Information processing method and device, equipment, medium and program product
By sending product testing tasks to multiple clients, obtaining functional test information and calculating scores, the problem of difficult to accurately obtain user feedback during product testing is solved, and the accuracy of product testing and user experience are improved.
Patent Information
- Application Number
- CN202411937220.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-26
- Publication Date
- 2025-09-19
AI Technical Summary
During the product testing process, it is difficult to obtain accurate user feedback, resulting in poor product testing results.
By sending product testing tasks to multiple clients, we can obtain functional testing information of different dimensions, identify problem conditions and usage frequency, calculate functional scores based on this information, and generate product testing information.
It improves the accuracy of product testing, quantifies user attitudes, and facilitates product optimization and user experience improvement.
Smart Images
Figure CN120670283A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of artificial intelligence technology or financial technology, and specifically to an information processing method, device, equipment, medium and program product. Background Art
[0002] Financial institutions have launched various products to facilitate users in conducting business online. However, during the use of these products, various problems may arise, affecting the user experience.
[0003] In the process of implementing the inventive concept of the present disclosure, the inventors discovered that it is difficult to obtain accurate product testing information when testing a product, which results in poor product testing results. Summary of the Invention
[0004] In view of the above problems, the present disclosure provides an information processing method, apparatus, device, medium, and program product.
[0005] According to the first aspect of the present disclosure, an information processing method is provided, comprising: in response to a test instruction for a product to be tested, sending a product test task to M clients, and receiving M pieces of function test information from the M clients, wherein the M pieces of function test information respectively represent the test conditions of product functions in different dimensions, and M is an integer greater than 1; identifying the content in the mth function test information to obtain the mth problem condition information and the mth usage frequency information, wherein the usage frequency information represents the usage frequency of the problem function in the product function, and the problem condition information represents the problem severity of the problem function, and m is a positive integer less than or equal to M; determining the mth dimension function score based on the mth problem condition information and the mth usage frequency information; and generating product test information for the product based on the M dimension function scores.
[0006] According to an embodiment of the present disclosure, in response to a test instruction for a product to be tested, a product test task is sent to M clients, and M functional test information is received from the M clients, including: in response to the test instruction, obtaining product attribute information of the product, wherein the product attribute information includes attribute information of M dimensions; based on the attribute information of the M dimensions, determining M clients from N candidate clients, the N candidate clients corresponding to different test objects, and N being an integer greater than or equal to M; sending the product test task of the mth dimension to the mth client, and obtaining the mth functional test information input into the mth client by the mth test object.
[0007] According to an embodiment of the present disclosure, the mth client has a human-computer interaction interface; sending an mth dimension product test task to the mth client, and obtaining the mth function test information input into the mth client by the mth test object, including: sending the mth dimension product test task to the mth client, controlling the mth client to run the product in the human-computer interaction interface, so that the mth test object interacts with the product function of the mth dimension to test the product function of the mth dimension.
[0008] According to an embodiment of the present disclosure, the mth problem status information includes problem quantity information and problem impact degree information of the problems existing in the product function of the mth dimension; based on the mth problem status information and the mth usage frequency information, the mth dimension function score is determined, including: determining the problem severity level information based on the problem impact degree information and the mth usage frequency information; based on the problem severity level information, querying from the database to obtain the problem severity level weight; calling the first predetermined model to process the problem quantity information and the problem severity level weight to obtain the mth dimension function score.
[0009] According to an embodiment of the present disclosure, the content in the mth function test information is identified to obtain the mth problem status information and the mth usage frequency information, including: obtaining the problem description field and the function description field by identifying the content in the mth function test information; generating the mth problem status information based on the problem description field; obtaining historical usage frequency information by querying from the server based on the function description field, wherein the historical usage frequency information represents the historical usage frequency of the function described in the function description field; and determining the historical usage frequency information as the mth usage frequency information.
[0010] According to an embodiment of the present disclosure, there are K problem description fields, where K is an integer greater than 1; the mth problem status information includes problem quantity information and problem impact degree information of the problems existing in the product function of the mth dimension; based on the problem description field, the mth problem status information is generated, including: by matching J predetermined problem fields stored in the database with the K problem description fields, determining the matching problem fields that match the K problem description fields from the J predetermined problem fields, wherein the J predetermined problem fields are associated with J predetermined severity level information, and J is an integer greater than or equal to K; determining the problem quantity information based on the number of matching problem fields that match the K problem description fields; determining the predetermined impact degree information associated with the matching problem field that matches the kth problem description field as the problem impact degree information of the kth problem description field, and k is a positive integer less than or equal to K.
