A method, device, equipment and medium for solving a problem

Abstract

The present disclosure relates to a method, apparatus, device, and medium for handling a problem. The method includes: obtaining a problem and problem description information in a software project to be handled; the software project to be handled includes multiple functional modules; determining a first correlation between the problem description information and each functional module based on keywords included in the problem description information; and determining a target functional module corresponding to the problem based on the first correlation information. The present disclosure can accurately determine the target functional module corresponding to the problem.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a method, apparatus, device, and medium for processing a problem. Background Art

[0002] To improve user experience, a large number of feedback issues are collected during the development and operation and maintenance of software projects. These issues are then assigned to relevant stakeholders, such as developers or planners, for follow-up. Currently, this approach often uses natural language processing and deep learning to directly assign follow-up targets to issues. However, the follow-up targets assigned using this method often do not match the functional modules where the issues occur, making it difficult to resolve the issues quickly and effectively. Therefore, before assigning follow-up targets, it is crucial to determine the functional modules that correspond to the issues. Summary of the Invention

[0003] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a method, device, equipment and medium for solving the problem.

[0004] The present disclosure provides a method for solving the problem, including:

[0005] Obtaining problems and problem description information in a pending software project; wherein the pending software project includes multiple functional modules;

[0006] Determining, based on keywords included in the problem description information, first relevance between the problem description information and each of the functional modules;

[0007] According to the first correlation, a target functional module corresponding to the problem is determined.

[0008] Optionally, determining the first relevance between the problem description information and each of the functional modules according to the keywords included in the problem description information includes:

[0009] Recording historical statistical information related to the keyword in the software project to be processed; wherein the historical statistical information includes: the number of times the keyword is assigned to each of the functional modules, the number of occurrences of historical problem description information containing the keyword, and the weight of the keyword in the historical problem description information;

[0010] Determining, based on the historical statistical information, a second correlation between each of the keywords and each of the functional modules;

[0011] And, according to the second correlation corresponding to each functional module, the first correlation between the problem description information and each functional module is determined.

[0012] Optionally, determining a target functional module corresponding to the problem according to the first correlation includes:

[0013] comparing a maximum correlation among the plurality of first correlations with a preset correlation threshold;

[0014] If the comparison result shows that the maximum correlation is not less than the correlation threshold, the functional module corresponding to the maximum correlation is determined as the target functional module corresponding to the problem.

[0015] Optionally, the method further includes:

[0016] If the comparison result shows that the maximum correlation is less than the correlation threshold, then obtaining the relevant words corresponding to each of the functional modules according to a pre-configured correspondence relationship; wherein the correspondence relationship includes: a relationship between a functional module and at least one relevant word;

[0017] Matching the keywords extracted from the problem description information with the relevant words corresponding to each of the functional modules;

[0018] Determine the number of matching words that match the keyword in the relevant words corresponding to each functional module according to the matching results;

[0019] Determining whether there is only one maximum number of matching words among the plurality of matching word numbers;

[0020] If yes, determining the functional module corresponding to the maximum number of matching words as the target functional module corresponding to the question;

[0021] If not, determine a target functional module corresponding to the question from a plurality of functional modules corresponding to the maximum number of matching words.

[0022] Optionally, determining a target functional module corresponding to the question from a plurality of functional modules corresponding to the maximum number of matching words includes:

[0023] Determine multiple function modules corresponding to the maximum number of matching words as candidate function modules;

[0024] Determining the length of the matching words that match the keyword in the relevant words corresponding to each candidate functional module;

[0025] The candidate function module corresponding to the maximum matching word length is determined as the target function module corresponding to the question.

[0026] Optionally, the method further includes: determining the object corresponding to the target functional module as the target object of the question according to a pre-configured corresponding relationship; wherein the corresponding relationship includes: the relationship between the functional module and the object.

[0027] Optionally, the method further includes:

[0028] configuring project information of at least one software project;

[0029] Configuring object information of objects for maintaining multiple positions of the software project;

[0030] Module information of multiple functional modules used to maintain the software project is configured, wherein the module information includes: related words and objects of at least one position.

