such as vertical rods), appearance features (such as fixed guns), rod heights (such as short rods or high feelings), electronic eye types (such as electronic eyes against regulations), and the like of the road where the electronic eye is located in the map. And the manually extracted features may only extract a few data feature values of the type of the electronic eye, the type of the platform and the height of the rod body which are relatively concerned by the user. The method and the device can extract all the characteristics based on all the information of the test cases, and compared with a plurality of characteristics at manual extraction positions, the characteristics are more and more standard, so that the characteristics are more accurately matched, and the difference reason determined based on the extracted characteristics is more accurate.

In the above embodiment, the initial information of the test case may be structured and then feature extraction may be performed. According to the technical scheme, all information in the test case can be subjected to feature extraction, manual participation is not needed, the labor cost can be reduced, the feature extraction is automatic extraction and maintenance of a machine and is not influenced by manpower, and the features extracted based on all information of the test case are more and more standard, so that the feature matching is more accurate, and the difference reason determined based on the extracted features is more accurate.

In an embodiment of the present disclosure, in the data processing method, the step S102 of performing a structuring process on the initial information of the test case to obtain a structured test case information portion may include the following steps:

performing word segmentation processing on the requirement data of the test case and the description data of the test case to obtain requirement word segmentation and description word segmentation;

In this embodiment, the requirement data and the description data in the initial information are all some characters, as shown in fig. 2, after the initial information of the test case is obtained, the characters may be participled by a participle algorithm, the characters are divided into individual participles, and the requirement participles divided by the requirement data and the description participles divided by the description data are obtained. The word segmentation algorithm may be a word segmentation algorithm based on character string matching, a word segmentation algorithm based on understanding, or a word segmentation algorithm based on statistics, and may be, for example, a HanLP (chinese Language Processing) word segmentation algorithm.

Still taking the above example as an example, the requirement data of the test case is that when the electronic eye of the upright rod in front of the tunnel is made into the violation electronic eye in a high-speed scene, the upright rod is a fixed gun type and short upright rod, and the violation electronic eye is made, the following requirement participles can be obtained after the participle processing is performed: high speed, tunnel, upright, electronic eye, upright, fixed gun, short pole, violation electronic eye.

In this embodiment, as shown in fig. 2, after obtaining the participles, the required participles may be matched with the participles in the preset corpus data, where two participles with the same semantic meaning are matched, for example, the "short vertical rod" may be matched with the participle "short rod" in the corpus data, so as to obtain the structural data, such as numbers or symbols, corresponding to the participles "short rod" in the corpus data. For example, "short stalk" corresponds to the number "0" and "high stalk" corresponds to the number "1".

Here, it should be noted that both the input/output data and the application data of the test case are structured data that can be recognized by the machine, and the structuring process may not be performed.

Fig. 3 illustrates a flowchart of another data processing method according to an embodiment of the present disclosure, which includes the following steps S301 to S303, as illustrated in fig. 3.

In step S301, initial information of an existing use case and an output result corresponding to the initial information are obtained;

in step S302, performing feature extraction on the initial information of the existing use case to obtain an existing feature fingerprint of the existing use case, and generating the fingerprint library, where the existing feature fingerprint and a corresponding output result thereof are recorded in the fingerprint library;

in step S303, an initial fingerprint matching algorithm is trained based on the fingerprint library, and parameters of the initial fingerprint matching algorithm are adjusted to obtain the pre-stored fingerprint matching algorithm.

In an embodiment of the present disclosure, the data processing method may be applied to a server such as a computer, a computing device, an electronic device, a server, and a service cluster, which can perform data processing.

In an embodiment of the present disclosure, the existing use cases may include a test case that has been executed historically and has a difference from an expected result, and a test case that has been executed historically and has no difference from an expected result, where the existing use cases are all normal and flawless use cases, and a difference between an output result and an expected result indicates that a cause of the difference is generated by normal iteration. The output result here refers to the processing result of each processing point when the test case is executed, and includes whether the processing is successful or failed. In an example, when the test case is a case for making the violation electronic eye, the output result is that whether the violation electronic eye is successfully made at the position where each violation electronic eye is to be made is identified.

In an embodiment of the present disclosure, as shown in fig. 4, after the initial information of an existing use case is obtained, feature extraction may be performed on the initial information of the existing use case to obtain an existing feature fingerprint of the existing use case, so as to form a fingerprint library; the specific extraction scheme may refer to the above-described feature extraction method for the initial information of the test case in the implementation, and is not described herein again.