[0011] According to an embodiment of the present disclosure, product test information of a product is generated based on M dimensional function scores, including: determining the dimension identifiers corresponding to each of the M dimensional function scores; based on the M dimensional identifiers, querying from a database to obtain the dimension weights corresponding to each of the M dimensional function scores; calling a second predetermined model to process the M dimensional function scores and the dimension weights corresponding to each of the M dimensional function scores to obtain a product test score; and generating product test information based on the product test score.
[0012] The second aspect of the present disclosure provides an information processing device, including: a sending module, used to send product test tasks to M clients in response to a test instruction for a product to be tested, and receive M functional test information from the M clients, wherein the M functional test information respectively represent the test conditions of product functions in different dimensions, and M is an integer greater than 1; an identification module, used to identify the content in the mth functional test information, and obtain the mth problem condition information and the mth usage frequency information, wherein the usage frequency information represents the usage frequency of the problem function in the product function, and the problem condition information represents the problem severity of the problem function, and m is a positive integer less than or equal to M; a determination module, used to determine the mth dimension function score based on the mth problem condition information and the mth usage frequency information; a generation module, used to generate product test information of the product based on the M dimension function scores.
[0013] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.
[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.
[0015] The fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.
[0016] According to an embodiment of the present disclosure, a product test task for a product to be tested is sent to M clients, and functional test information for product functions in different dimensions is obtained from the M clients. The content of the functional test information for each dimension is then identified to obtain the usage frequency and problem status of the problematic product functions. This allows the status of the product functions in each dimension to be quantified, and product test information is generated based on the quantified dimensional function scores for multiple dimensions, thereby improving the accuracy of product testing. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:
[0018] Figure 1 Schematically illustrates an application scenario diagram of the information processing method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;
[0019] Figure 2 The flowchart of the information processing method according to the embodiment of the present disclosure is schematically shown;
[0020] Figure 3 A schematic diagram schematically illustrates a method for acquiring functional test information according to an embodiment of the present disclosure;
[0021] Figure 4 A schematic diagram schematically illustrates field matching according to an embodiment of the present disclosure;
[0022] Figure 5 A block diagram schematically illustrates a structure of an information processing device according to an embodiment of the present disclosure; and
[0023] Figure 6 The block diagram schematically shows an electronic device suitable for implementing the information processing method according to an embodiment of the present disclosure. DETAILED DESCRIPTION
[0024] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.
[0025] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.
[0026] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.
[0027] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).
[0028] In the technical solutions disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.
[0029] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure all provide users with corresponding operation portals for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge, and skills, and have reached a certain level of professionalism.
[0030] During the process of implementing the disclosed invention, the inventors discovered that user experience attitudes are subjective and therefore difficult to quantify into clear data. Furthermore, during product testing, users often raise many questions, both positive and negative, regarding the product. However, these questions are difficult to directly translate into quantifiable data for accurate product evaluation.
[0031] In view of this, the embodiments of the present disclosure provide an information processing method to measure users' attitudes when using a product. This method splits the factors of different dimensions that affect users' attitudes and converts each factor into a measurable quantitative score. An algorithm is used to form comprehensive and matchable data from these factors, which can be compared and measured.
[0032] Specifically, the information processing method includes: in response to a test instruction for a product to be tested, sending a product test task to M clients, and receiving M functional test information from the M clients, wherein the M functional test information respectively represent the test status of product functions in different dimensions, and M is an integer greater than 1; identifying the content in the mth functional test information to obtain the mth problem status information and the mth usage frequency information, wherein the usage frequency information represents the usage frequency of the problem function in the product function, and the problem status information represents the problem severity of the problem function, and m is a positive integer less than or equal to M; determining the mth dimension function score based on the mth problem status information and the mth usage frequency information; and generating product test information for the product based on the M dimension function scores.
[0033] Figure 1 The application scenario diagram of the information processing method, apparatus, device, medium and program product according to the embodiments of the present disclosure is schematically shown.
[0034] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.
[0035] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).
[0036] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.
[0037] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.
[0038] It should be noted that the information processing method provided in the embodiments of the present disclosure can generally be executed by the server 105. Accordingly, the information processing apparatus provided in the embodiments of the present disclosure can generally be set in the server 105. The information processing method provided in the embodiments of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the information processing apparatus provided in the embodiments of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.