[0031] Optionally, the method further includes:

[0032] Assign weights to the related words, and synchronize the weighted related words to a preset vocabulary.

[0033] Optionally, the method further includes:

[0034] When the target-related words corresponding to the current functional module are added or deleted, the weights of the target-related words in the vocabulary are modified, and the number of times the target-related words are assigned to the current functional module is modified.

[0035] The present disclosure also provides a device for processing a problem, including:

[0036] A problem acquisition module is used to acquire problems and problem description information in the software project to be processed; wherein the software project to be processed includes multiple functional modules;

[0037] a correlation determination module, configured to determine, based on keywords included in the problem description information, a first correlation between the problem description information and each of the functional modules;

[0038] A target determination module is used to determine a target functional module corresponding to the problem based on the first correlation.

[0039] An embodiment of the present disclosure also provides an electronic device, which includes: a processor; a memory for storing instructions executable by the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the above method provided by the embodiment of the present disclosure.

[0040] An embodiment of the present disclosure further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the above method provided by the embodiment of the present disclosure.

[0041] The technical solution provided by the embodiments of the present disclosure has the following advantages over the prior art:

[0042] The problem processing method provided by the embodiment of the present disclosure first obtains the problem and problem description information in the software project to be processed, then determines the first correlation between the problem description information and each functional module, and determines the target functional module corresponding to the problem based on the first correlation.

[0043] Compared with the current method of directly assigning objects to problems, this technical solution fully considers that any problem occurring in a software project will belong to a certain functional module, and thus determines the corresponding target functional module for the problem in the software project to be processed based on the first correlation between the problem description information and each functional module; this method can not only accurately determine the target functional module corresponding to the problem, but also subsequently help to improve the matching between the assigned object and the target functional module. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0045] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 A schematic diagram of a model-based problem-solving process provided by an embodiment of the present disclosure;

[0047] Figure 2 A flowchart of a method for solving a problem provided by an embodiment of the present disclosure;

[0048] Figure 3 A schematic diagram of a configuration system architecture provided in an embodiment of the present disclosure;

[0049] Figure 4 A schematic diagram of an interface of an object provided in an embodiment of the present disclosure;

[0050] Figure 5 A schematic diagram of a configuration function module provided in an embodiment of the present disclosure;

[0051] Figure 6 A schematic diagram of an application scenario for problem solving provided by an embodiment of the present disclosure;

[0052] Figure 7 A flowchart of another method for solving a problem provided by an embodiment of the present disclosure;

[0053] Figure 8A schematic diagram of another application scenario for problem solving provided by an embodiment of the present disclosure;

[0054] Figure 9 A schematic diagram of a problem-solving device according to an embodiment of the present disclosure;

[0055] Figure 10 A schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0056] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features therein can be combined with each other in the absence of conflict.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0058] For feedback issues such as bugs (program errors) in software projects, deep learning models are currently used to assign follow-up targets to the issues. Figure 1 , the model needs to be trained with a large number of problem samples, and the information content of the problem samples includes problem summary, detailed description, submission information, error information, repair information, etc. However, the feedback problems actually collected usually have short text content and less effective information, and are often described in one sentence, which is difficult to meet the training requirements. As a result, the follow-up objects recommended by the model for the problem often do not match the functional module where the problem occurs. It can be seen that before assigning a follow-up object to the problem, it is necessary to first determine the functional module corresponding to the problem. Based on this, the embodiment of the present disclosure provides a method, device, equipment and medium for handling problems. To facilitate understanding of this embodiment, a method for handling problems disclosed in the embodiment of the present disclosure is first introduced in detail.

[0059] Figure 2 This is a flow chart of a method for processing a problem provided by an embodiment of the present disclosure. The method can be executed by a problem processing device, wherein the device can be implemented using software and / or hardware and can generally be integrated into an electronic device. Figure 2 As shown, the method includes:

[0060] Step S102: Obtain problems and problem description information in the software project to be processed; wherein the software project to be processed includes multiple functional modules.