In one possible implementation, as shown in fig. 4, a part of the existing feature fingerprints may be randomly acquired from the fingerprint database as a training set, and another part of the existing feature fingerprints may be acquired as a verification set, where the training set and the verification set do not intersect with each other, and the data size of the training set is generally larger than that of the verification set.

In a possible implementation manner, as shown in fig. 4, the training set and the verification set may be used to obtain a fingerprint matching algorithm through machine learning training, for example, the training set may be used to train the initial fingerprint matching algorithm, the parameters of the initial fingerprint matching algorithm are continuously adjusted, after the parameters are adjusted to a certain degree, the verification set may be used to check the matching success rate of the fingerprint matching algorithm after the parameters are adjusted, if the success rate meets the requirement, the training is stopped, and the fingerprint matching algorithm after the parameters are adjusted is stored as the pre-stored fingerprint matching algorithm to perform subsequent matching of the feature fingerprints. And if the success rate does not meet the requirement, continuing training and adjusting the parameters until the matching success rate of the fingerprint matching algorithm after the parameters are adjusted through the verification set is up to the requirement.

In an embodiment of the present disclosure, the fingerprint matching algorithm includes an NN (Nearest Neighbor) algorithm, a KNN (K-Nearest Neighbor) algorithm, or a WKNN (Weighted K-Nearest Neighbors) algorithm.

Fig. 5 shows a flowchart of still another data processing method according to an embodiment of the present disclosure, which includes the following steps S501-S506, as shown in fig. 5:

in step S501, a test case analysis message of a map application sent by a client is received, where the test case analysis message is used to indicate a test case to be analyzed;

in step S502, executing the test case to be analyzed to obtain a test result, and obtaining initial information of a target case of the target case where the test result is different from an expected result;

in step S503, feature extraction is performed on the initial information of the target use case to obtain a target feature fingerprint of the target use case;

in step S504, a pre-stored fingerprint matching algorithm is used to match the target characteristic fingerprint with an existing characteristic fingerprint recorded in a pre-stored fingerprint library, so as to obtain an existing characteristic fingerprint matched with the target characteristic fingerprint, where the existing characteristic fingerprint and a corresponding output result are recorded in the fingerprint library;

in step S505, comparing the output result corresponding to the matched existing characteristic fingerprint with the test result of the target case, and determining the output result as a difference reason of the target case;

in step S506, the difference reason corresponding to the target use case is sent to the client.

In an embodiment of the present disclosure, the client refers to a terminal used by a tester, the tester may input a test case analysis instruction through an input device of the client, such as a keyboard or a touch screen, where the test case analysis instruction is used to indicate a test case to be analyzed, and the client may send a test case analysis message to the server after receiving the test case analysis instruction input by the tester.

In one embodiment of the present disclosure, a test case is a set of test inputs, execution conditions, and expected results tailored for a particular purpose to verify that a particular software requirement is met. The server receives the test case analysis instruction, executes the test case to obtain a test result of the test case, and if the test result is different from an expected result of the test case, the test case can be recorded as a target case, initial information of the target case is the same as the initial information of the test case, and the initial information of the target case also comprises required data of the target case, description data of the target case and input and output data of the target case; or, the initial information of the target use case further includes application data used for executing the target use case,

in an embodiment of the present disclosure, as shown in fig. 6, the above-mentioned scheme of extracting the features of the initial information of the test case may be referred to, and the features of the initial information of the target case may be extracted to obtain the target feature fingerprint of the target case.

In an embodiment of the present disclosure, the pre-stored fingerprint matching algorithm may be obtained by training using a fingerprint library based on the above scheme, and is an algorithm for calculating similarity of feature fingerprints, and as shown in fig. 6, the target feature fingerprint may be matched with existing feature fingerprints recorded in the pre-stored fingerprint library by using the fingerprint matching algorithm to obtain existing feature fingerprints matched with the target feature fingerprint; the existing characteristic fingerprint that matches the target characteristic fingerprint as described herein refers to an existing characteristic fingerprint that is most similar to the target characteristic fingerprint.