[0039] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.
[0040] The following will be based on Figure 1 The scene described by Figures 2 to 4 The information processing method of the disclosed embodiment is described in detail.
[0041] Figure 2 The flowchart of the information processing method according to the embodiment of the present disclosure is schematically shown.
[0042] like Figure 2 As shown, the information processing method of this embodiment includes operations S210 to S240.
[0043] In operation S210, in response to a test instruction for a product to be tested, a product test task is sent to M clients, and M functional test information is received from the M clients, wherein the M functional test information respectively represent the test status of product functions in different dimensions, and M is an integer greater than 1.
[0044] In operation S220, the content in the mth function test information is identified to obtain the mth problem status information and the mth usage frequency information, wherein the usage frequency information represents the usage frequency of the problem function in the product function, and the problem status information represents the problem severity of the problem function, and m is a positive integer less than or equal to M.
[0045] In operation S230 , an m-th dimension function score is determined based on the m-th problem status information and the m-th usage frequency information.
[0046] In operation S240 , product testing information of the product is generated based on the M dimension function scores.
[0047] According to an embodiment of the present disclosure, the product to be tested may be a product for implementing a specific function, for example, a product for implementing a transaction or a product for implementing information query, etc.
[0048] The test instruction may be a test instruction sent by other electronic devices to the test system. When the test system receives the test instruction, it may send product test tasks to the M clients.
[0049] For example, M clients may correspond to product functions of different dimensions, respectively. Specifically, the mth client may correspond to the mth test object.
[0050] The mth test subject can be an expert who tests product functions in the mth dimension. Thus, upon receiving a product testing task, the mth client runs the product. The mth test subject can test the product functions in the mth dimension on the mth client, thereby discovering problematic functions within the product functions in the mth dimension. Based on the problems with the problematic functions, the mth client then inputs functional test information. The functional test information can be presented in the form of a list of experience problems. For example, the list of experience problems can include problem descriptions, problem classifications, and problem importance assessments.
[0051] In the embodiment of the present disclosure, the dimensions of the product to be tested may be determined based on the characteristics of the product to be tested, for example, based on the causes and formation methods of problems that are prone to occur in the product to be tested.
[0052] Dimensions can include business processes, interaction design, visual design, information cognition, security perception, system performance, intelligence, and operational services. For example, based on the project attributes of the product to be tested, experts from business, development, product, and experience departments can be invited to form an evaluation team to conduct a heuristic evaluation of the product and generate a list of experience issues.
[0053] According to an embodiment of the present disclosure, fields in the function test information may be used for identification, thereby generating problem condition information for describing the problem condition of the problem function and usage frequency information of the problem function in the function test information.
[0054] The problem status information may include problem impact information, which may be divided into multiple levels.
[0055] For example, high-impact issues might include those that directly cause user operations to fail. These issues make it virtually impossible to complete operations, potentially harming users and leading to user complaints. Medium-impact issues might include those that cause delays or frustration in user operations but don't directly cause them to fail. Low-impact issues might include those that affect the user experience but have little impact on user operations.
[0056] Frequency of use information for problematic features can be used to determine the commonness of problematic features. For example, commonly used features can refer to features that are frequently used by users. Infrequently used features refer to features that are infrequently used by users. Frequency of use information can be determined based on the number of times a feature is used during the execution of the mth client.
[0057] However, the embodiments of the present disclosure are not limited thereto. In the present disclosure, information such as the historical click rate of the question function can also be queried in the database based on the identifier of the question function, thereby determining the usage frequency information based on the queried information.
[0058] The problem severity level information of the problem function can be determined by combining the usage frequency information and the problem status information of the problem function as shown in Table 1 below.
[0059] Table 1 Classification of experience problem severity
[0060]
[0061] In the database, different problem severity levels are associated with different predetermined scores. Based on the problem severity level information for the problem function in the mth dimension, the database can be queried for the predetermined score associated with that level, thereby achieving a score for each problem function in the mth dimension. The score for each problem function in the mth dimension can be calculated to obtain the mth dimension function score. Based on this, a product test score can be calculated based on the M dimension function scores, and product test information can be generated based on the product test score.