[0061] Common software projects such as games and videos are usually composed of multiple functional modules to achieve different functions. For example, in a game, the modules may include purchasing props, controlling game characters, collecting cards, and many other functional modules.

[0062] During the development, testing, operation, and maintenance of software projects, user feedback is often received. The corresponding problem description information is generally bugs, suggestions, business consultations, and other unresolved information that requires staff follow-up. This embodiment can obtain problem description information in pending software projects through user submission operations or web crawlers.

[0063] Step S104: determining the first correlation between the problem description information and each functional module according to the keywords included in the problem description information.

[0064] It is understandable that any problem description information in the software project being processed is generated by a specific functional module. For example, the bug information about an abnormal number of game heroes is caused by an error in the functional module that implements hero selection during operation. Furthermore, in this embodiment, considering that functional modules and problem description information generally have distinct characteristics, functional module characteristics such as module name, related words, and functional description, and problem description characteristics such as keywords, etc., in this case, the first correlation between the problem description information and each functional module can be determined based on the characteristics of each functional module and the keywords in the problem description information.

[0065] Step S106: Determine the target functional module corresponding to the problem according to the first correlation.

[0066] In some embodiments, after determining the first correlation between the problem description information and each functional module, the sizes of multiple first correlations can be compared, and the functional module corresponding to the maximum correlation can be used as the target functional module to which the problem belongs; or, the multiple first correlations can be compared with a preset correlation threshold, and the target functional module to which the problem belongs can be assigned based on the comparison results.

[0067] The problem handling method provided by the embodiment of the present disclosure first obtains the problem and problem description information in the software project to be processed, and then determines the first correlation between the problem description information and each functional module based on the keywords included in the problem description information, and determines the target functional module corresponding to the problem based on the first correlation. Compared with the method of directly assigning follow-up objects to problems, the solution provided by the embodiment of the present disclosure takes into account that any problem occurring in the software project will be attributed to a certain functional module, and thus determines the corresponding target functional module for the problem in the software project to be processed based on the first correlation between the problem description information and each functional module; this solution can not only accurately determine the target functional module corresponding to the problem, but also subsequently help to improve the matching between the assigned object and the target functional module.

[0068] After determining the target functional module corresponding to the problem, the method provided in this embodiment may further include: determining the object corresponding to the target functional module as the target object of the problem according to a pre-configured correspondence relationship; wherein the correspondence relationship includes but is not limited to: the relationship between the functional module and the object.

[0069] In actual applications, each functional module will be configured with at least one object to maintain its function, and the object is generally a staff member engaged in the development and maintenance of the current functional module. Based on this, the present embodiment can configure the correspondence between the functional modules and their objects according to the actual working scenario. According to the correspondence, the object corresponding to the target functional module is determined. Since the object is specifically used to maintain the function of the target functional module, the object is suitable for following up on the problems belonging to the target functional module. Therefore, the object corresponding to the target functional module is determined as the target object for following up on the problem; then, a reminder message for following up on the problem can be sent to the target object. The object of the above-mentioned functional module and the target object of the problem can be at least one, and when there are multiple people, they can be objects of different positions, so that multiple positions can collaboratively handle the problem.

[0070] For ease of understanding, based on the project characteristics of different software projects, the present disclosure provides a system architecture configuration solution, which includes multiple different software projects and functional modules and objects corresponding to each software project. Figure 3 This embodiment includes: configuring project information of at least one software project; configuring object information of objects of multiple positions for maintaining the software project; configuring module information of multiple functional modules for maintaining the software project, and the module information includes: related words and objects of at least one position.

[0071] In this embodiment, software projects, functional modules, and objects can be configured through the web front-end. Taking software project 1 as an example, the configured project information may include: functional modules and objects required to maintain software project 1, project name, problem submission method, project reminder information, and other project personalized configuration content.