In an embodiment of the present disclosure, as shown in fig. 6, the server may compare the output result corresponding to the matched existing feature fingerprint with the test result of the target case, and determine that the output result is the difference reason of the target case. If the output result of the matched existing characteristic fingerprint is similar to the test result of the target case, because the existing characteristic fingerprint is the output result of normal iteration, the test result of the target case is also the test result of normal iteration, and the difference reason of the target case is not iteration defect; if the output result of the matched existing characteristic fingerprint is different from the test result of the target case, the existing characteristic fingerprint is the output result of normal iteration, which indicates that the test result of the target case is not the normal difference result, and the difference reason of the target case is iteration defect.

In an embodiment of the present disclosure, as shown in fig. 6, the server may send a difference reason corresponding to the target case to the client, the client may display the difference reason corresponding to the target case on a user interface of the client, and if the difference reason is a difference generated by normal iteration, a tester sees that the difference reason is a difference generated by iteration bug (defect), and then the tester may submit the bug to a research and development staff for modification by the research and development staff without paying attention to the test case.

In the above embodiment, after receiving a test case analysis message of a map application sent by a client, executing the test case to be analyzed to obtain a test result and obtain target case information of a target case with the test result different from an expected result, performing feature extraction on the target case information to obtain a target feature fingerprint, matching the target feature fingerprint with existing feature fingerprints recorded in a fingerprint library to obtain an existing feature fingerprint most similar to the target feature fingerprint, wherein the feature fingerprints of the existing feature fingerprint and the target feature fingerprint are similar, which indicates that the two test cases are similar, and at this time, the difference analysis of the target case can be automatically completed by comparing the output results of the two test cases, so that human resources are saved, and the feature extraction is automatically extracted by a machine and is not influenced by human, and the output features are more and more standard, so that the subsequent feature fingerprint matching is more accurate, thereby making the determined cause of the discrepancy more accurate.

In an embodiment of the present disclosure, the step S505 of comparing the output result corresponding to the matched existing characteristic fingerprint with the test result of the target use case, and determining the difference reason of the target use case may include the following steps:

when the similarity between the output result corresponding to the matched existing characteristic fingerprint and the test result of the target case exceeds a preset threshold value, determining that the difference reason of the target case is not an iteration defect;

and when the similarity between the output result corresponding to the matched existing characteristic fingerprint and the test result of the target case does not exceed a preset threshold value, determining that the difference reason of the target case is iteration defect.

For example, assuming that the target case is a test case for manufacturing a violation electronic eye on an electronic map, the test result of the executed target case is that some manufacturing positions are not manufactured successfully, and there is a difference from the expected result that all the manufacturing positions are manufactured successfully, the matched existing case is obtained through fingerprint feature matching, if the output result of the existing case is that similar manufacturing positions are not manufactured successfully, and the similarity of the two results exceeds a preset threshold, it indicates that some manufacturing positions are not manufactured successfully during normal iteration, the target case is not a defect, if the output result of the existing case is that all the manufacturing positions are manufactured successfully, and the similarity of the two results does not exceed the preset threshold, it indicates that all the manufacturing positions can be manufactured successfully during normal iteration, the target case is a defect, the function of manufacturing the violation electronic eye has a defect, and a tester can submit the bug to a research and development staff, the functional program for making the violation electronic eye is modified by the developer.

Or, as an example, assuming that the target case is a test case for drawing prohibited information on an electronic map, the test result of the executed target case is that some drawing positions are not drawn successfully, and there is a difference between the drawing positions of the executed target case and the drawing success of the expected result, obtaining a matched existing case through fingerprint feature matching, if the output result of the existing case is that similar drawing positions are not drawn successfully, and the similarity of the two results exceeds a preset threshold, it indicates that some drawing positions are not drawn successfully during normal iteration, the target case is not a defect, if the output result of the existing case is that each manufacturing position is drawn successfully, and the similarity of the two results does not exceed the preset threshold, it indicates that each drawing position can be drawn successfully during normal iteration, the target case has a defect, and the function of the drawing prohibited information has a defect, the tester may submit the bug to the developer, and the developer may modify the function program of the drawing prohibition information.