[0062] In an embodiment of the present disclosure, before obtaining functional test information, the consent or authorization of the test subject may be obtained. For example, before operation S210, a request to obtain functional test information may be issued to the test subject. If the test subject agrees or authorizes the acquisition of functional test information, operation S210 is performed.
[0063] In embodiments of the present disclosure, a corresponding operation entry can be provided for the test subject, allowing them to choose to agree or reject the automated decision result. That is, before processing the functional test information, an instruction to agree or reject the processing can be obtained from the test subject through the corresponding operation entry. If the test subject agrees to the processing, the processing is performed on the test subject, that is, steps S220 to S240 are executed. If the test subject rejects the processing, the expert decision process is entered.
[0064] According to an embodiment of the present disclosure, a product test task for a product to be tested is sent to M clients, and functional test information for product functions in different dimensions is obtained from the M clients. The content of the functional test information for each dimension is then identified to obtain the usage frequency and problem status of the problematic product functions. This allows the status of the product functions in each dimension to be quantified, and product test information is generated based on the quantified dimensional function scores for multiple dimensions, thereby improving the accuracy of product testing.
[0065] According to an embodiment of the present disclosure, in response to a test instruction for a product to be tested, a product test task is sent to M clients, and M functional test information is received from the M clients. This includes: in response to the test instruction, obtaining product attribute information of the product, wherein the product attribute information includes attribute information of M dimensions. Based on the attribute information of the M dimensions, M clients are determined from N candidate clients, where the N candidate clients correspond to different test objects, and N is an integer greater than or equal to M. The product test task of the mth dimension is sent to the mth client, and the mth functional test information input by the mth test object into the mth client is obtained.
[0066] According to embodiments of the present disclosure, product attribute information may include attribute information of various dimensions of a product's functionality. For example, a product used to implement a transaction may include attribute information of a business process dimension, etc. A product used to implement an information query may include attribute information of an interactive design dimension, etc.
[0067] Figure 3 The following schematically illustrates a method for acquiring functional test information according to an embodiment of the present disclosure.
[0068] like Figure 3 As shown, based on M dimensions of attribute information 301_1 ... 301_M of the product to be tested, M clients 302_1 ... 302_M corresponding to the M dimensions of attribute information 301_1 ... 301_M can be determined from N candidate clients. Thus, the test objects 303_1 ... 303_M of the M clients can accurately test the product's functions based on the dimensions of the product's functions, thereby generating functional test information.
[0069] According to an embodiment of the present disclosure, by determining the client corresponding to the M-dimensional attribute information of a product from N candidate clients based on the M-dimensional attribute information of the product, the test subjects of the M clients can test the product functions in M dimensions respectively and generate functional test information, thereby improving the accuracy of the functional test information and, in turn, the accuracy of the product test information. On this basis, the method of the present disclosure converts the attitude of the test subject into a score, realizing the conversion of qualitative questions into quantitative data. Therefore, the test status of the product can be accurately determined based on the functional test information fed back by the test subject, facilitating product optimization and improving the user experience.
[0070] According to an embodiment of the present disclosure, an mth client has a human-computer interaction interface. Sending an mth dimension product test task to the mth client and obtaining mth function test information input into the mth client by an mth test subject includes: sending the mth dimension product test task to the mth client, controlling the mth client to run the product on the human-computer interaction interface so that the mth test subject interacts with the product function of the mth dimension to test the product function of the mth dimension.
[0071] According to embodiments of the present disclosure, product testing tasks of different dimensions can be sent to clients of different dimensions. For example, a product testing task of the mth dimension can include attribute information of a product function of the mth dimension, such as a function identifier. Thus, when the mth client receives the product testing task of the mth dimension, the product function of the mth dimension can be executed.
[0072] On this basis, the mth test subject can interact with the product function of the mth dimension in the human-computer interaction interface to perform testing. After the test, the mth test subject can input the mth function test information into the mth client, and the mth client will send the mth function test information to the test system.
[0073] According to an embodiment of the present disclosure, by sending the product testing task of the mth dimension to the mth client, the mth client is controlled to run the product in the human-computer interaction interface, so that the mth test object can accurately test the product function of the mth dimension in the human-computer interaction interface of the mth client, thereby improving the accuracy of the test.
[0074] According to an embodiment of the present disclosure, the content of the mth function test information is identified to obtain the mth problem status information and the mth usage frequency information, including: obtaining a problem description field and a function description field by identifying the content of the mth function test information. Based on the problem description field, the mth problem status information is generated. Based on the function description field, historical usage frequency information is queried from a server, wherein the historical usage frequency information represents the historical usage frequency of the function described in the function description field. The historical usage frequency information is determined as the mth usage frequency information.