[0072] The submission method of the above-mentioned question is to send the question to the target object after determining the target object for following up the question. Exemplarily, one submission method is to automatically send the question to the target object, which can quickly and promptly feedback the user's question. Another submission method is to first send the question to the preset reviewer. The reviewer can review the validity of the question, whether it is repeated, and whether the target object assigned to the question is accurate. After the reviewer confirms that the review is passed, the question is sent to the target object. In addition, if the reviewer believes that the current target object is not accurate, such as having resigned, transferred, or not professionally qualified, an object replacement operation can be initiated; in this case, according to the object replacement operation, the current target object is replaced with a new target object. This submission method can handle repeated and invalid questions, reduce the workload of the object, and improve the accuracy of the allocation by reviewing the target object.

[0073] Project reminder information includes reminder cycles and duty schedules. Once the target individuals for follow-up issues are identified, reminders are sent to them according to the preset reminder cycle. When configuring a large number of objects for a software project or functional module, you can group them and assign them work periods based on duty schedules to optimize staffing.

[0074] In this embodiment, the objects configured for multiple positions of maintaining the software project can refer to Figure 4 In the example interface shown, the object information includes but is not limited to: the object's name, position information, contact information, such as common email addresses and mobile phone numbers, which are used to contact the object. Figure 4 The interface also features buttons for adding and deleting objects, allowing you to add new objects and delete existing ones, ensuring that the configured objects match the actual work scenario. A single issue often requires collaborative follow-up from multiple positions, such as planning, development, and testing. Based on this, you can assign objects from multiple positions to the issue for collaborative follow-up based on the configured position information.

[0075] In this embodiment, Figure 3As shown, module information for multiple functional modules used to maintain a software project is configured. The module information includes, but is not limited to, the functional module name, related terms, functional description, and at least one position object. Software projects to be processed typically have a large number of objects. This embodiment configures the functional module objects and assigns all objects to different functional modules, thereby improving compatibility between objects and functional modules.

[0076] Based on the module information of each functional module, a correspondence is established between the functional module, the related terms, and the object by configuring the functional module based on its corresponding related terms and at least one position object. Furthermore, in combination with the relationship between the functional module and the software project, the above correspondence can also represent the relationship between the software project, the functional module, the related terms, and the object. This correspondence can be used to assign functional modules and target objects to questions.

[0077] In this embodiment, the relevant words of the functional module can be synchronized and updated to a preset vocabulary. The synchronization method includes: assigning weights to the relevant words and synchronizing the weighted relevant words to the preset vocabulary. The above vocabulary can be a TF-IDF (Term Frequency–Inverse Document Frequency) vocabulary; this vocabulary is used to record the relevant words of the functional module and the weights of the relevant words.

[0078] In one embodiment, in order to make the related words more accurately reflect the corresponding functional modules, related words can be added or deleted. When adding or deleting target related words corresponding to the current functional module, the weight of the target related words in the vocabulary is modified. In a specific example, when adding target related words, the added target related words can generally accurately describe at least one feature of the current functional module, such as Figure 5 The related word "weapon value" in the vocabulary is directly related to the "weapon" functional module. Based on this, a certain correction value can be added to the weight of "weapon value" in the vocabulary. Correspondingly, when a target-related word is deleted, it indicates that the deleted target-related word has a low relevance to the current functional module, and thus the weight of the deleted target-related word in the vocabulary is reduced by a certain correction value.

[0079] This embodiment synchronizes the relevant words into the vocabulary, which can ensure that the configured related words can be correctly obtained during the process of extracting keywords from the problem description information; it increases / decreases the weights of the target related words in the vocabulary by a certain correction value, ensuring that when extracting keywords from the problem description information, the keywords corresponding to the related words with high weights can be extracted first.

[0080] In the above configuration embodiment, based on the above embodiment, software projects, functional modules, objects, and related words can be flexibly configured, and various configuration information can be added, deleted or adjusted to make the configuration results of the system architecture conform to the actual work scenario, thereby realizing a configurable system architecture that supports multi-position collaboration.

[0081] This embodiment stores the configured system architecture so that when a problem to be processed is obtained, data support is provided for the allocation of the problem based on the system architecture.

[0082] According to the above embodiment, a method for determining the first correlation between the problem description information and each functional module is provided herein, with reference to the following content.