In an embodiment of the present disclosure, the step S504 of matching the target characteristic fingerprint with an existing characteristic fingerprint recorded in a pre-stored fingerprint library through a pre-stored fingerprint matching algorithm to obtain an existing characteristic fingerprint matched with the target characteristic fingerprint may further include the following steps:

In this embodiment, the input of the pre-stored fingerprint matching algorithm is a target feature fingerprint and an existing feature fingerprint in the fingerprint library, and the output is a similarity between the target feature fingerprint and the existing feature fingerprint. All the existing characteristic fingerprints in the fingerprint database can be traversed, the similarity between the target characteristic fingerprint and each existing characteristic fingerprint is calculated through the pre-stored fingerprint matching algorithm, the existing characteristic fingerprint with the highest similarity to the target characteristic fingerprint is obtained after the traversal is finished, and the existing characteristic fingerprint with the highest similarity is determined to be the existing characteristic fingerprint matched with the target characteristic fingerprint.

In an embodiment of the present disclosure, the method may further include the steps of:

determining the credibility of the target case for testing the bug on the basis of the matching similarity and the difference reason of the target case;

and sending prompt information to the client, wherein the prompt information comprises the reliability of the bug tested by the target use case and is used for prompting a tester to manually verify the difference reason corresponding to the target use case.

In this embodiment, the pre-stored fingerprint matching algorithm can calculate the similarity between the target feature fingerprint and the existing feature fingerprint matched with the target feature fingerprint, and the difference reason corresponding to the target feature fingerprint can also be obtained as the difference reason of the existing matched feature fingerprint. When the reason of the difference is the normal difference generated by iteration, the higher the similarity is, the higher the reliability of the difference is that the normal difference generated by iteration is, and the lower the reliability of the target case for testing the defect is. When the reason of the difference is the difference generated by the iterative defect, the higher the similarity is, which indicates that the reliability of the difference is higher, and the reliability of the target case for testing the defect is higher. The reason for the difference is that the reliability of the target case testing defect of the difference generated by the iterative defect is higher than that of the target case testing defect of which the reason for the difference is the normal difference generated by the iteration.

In this embodiment, as shown in fig. 6, the server may directly output, to the client, prompt information, where the prompt information includes the reliability of the bug tested by the target use case, and prompts a tester to manually verify the difference reason corresponding to the target use case, and the tester may manually verify the difference reason corresponding to the target use case with reference to the reliability, and if the difference reason is an iteration defect and has high reliability, the tester may report the research and development staff and change the program. Of course, if the difference reason is the difference generated by normal iteration and the reliability is high, the difference reason corresponding to the target feature fingerprint can be determined as the normal difference generated by iteration, so that whether the difference is generated by normal iteration can be verified in an auxiliary mode under the condition that the machine judges automatically, and the differentiated analysis result is more accurate.

It should be noted here that when the reliability of the target case for testing the defect is smaller than the preset reliability value, which indicates that the reliability of the target case for testing the defect is not large, and it is not determined that the defect is a valid defect, the prompt information is sent to the client, so as to prompt the tester to manually verify the difference reason corresponding to the target case.

In an embodiment of the present disclosure, the method may further include:

recording the difference reason of the target characteristic fingerprint and the target use case in the verification result as new data in the fingerprint library;

In this embodiment, the client may display a prompt message to prompt a tester to manually verify a difference reason corresponding to the target case, the tester may input a verification result after the manual verification, the client may send the appropriate result to the server after receiving the verification result input by the tester, and the server may update the target characteristic fingerprint and the difference reason in the verification result as new data to the fingerprint library to update the fingerprint library. The new data can be buffered first, and the new data can be updated into the fingerprint database periodically.

In this embodiment, after the new data is updated in the fingerprint database at regular time, the new data in the fingerprint database may be acquired, and then the new data is used to perform update iteration on the parameters of the pre-stored fingerprint matching algorithm, so as to update the fingerprint database and the fingerprint matching algorithm, so that the subsequent determination of the cause of the difference is more accurate. Automatic extraction of fingerprint features and automatic updating of fingerprint matching algorithms can achieve automatic maintenance of the features and the weight parameters.

Fig. 7 illustrates a flowchart of still another data processing method according to an embodiment of the present disclosure, which includes the following steps S701 to S702, as illustrated in fig. 7:

in step S701, sending a test case analysis message of a map application to a server, where the test case analysis message is used to indicate a test case to be analyzed, so that the server analyzes a difference reason corresponding to a target case in which a test result is different from an expected result in the test case;

in step S702, a difference reason corresponding to the target use case sent by the server is received.