[0075] According to embodiments of the present disclosure, a problem description field in the functional test information can be identified by matching a predetermined problem field with fields in the functional test information. For example, the problem description field may include a problem identification field, a problem category field, and a problem severity field. Thus, problem status information can be generated based on the problem severity field in the problem description field.
[0076] According to an embodiment of the present disclosure, the function description field in the function test information can be identified by matching the predetermined function field with the fields in the function test information. The function description field may include, for example, a field identifying the problematic function. Thus, based on the identification field of the problematic function, historical usage frequency information associated with the problematic function can be retrieved from the server and determined as the mth piece of usage frequency information.
[0077] According to an embodiment of the present disclosure, by extracting the problem description field and the function description field from the function test information, and then querying the database based on the function description field in the function test information to obtain the historical usage frequency information of the function corresponding to the function description field, and determining the historical usage frequency information as the usage frequency information of the function, and generating problem status information based on the problem description field, accurate usage frequency information and problem status information under each dimension are obtained, thereby improving the accuracy of the dimension function score, and further improving the accuracy of the product test information.
[0078] According to an embodiment of the present disclosure, there are K problem description fields, where K is an integer greater than 1. The m-th problem status information includes problem quantity information and problem impact degree information of the problems existing in the product function of the m-th dimension. Based on the problem description field, the m-th problem status information is generated, including: by matching J predetermined problem fields stored in the database with the K problem description fields, determining the matching problem fields that match the K problem description fields from the J predetermined problem fields, wherein the J predetermined problem fields are associated with J predetermined impact degree information, and J is an integer greater than or equal to K. Based on the number of matching problem fields that match the K problem description fields, the problem quantity information is determined. The predetermined impact degree information associated with the matching problem field that matches the k-th problem description field is determined as the problem impact degree information of the k-th problem description field, and k is a positive integer less than or equal to K.
[0079] Figure 4 A schematic diagram of field matching according to an embodiment of the present disclosure is schematically shown.
[0080] like Figure 4 As shown, by matching J predetermined question fields 402_1 ... 402_J with K question description fields 401_1 ... 401_K, a matching question field 403 that matches the question description field can be determined. Based on this, since each predetermined question field is associated with predetermined impact level information, the predetermined impact level information 404 associated with the matching question field 403 can be determined as the problem impact level information corresponding to the problem description field that matches the matching question field 403. Thus, the problem impact level corresponding to the kth question description field can be determined.
[0081] According to an embodiment of the present disclosure, J predetermined problem fields stored in a database are matched with K problem description fields, and matching problem fields that match the K problem description fields are determined from the J predetermined problem fields. Accurate problem quantity information is then determined based on the number of matching problem fields that match the K problem description fields. Furthermore, the predetermined impact level information associated with the matching problem field that matches the k-th problem description field is determined as the problem impact level information for the k-th problem description field. Thus, accurate problem quantity information and problem impact level information are obtained, thereby improving the accuracy of dimension function scoring and, in turn, the accuracy of product testing information.
[0082] According to an embodiment of the present disclosure, the mth problem status information includes information on the number of problems and information on the degree of impact of the problems existing in the product function of the mth dimension. Based on the mth problem status information and the mth usage frequency information, the mth dimension function score is determined, including: determining problem severity information based on the problem impact information and the mth usage frequency information. Based on the problem severity information, querying the problem severity weight from a database. Calling a first predetermined model to process the problem quantity information and the problem severity weight to obtain the mth dimension function score.
[0083] According to an embodiment of the present disclosure, the problem severity information may be stored in association with the problem severity weight in a database. Based on this, the problem severity weight stored in association with the problem severity information may be retrieved in the database.
[0084] The first predetermined model can be a model for calculating the problem quantity information and problem severity weights according to a predetermined algorithm. The first predetermined model can be called to process the problem quantity information and problem severity weights to obtain the mth dimension function score. Table 2 exemplifies the problem severity weights for different levels.
[0085] Table 2 Experience Problem Severity Level Weight
[0086]
[0087] According to an embodiment of the present disclosure, by determining the problem severity information based on the problem impact information and the usage frequency information, and then querying the problem severity weight from the database, the first predetermined model can be called to process the problem quantity information and the problem severity weight, and the severity of the problem under each dimension can be quantified to obtain the dimension function score, thereby improving the accuracy of the calculated dimension function score.