[0083] Keywords that appear in a preset vocabulary are extracted from the problem description information; wherein the vocabulary is used to record relevant words of the functional module and the weights of the relevant words.

[0084] Specifically, the initial keywords that appear in the vocabulary are extracted from the problem description information; the weights of the related words that are the same as the initial keywords in the vocabulary are used as the weights of the initial keywords; and the multiple initial keywords with the highest weights or the multiple initial keywords with weights higher than a preset threshold are used as the final extracted keywords.

[0085] The first relevance between the problem description information and each functional module is determined based on the keywords.

[0086] In one implementation, historical statistical information related to keywords in the software project to be processed is recorded; wherein the historical statistical information includes: the number of times the keyword is assigned to each functional module, the number of occurrences of historical problem description information containing the keyword, and the weight of the keyword in the historical problem description information; the weight of the keyword in the historical problem description information is represented by the weight of the related words of the keyword in the vocabulary.

[0087] Exemplarily, the historical statistical information may be pre-configured to be stored in an associated form of software project, functional module, keyword, number of allocations, number of occurrences, and weight.

[0088] In the above historical statistical information, the more times the problem description information containing the keyword j in the software project to be processed is assigned to the functional module i, the higher the correlation between the keyword j and the functional module i. Therefore, this embodiment can determine the second correlation between each keyword and each functional module based on the historical statistical information, which can be specifically referred to as shown in the following formula (1).

[0089]

[0090] Among them, r i,jrepresents the second correlation between keyword j and functional module i, n i,j represents the number of occurrences of historical problem description information containing keyword j in functional module i, n j represents the total number of occurrences of historical problem description information containing keyword j in the software project to be processed; min(log n i,j ,1) It is used to reduce the impact of a small number of statistical samples on the results of the second correlation calculation.

[0091] The second correlation between each keyword and each functional module is calculated in the above manner, and then the first correlation between the problem description information and each functional module is determined based on the second correlation corresponding to each functional module.

[0092] Specifically, the first correlation between the problem description information and a certain functional module can be expressed as the sum of the second correlations of all keywords in the problem description information and the functional module. Refer to the following formula (2):

[0093] R i =∑ j r i,j *w j (2)

[0094] Among them, R i Indicates the first correlation between the problem description information and the functional module i, r i, represents the second correlation between keyword j and functional module i, w j Represents the weight of keyword j in the question description information.

[0095] The problem is assigned to a target functional module according to the first correlation between the problem description information and each functional module.

[0096] In one embodiment, a maximum correlation among the plurality of first correlations is compared with a preset correlation threshold. A determination is made as to whether the maximum correlation is not less than the preset correlation threshold. If the comparison result shows that the maximum correlation is not less than the correlation threshold, the functional module corresponding to the maximum correlation is determined as the target functional module corresponding to the problem.

[0097] In one embodiment, if the comparison result shows that the maximum correlation is less than the correlation threshold, then the relevant words corresponding to each functional module are obtained respectively according to the corresponding relationship; the comparison relationship includes: the relationship between the functional module and at least one relevant word.

[0098] The keywords extracted from the problem description information are matched with the relevant words corresponding to each functional module respectively; and the number of matching words in the relevant words corresponding to each functional module that match the keywords is determined based on the matching results.

[0099] Specifically, the keywords in the problem description are matched with the relevant words in the functional modules. This means that the number of words that match the keywords among the multiple relevant words in the functional modules is counted, as well as the number of matching words. For example, there are three relevant words that match the keywords in functional module 1, and five relevant words that match the keywords in functional module 2.

[0100] Determine whether there is only one maximum number of matching words among the multiple matching words. If so, that is, there is only one functional module that matches the largest number of keywords, indicating that the functional module has the greatest relevance to the problem description information, then the functional module corresponding to the largest number of matching words is determined as the target functional module corresponding to the problem. If not, that is, there are at least two functional modules that match the same and largest number of keywords, indicating that the relevance of these functional modules to the problem description information is similar, then the target functional module corresponding to the problem is determined from the multiple functional modules corresponding to the largest number of matching words. Exemplarily, a functional module can be randomly determined from the multiple functional modules corresponding to the largest number of matching words as the target functional module to which the problem belongs.