In an embodiment of the present disclosure, the data processing method may be applied to a client of a computer, a computing device, an electronic device, or the like, which may perform data processing.

In an embodiment of the present disclosure, the client refers to a terminal used by a tester, the tester may input a test case analysis instruction through an input device of the client, such as a keyboard or a touch screen, where the test case analysis instruction is used to indicate a test case to be analyzed, and the client may send a test case analysis message to the electronic device after receiving the test case analysis instruction input by the tester. The test case analysis message is used to indicate a test case to be analyzed, so that the server executes steps S501 to S506, automatically analyzes and obtains a difference reason corresponding to a target case in which a test result is different from an expected result in the test case, and then sends the difference reason corresponding to the target case to the client, and the client can display the difference reason of the target case. It should be noted here that the client may display the target use case information and the difference reason of the target use case.

According to the embodiment, the server side can automatically complete the differentiation analysis of the target use case, so that the human resources are saved.

In an embodiment of the present disclosure, the data processing method may further include:

receiving and displaying prompt information sent by the server, wherein the prompt information comprises the reliability of the target case for testing the defects and is used for prompting a tester to manually verify the difference reasons corresponding to the target case;

In this embodiment, the client may receive and display a prompt message sent by the server to prompt a tester to manually verify a difference reason corresponding to the target case, and the tester may input a verification instruction after manual verification. The client side receives the verification result input by the tester and then sends the appropriate result to the server side, and the server side can update the difference reason between the target characteristic fingerprint and the verification result into the fingerprint library as new data to update the fingerprint library. The new data can be buffered first, and the new data can be updated into the fingerprint database periodically.

The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.

Fig. 8 shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of both. As shown in fig. 8, the data processing apparatus includes:

a first obtaining module 801 configured to obtain initial information of a test case, where the initial information includes requirement data, description data, and input/output data; or, the initial information further includes application data used for executing the test case;

a processing module 802, configured to perform structural processing on the initial information of the test case to obtain structural initial information;

a first extraction module 803, configured to perform feature extraction on the structured initial information to obtain a feature fingerprint of the test case.

In an embodiment of the present disclosure, the processing module 802 is configured to:

Fig. 9 shows a block diagram of another data processing apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of both. As shown in fig. 9, the data processing apparatus includes:

a second obtaining module 901, configured to obtain initial information of an existing use case and an output result corresponding to the initial information;

a second extraction module 902, configured to perform feature extraction on the initial information of the existing use case to obtain an existing feature fingerprint of the existing use case, and generate the fingerprint library, where the existing feature fingerprint and an output result corresponding to the existing feature fingerprint are recorded in the fingerprint library;

a training module 903 configured to train an initial fingerprint matching algorithm based on the fingerprint library, and adjust parameters of the initial fingerprint matching algorithm to obtain the pre-stored fingerprint matching algorithm.

In an embodiment of the present disclosure, the fingerprint matching algorithm includes a nearest neighbor NN algorithm, a K-nearest neighbor KNN algorithm, or a weighted K-nearest neighbor WKNN algorithm.

Fig. 10 shows a block diagram of a further data processing apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of both. As shown in fig. 10, the data processing apparatus includes:

a first receiving module 1001 configured to receive a test case analysis message of a map application sent by a client, where the test case analysis message is used to indicate a test case to be analyzed;

the execution module 1002 is configured to execute the test case to be analyzed to obtain a test result, and obtain initial information of a target case of the target case, where the test result is different from an expected result;

a third extraction module 1003, configured to perform feature extraction on the initial information of the target use case to obtain a target feature fingerprint of the target use case;

a matching module 1004 configured to match the target characteristic fingerprint with an existing characteristic fingerprint recorded in a pre-stored fingerprint library by a pre-stored fingerprint matching algorithm to obtain an existing characteristic fingerprint matched with the target characteristic fingerprint, wherein the existing characteristic fingerprint and a corresponding output result thereof are recorded in the fingerprint library;

a comparison module 1005 configured to compare the output result corresponding to the matched existing feature fingerprint with the test result of the target use case, and determine that the output result is the difference reason of the target use case;

a first sending module 1006, configured to send the difference reason corresponding to the target use case to the client.

In an embodiment of the present disclosure, the matching module 1004 is configured to:

In an embodiment of the present disclosure, the alignment module 1005 is configured to:

In an embodiment of the present disclosure, the apparatus further includes:

In this embodiment, the data processing apparatus corresponds to the data processing method, and specific details can be referred to the description of the data processing method, which is not described herein again.