[0088] According to an embodiment of the present disclosure, generating product test information for a product based on M dimensional function scores includes: determining dimension identifiers corresponding to each of the M dimensional function scores. Based on the M dimensional identifiers, querying a database to obtain dimension weights corresponding to each of the M dimensional function scores. Invoking a second predetermined model to process the M dimensional function scores and the dimension weights corresponding to each of the M dimensional function scores to obtain a product test score. Generating product test information based on the product test scores.
[0089] According to an embodiment of the present disclosure, since each client has a corresponding product dimension, the dimension identifiers of the M dimensional function scores can be determined based on the identifiers of the M clients.
[0090] In the database, dimension identifiers and dimension weights can be stored in an associated manner. Thus, based on each dimension identifier, the dimension weight associated with each dimension identifier can be queried from the database. The M dimension weights retrieved are then combined with the M dimension function scores to calculate a product test score. Table 3 exemplifies the dimension weights.
[0091] Table 3
[0092]
[0093] According to an embodiment of the present disclosure, product test information can be used to determine whether there is a need for optimization of the product to be tested. When the product test score is higher than or equal to the predetermined score, product test information can be generated to indicate that the product does not need to be optimized. When the product test score is lower than the predetermined score, product test information can be generated to indicate that the product needs to be optimized. The predetermined score can be set based on demand, which is not limited in this disclosure. For example, the product test scores of Q products can be sorted, and the q products with the lowest ranking can be determined as products to be optimized. Q is an integer greater than 1, and q is a positive integer less than or equal to Q.
[0094] In the embodiment of the present disclosure, the first predetermined model and the second predetermined model can be integrated into one predetermined model, and the integrated predetermined model is expressed by the following formula:
[0095]
[0096] Among them, UES dev For product test scores, d is the total number of problems at each level under each dimension, that is, the number of problems, w p is the severity weight of the problem, w d is the dimension weight.
[0097] According to an embodiment of the present disclosure, by querying from a database based on M dimension identifiers to obtain the dimension weights corresponding to the M dimension function scores, and then calling a second predetermined model to process the dimension function scores in combination with the dimension weights corresponding to the respective dimension function scores, it is achieved that the product test score is calculated in combination with the importance of each dimension, thereby improving the accuracy of the product test score, and further improving the accuracy of the generated product test information.
[0098] According to the embodiments of the present disclosure, this method transforms qualitative methods into quantitative, measurable comparative data. This method is applicable to a wide range of different product and service offerings, including platform channels, transactions, services, and content, providing benchmarked and standardized measurement and analysis results. This method can quickly compare and gauge user attitudes towards different products, enabling comparisons and the development of more accurate optimization strategies.
[0099] Based on the above information processing method, the present disclosure also provides an information processing device. Figure 5 The device is described in detail.
[0100] Figure 5 The structure block diagram of the information processing device according to the embodiment of the present disclosure is schematically shown.
[0101] like Figure 5 As shown, the information processing device 500 of this embodiment includes a sending module 510 , an identification module 520 , a determination module 530 and a generation module 540 .
[0102] The sending module 510 is configured to, in response to a test instruction for a product to be tested, send a product test task to M clients and receive M pieces of functional test information from the M clients, where the M pieces of functional test information respectively represent test conditions of product functions in different dimensions, where M is an integer greater than 1. In one embodiment, the sending module 510 may be configured to perform operation S210 described above, which will not be further described here.
[0103] Identification module 520 is configured to identify the content of the mth function test information to obtain mth problem status information and mth usage frequency information, wherein the usage frequency information indicates the usage frequency of the problematic function among the product functions, and the problem status information indicates the problem severity of the problematic function, where m is a positive integer less than or equal to M. In one embodiment, identification module 520 may be configured to perform operation S220 described above, and will not be further described here.
[0104] The determination module 530 is configured to determine the mth dimension function score based on the mth problem status information and the mth usage frequency information. In one embodiment, the determination module 530 may be configured to perform the operation S230 described above, which will not be described in detail herein.
[0105] The generating module 540 is used to generate product test information of the product based on the M dimension function scores. In one embodiment, the generating module 540 can be used to perform the operation S240 described above, which will not be repeated here.