[0101] In order to improve the accuracy, this embodiment can determine the target functional module corresponding to the question from multiple functional modules corresponding to the maximum number of matching words in the following manner.

[0102] The plurality of function modules corresponding to the maximum number of matching words are determined as candidate function modules, and the length of the matching words that match the keyword in the related words corresponding to each candidate function module is determined.

[0103] In specific implementation, the length of the matching words corresponding to each candidate functional module is determined based on the word length of the keyword and the number of occurrences of the historical problem description information containing the keyword in each functional module, referring to the following formula (3):

[0104] M i =∑ j n i,j *l j (3)

[0105] Among them, M i The length of the matching word corresponding to the candidate function module i, n i,j represents the number of occurrences of historical problem description information containing keyword j in candidate functional module i, l j Indicates the word length of keyword j, which can be expressed in characters. In order to simplify the description, n i,j It can be referred to as word frequency.

[0106] The product of the word frequency and word length of all keywords in the current functional module is summed to obtain the matching word length corresponding to the current functional module.

[0107] Next, the candidate functional module corresponding to the maximum matching word length is determined as the target functional module corresponding to the question.

[0108] like Figure 6 In the application scenario shown above, in the embodiment of determining the target functional module to which the problem belongs, the system architecture including software projects, functional modules and objects is configured in advance, the TF-IDF vocabulary is set, and historical statistical information is recorded. Thus, when the problem and the description information of the question are obtained, the vocabulary can be used to extract the keywords of the problem description information, and the target functional module corresponding to the question can be determined based on the keywords and the historical statistical information related to the keywords. Compared with the existing allocation method of utilizing models, this embodiment does not require the problem description information to provide detailed information content, nor does it require a large number of questions, which simplifies the preparation process of the question. The keywords in the problem description information can be fully and effectively utilized by using the relevant words and historical statistical information of the functional module.

[0109] Furthermore, this embodiment, based on the functional modules and objects in the system architecture and the correspondence between the target functional module and the objects of at least one position, identifies the object of at least one position corresponding to the target functional module as the target object for follow-up issues. This embodiment takes into account issues involving multiple positions and identifies the objects of at least one position as target objects for follow-up issues, enabling collaboration among multiple positions to better address issues.

[0110] According to the above embodiments, a Figure 7 The method for handling the problem shown includes a process from obtaining the problem to determining the target object for following up the problem.

[0111] Step S701: Obtain problems and problem description information in the software project to be processed.

[0112] Step S702: extract keywords that appear in a preset vocabulary from the problem description information.

[0113] Step S703: Based on the historical statistical information related to the keywords in the software project to be processed, determine the second correlation (r i,j ).

[0114] Step S704: Determine the first correlation (R) between the problem description information and each functional module according to the second correlation corresponding to each functional module. i ).

[0115] Step S705, compare the maximum correlation among the multiple first correlations with the preset correlation threshold; if the comparison result is that the maximum correlation is not less than the correlation threshold, execute the following step S06; if the comparison result is that the maximum correlation is less than the correlation threshold, execute the following step S07.

[0116] Step S706: Determine the functional module corresponding to the maximum correlation as the target functional module corresponding to the problem.

[0117] Step S707 : Match the keywords in the problem description information with the relevant words corresponding to each functional module respectively, and determine the number of matching words in the relevant words corresponding to each functional module that match the keywords according to the matching results.

[0118] Specifically, according to the correspondence between the functional module and at least one related word, the related words corresponding to each functional module are obtained respectively; the keywords extracted from the problem description information are matched with the related words corresponding to each functional module respectively; and the number of matching words in the related words corresponding to each functional module that match the keywords is determined according to the matching results.

[0119] Step S708 , determining whether there is only one maximum number of matching words among the multiple numbers of matching words; if so, executing the following step S709 ; if not, executing the following step S710 .