Fig. 11 shows a block diagram of a still further data processing apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of both. As shown in fig. 11, the data processing apparatus includes:

a third sending module 1101 configured to send a test case analysis message of the map application to the server, where the test case analysis message is used to indicate a test case to be analyzed, so that the server analyzes a difference reason corresponding to a target case in which a test result in the test case is different from an expected result;

a third receiving module 1102 configured to receive a difference reason corresponding to the target use case sent by the server.

In an embodiment of the present disclosure, the apparatus further includes:

The present disclosure also discloses an electronic device, fig. 12 shows a block diagram of an electronic device according to an embodiment of the present disclosure, and as shown in fig. 12, the electronic device 1200 includes a memory 1201 and a processor 1202; wherein the content of the first and second substances,

the memory 1201 is used to store one or more computer instructions, which are executed by the processor 1202 to implement the above-described method steps.

As shown in fig. 13, the computer system 1300 includes a processing unit 1301 that can execute various processes in the above-described embodiments according to a program stored in a Read Only Memory (ROM)1302 or a program loaded from a storage portion 1308 into a Random Access Memory (RAM) 1303. In the RAM1303, various programs and data necessary for the operation of the system 1300 are also stored. The processing unit 1301, the ROM1302, and the RAM1303 are connected to each other via a bus 1304. An input/output (I/O) interface 1305 is also connected to bus 1304.

The following components are connected to the I/O interface 1305: an input portion 1306 including a keyboard, a mouse, and the like; an output section 1307 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 1308 including a hard disk and the like; and a communication section 1309 including a network interface card such as a LAN card, a modem, or the like. The communication section 1309 performs communication processing via a network such as the internet. A drive 1310 is also connected to the I/O interface 1305 as needed. A removable medium 1311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1310 as necessary, so that a computer program read out therefrom is mounted into the storage portion 1308 as necessary. The processing unit 1301 may be implemented as a CPU, a GPU, a TPU, an FPGA, an NPU, or other processing units.

In particular, the above described methods may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for performing the data processing method. In such embodiments, the computer program may be downloaded and installed from a network via communications component 1309 and/or installed from removable media 1311.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.

As another aspect, the disclosed embodiment also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the foregoing embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the embodiments of the present disclosure.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combinations of the above-mentioned features, and that other embodiments can be made by any combination of the above-mentioned features or their equivalents without departing from the spirit of the invention. For example, the above features and (but not limited to) the features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims

1. A method of data processing, comprising:

acquiring initial information of a test case, wherein the initial information comprises demand data, description data and input/output data; or, the initial information further includes application data used for executing the test case;

2. The method according to claim 1, wherein the structuring the initial information of the test case to obtain the structured initial information comprises:

3. A method of data processing, comprising:

and training an initial fingerprint matching algorithm based on the fingerprint database, and adjusting parameters of the initial fingerprint matching algorithm to obtain the pre-stored fingerprint matching algorithm.

4. The method of claim 3, wherein the fingerprint matching algorithm comprises a Nearest Neighbor (NN) algorithm, a K-nearest neighbor (KNN) algorithm, or a weighted K-nearest neighbor (WKNN) algorithm.

5. A method of data processing, comprising:

6. The method according to claim 5, wherein the matching the target characteristic fingerprint with existing characteristic fingerprints recorded in a pre-stored fingerprint library by a pre-stored fingerprint matching algorithm to obtain an existing characteristic fingerprint matching the target characteristic fingerprint comprises:

7. The method of claim 5, wherein the comparing the output result corresponding to the matched existing feature fingerprint with the test result of the target use case to determine the reason for the difference of the target use case comprises:

8. The method of claim 5, wherein the method further comprises:

9. The method of claim 8, wherein the method further comprises:

10. A method of data processing, comprising:

11. The method of claim 10, comprising:

12. An electronic device comprising a memory and at least one processor; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the at least one processor to implement the method steps of any of claims 1-11.

13. A computer-readable storage medium having stored thereon computer instructions, characterized in that the computer instructions, when executed by a processor, carry out the method steps of any of claims 1-11.

14. A computer program product comprising computer programs/instructions, wherein the computer programs/instructions, when executed by a processor, implement the method steps of any of claims 1-11.