[0106] According to an embodiment of the present disclosure, the sending module 510 includes an acquisition submodule, a first determination submodule, and a sending submodule. The acquisition submodule is configured to acquire product attribute information of a product in response to a test instruction, wherein the product attribute information includes attribute information of M dimensions; the first determination submodule is configured to determine M clients from N candidate clients based on the attribute information of the M dimensions, wherein the N candidate clients correspond to different test objects, where N is an integer greater than or equal to M; and the sending submodule is configured to send a product test task of the mth dimension to the mth client and obtain the mth functional test information input by the mth test object into the mth client.
[0107] According to an embodiment of the present disclosure, the sending submodule includes a sending unit, wherein the sending unit is configured to send an m-th dimension product test task to the m-th client, control the m-th client to run the product in the human-computer interaction interface, and enable the m-th test object to interact with the m-th dimension product function to test the m-th dimension product function.
[0108] According to an embodiment of the present disclosure, the determination module 530 includes a second determination submodule, a first query submodule, and a first call submodule. The second determination submodule is configured to determine problem severity information based on problem impact information and the mth frequency of use information; the first query submodule is configured to query a database for problem severity weights based on the problem severity information; and the first call submodule is configured to call a first predetermined model to process problem quantity information and problem severity weights to obtain the mth dimension function score.
[0109] According to an embodiment of the present disclosure, the identification module 520 includes an identification submodule, a generation submodule, a query submodule, and a third determination submodule. The identification submodule is configured to identify the content of the mth functional test information to obtain a problem description field and a function description field; the generation submodule is configured to generate the mth problem status information based on the problem description field; the query submodule is configured to query the server for historical usage frequency information based on the function description field, wherein the historical usage frequency information represents the historical usage frequency of the function described in the function description field; and the third determination submodule determines the historical usage frequency information as the mth usage frequency information.
[0110] According to an embodiment of the present disclosure, the generation submodule includes a matching unit, a first determining unit, and a second determining unit. The matching unit is configured to match J predetermined problem fields stored in a database with K problem description fields, thereby determining a matching problem field that matches the K problem description fields from the J predetermined problem fields, wherein the J predetermined problem fields are associated with J predetermined impact information, where J is an integer greater than or equal to K; the first determining unit is configured to determine problem quantity information based on the number of matching problem fields that match the K problem description fields; and the second determining unit is configured to determine the predetermined impact information associated with the matching problem field that matches the k-th problem description field as the problem impact information of the k-th problem description field, where k is a positive integer less than or equal to K.
[0111] According to an embodiment of the present disclosure, the generation module 540 includes a fourth determination submodule, a first query submodule, a second call submodule, and a generation submodule. The fourth determination submodule is configured to determine the dimension identifier corresponding to each of the M dimension function scores; the first query submodule is configured to query a database based on the M dimension identifiers to obtain the dimension weights corresponding to each of the M dimension function scores; the second call submodule is configured to call a second predetermined model to process the M dimension function scores and the dimension weights corresponding to each of the M dimension function scores to obtain a product test score; and the generation submodule is configured to generate product test information based on the product test score.
[0112] According to embodiments of the present disclosure, any multiple modules among the sending module 510, identification module 520, determination module 530, and generation module 540 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the sending module 510, identification module 520, determination module 530, and generation module 540 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of these. Alternatively, at least one of the sending module 510, identification module 520, determination module 530, and generation module 540 may be at least partially implemented as a computer program module that, when executed, performs the corresponding functionality.
[0113] Figure 6The block diagram schematically shows an electronic device suitable for implementing the information processing method according to an embodiment of the present disclosure.
[0114] like Figure 6 As shown, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage portion 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.
[0115] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 602 and / or RAM 603. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.
[0116] According to an embodiment of the present disclosure, electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to bus 604. Electronic device 600 may also include one or more of the following components connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or modem. Communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 610 as needed, so that computer programs read from the removable media can be installed into storage section 608 as needed.
[0117] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.
[0118] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above, and / or one or more memories other than ROM 602 and RAM 603.
[0119] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to cause the computer system to implement the information processing method provided by the embodiments of the present disclosure.
[0120] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the processor 601 executes the computer program. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.
[0121] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.
[0122] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.
[0123] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, languages such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).
[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0125] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.
[0126] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.
Claims
1. An information processing method, characterized in that: The method comprises: In response to a test instruction for a product to be tested, send a product test task to M clients, and receive M pieces of functional test information from the M clients, wherein the M pieces of functional test information respectively represent test conditions of product functions in different dimensions, where M is an integer greater than 1; Identify the content of the mth function test information to obtain the mth problem status information and the mth usage frequency information, wherein the usage frequency information represents the usage frequency of the problem function among the product functions, and the problem status information represents the problem severity of the problem function, where m is a positive integer less than or equal to M; Determining an mth dimension function score based on the mth problem status information and the mth usage frequency information; Based on the M dimension function scores, product testing information of the product is generated.
2. The method according to claim 1, characterized in that The step of sending a product test task to M clients in response to a test instruction for a product to be tested, and receiving M pieces of functional test information from the M clients, includes: In response to the test instruction, obtaining product attribute information of the product, wherein the product attribute information includes attribute information of M dimensions; Determining the M clients from N candidate clients based on the attribute information of the M dimensions, where the N candidate clients correspond to different test objects, and N is an integer greater than or equal to M; A product test task of an mth dimension is sent to an mth client, and the mth functional test information input by an mth test subject into the mth client is obtained.
3. The method according to claim 2, characterized in that The mth client has a human-computer interaction interface; The sending of the product test task of the mth dimension to the mth client and obtaining the mth functional test information input by the mth test subject into the mth client includes: Send the product testing task of the mth dimension to the mth client, control the mth client to run the product on the human-computer interaction interface, so that the mth test object interacts with the product function of the mth dimension to test the product function of the mth dimension.
4. The method according to any one of claims 1 to 3, characterized in that The m-th problem status information includes information on the number of problems existing in the product function of the m-th dimension and information on the degree of impact of the problems; The determining the mth dimension function score based on the mth problem status information and the mth usage frequency information includes: Determining problem severity information based on the problem impact information and the mth usage frequency information; Based on the problem severity information, query the database to obtain the problem severity weight; The first predetermined model is called to process the problem quantity information and the problem severity weight to obtain the m-th dimension function score.
5. The method according to any one of claims 1 to 3, characterized in that The identifying of the content in the mth functional test information to obtain the mth problem status information and the mth usage frequency information includes: By identifying the content in the mth functional test information, a problem description field and a function description field are obtained; Based on the problem description field, generating the mth problem status information; Based on the function description field, query and obtain historical usage frequency information from a server, wherein the historical usage frequency information represents the historical usage frequency of the function described in the function description field; The historical usage frequency information is determined as the mth usage frequency information.
6. The method according to claim 5, characterized in that The number of problem description fields is K, where K is an integer greater than 1; the m-th problem status information includes information on the number of problems existing in the product function of the m-th dimension and information on the degree of impact of the problem; The generating the mth problem status information based on the problem description field includes: Determining a matching problem field that matches the K problem description fields from the J predetermined problem fields by matching J predetermined problem fields stored in a database with the K problem description fields, wherein the J predetermined problem fields are associated with J predetermined severity level information, and J is an integer greater than or equal to K; Determining the question quantity information based on the number of matching question fields that match the K question description fields; The predetermined impact degree information associated with the matching problem field that matches the k-th problem description field is determined as the problem impact degree information of the k-th problem description field, where k is a positive integer less than or equal to K.
7. The method according to any one of claims 1 to 3, characterized in that Generating product test information of the product based on the M-dimensional function scores includes: Determine the dimension identifier corresponding to each of the M dimension function scores; Based on the M dimension identifiers, querying from a database to obtain dimension weights corresponding to the M dimension function scores; Calling a second predetermined model to process the M dimension function scores and the dimension weights corresponding to the M dimension function scores to obtain a product test score; The product test information is generated based on the product test score.
8. An information processing device, characterized in that The device comprises: a sending module, configured to send a product test task to M clients in response to a test instruction for a product to be tested, and receive M pieces of functional test information from the M clients, wherein the M pieces of functional test information respectively represent test conditions of product functions in different dimensions, where M is an integer greater than 1; an identification module, configured to identify the content of the mth function test information and obtain the mth problem status information and the mth usage frequency information, wherein the usage frequency information represents the usage frequency of the problem function among the product functions, and the problem status information represents the problem severity of the problem function, where m is a positive integer less than or equal to M; a determination module, configured to determine an mth dimension function score based on the mth problem status information and the mth usage frequency information; A generating module is used to generate product test information of the product based on the M dimension function scores.
9. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.
11. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.