[0120] Step S709: Determine the function module corresponding to the maximum number of matching words as the target function module corresponding to the question.

[0121] Step S710 : From the plurality of function modules corresponding to the maximum number of matching words, determine the function module corresponding to the maximum matching word length as the target function module corresponding to the question.

[0122] Specifically, multiple function modules corresponding to the maximum number of matching words are determined as candidate function modules; the length of matching words that match the keywords in the relevant words corresponding to each candidate function module is determined; and the candidate function module corresponding to the maximum matching word length is determined as the target function module corresponding to the question.

[0123] Step S711 : According to the correspondence between the functional modules and the objects, the object corresponding to the target functional module is determined as the target object of the question.

[0124] Reference Figure 8As shown, in the process of assigning a target object to a problem, if the target functional module or target object cannot be determined for the problem, or if the determined target functional module or target object is inaccurate, then the target functional module and target object can be assigned to the problem by manual assignment. The manual assignment method is to send the problem to a preset assignment person, and have the assignment person perform operations to determine the target functional module and target object of the problem. A specific example is that after the target object receives the reminder information for following up on the problem, if it is found that it is not appropriate to follow up on the problem, then a request information for reallocation is fed back. In this embodiment, the problem is sent to the preset assignment person according to the request information, and the assignment person performs an adjustment operation. In response to the adjustment operation, the assignment result of the problem is adjusted, and a corresponding new target functional module and a new target object are assigned to the problem.

[0125] After determining the target functional module corresponding to the question, the method provided in this embodiment further includes: assigning keywords extracted from the question description information to the target functional module as related words of the target functional module. Figure 8 This also includes correcting the following historical statistical information: the number of times a keyword was assigned to each functional module, the number of times the keyword appeared in historical problem descriptions, and correcting the weight of the keyword in historical problem descriptions in the vocabulary. The number of assignments and the number of appearances are generally calculated by adding one, while the weight is generally increased by a certain correction value.

[0126] In actual applications, whether it is assigning keywords in the problem description information to the functional module corresponding to the problem, configuring related words in the functional module, or manually adjusting the assignment results, it can be considered as an event of adding or deleting target-related words corresponding to the current functional module.

[0127] When adding or deleting target-related words corresponding to the current functional module, on the one hand, the weights of the target-related words in the vocabulary are modified, and on the other hand, the number of times the target-related words are assigned to the current functional module is modified.

[0128] This embodiment can help optimize subsequent allocation results by correcting statistical information such as weights and allocation times, and further improve the accuracy of allocating corresponding functional modules and target objects to problems.

[0129] According to the above embodiments, taking the software project to be processed as a game APP (Application) as an example, a specific example of a method for handling a problem is provided, as shown below.

[0130] In this embodiment, the game app includes functional modules such as a treasure chest system, an activity system, and a scenario system. A game app question and the following question description are obtained: The treasure chest in the central square has a chance of receiving two rewards after opening. As shown in Table 1, keywords that appear in the TF-IDF vocabulary are extracted from the question description, and the weights of the keywords recorded in the TF-IDF vocabulary are obtained.

[0131] Table 1

[0132] Keywords Treasure Chest Open award square Get Weight 11.74 7.77 7.57 7.11 6.91

[0133] Record historical statistical information related to keywords in the game APP; as shown in Table 2 below, some historical statistical information is displayed, namely: the number of times the keyword is assigned to the functional module n i,j .

[0134] Table 2

[0135] Functional modules Keywords <![CDATA[Number of allocations n i,j > Treasure Chest System Treasure Chest 9 Treasure Chest System Open 6 Treasure Chest System award 3 Treasure Chest System Get 2 Activity System Open 4 Activity System award 2 Activity System Treasure Chest 2 Activity System square 2 Scene System square 7 Scene System Open 1

[0136] Taking the keyword "treasure box" as an example, its historical statistical information may include: the number of times the treasure box is allocated to the three functional modules of the treasure box system, the activity system, and the scene system is 9 times, 2 times, and 0 times respectively, and the number of occurrences of the historical question description information containing the treasure box is 9+2+0=11 times; based on the above historical statistical information and formula (1) in the above embodiment, the second correlation between the keyword treasure box and the treasure box system is calculated as:

[0137]

[0138] According to the above method, the second correlation between each keyword and each functional module is calculated. For example: the second correlation between opening and the treasure chest system is: 0.424446137, the second correlation between the reward and the treasure chest system is: 0.286272753, the second correlation between acquisition and the treasure chest system is: 0.301029996, the second correlation between opening and the activity system is: 0.218930906, the second correlation between the reward and the activity system is: 0.120411998, the second correlation between the treasure chest and the activity system is: 0.054732726, the second correlation between the square and the activity system is: 0.066895555, the second correlation between the square and the scene system is: 0.657298476, and the second correlation between opening and the scene system is: 0.

[0139] Then, referring to formula (2), the first correlation R between the problem description information and each functional module is determined according to the second correlation corresponding to each functional module. i Among them, the first correlation between the problem description information and the treasure chest system is:

[0140] R i =11.71*0.780743871+7.77*0.424446137+7.57*0.286272753+7.11*0+6.91*0.301029996=16.69. Based on this method, the first correlation between the problem description information and the activity system is determined to be 3.73, and the first correlation between the problem description information and the scenario system is determined to be 4.67 (the specific calculation process is not further explained).

[0141] The maximum correlation among multiple first correlations, i.e. 16.69, is compared with a preset correlation threshold (such as 16); if the comparison result is 16.69>16 and the maximum correlation is not less than the correlation threshold, the functional module of the treasure chest system corresponding to the maximum correlation is determined as the target functional module corresponding to the problem, or in other words, the target functional module corresponding to the problem is determined to be the treasure chest system, and the above problem belongs to the treasure chest system.

[0142] Next, this embodiment can determine the object corresponding to the treasure box system as the target object of the question based on the pre-configured correspondence.

[0143] Referring to Table 3, the corresponding relationship between each functional module and object is given.

[0144] Table 3

[0145] Functional modules QA plan Client Program Server program Treasure Chest System Xiao Zhang Xiao Wang Xiao Li Xiao Zhao Activity System Xiao Sun Small money Xiao Zhou Xiao Wu Scene System Xiao Zheng Xiao Feng pony Xiao Chen

[0146] According to the above correspondence, the objects corresponding to the treasure box system: Xiao Zhang, Xiao Wang, Xiao Li and Xiao Zhao are determined as the target objects of the problem. In actual application, a reminder message for following up the problem can be sent to the target objects so that the target objects follow up and handle the problem.

[0147] In this embodiment, assigning keywords from the problem description information to the target functional module can be considered as adding target-related words corresponding to the target functional module. Therefore, the number of times target-related words are assigned to the target functional module is revised. The target functional module is the treasure chest system, and the target-related words are the keywords extracted from the problem description information: treasure chest, open, reward, obtain, and square. Compared to Table 1, the revised number of times keywords are assigned to the functional module can be seen in Table 4 below, showing an increase of 1 in each case.

[0148] Table 4

[0149] Functional modules Target-related words <![CDATA[Number of allocations n i,j > Treasure Chest System Treasure Chest 10 Treasure Chest System Open 7 Treasure Chest System award 4 Treasure Chest System Get 3 Treasure Chest System square 1

[0150] So far, this embodiment provides a specific example of the problem handling method from obtaining the problem and problem description information, to determining the target functional module corresponding to the problem, determining the target object for following up the problem, correcting the number of times the target-related words are assigned to the current functional module, and other processes.

[0151] In summary, the problem handling method provided by the embodiment of the present disclosure utilizes the correspondence between the objects of software projects, functional modules, at least one position and related words to provide a method for flexibly configuring the system architecture, so that the problem handling method provided by this embodiment can be freely and flexibly applied to any software project; at the same time, considering the correlation between the keywords of the problem description information and the functional modules, the second correlation between the keywords and the functional modules is first determined, and then the second correlation between the problem description information and the functional modules is determined based on this, and the second correlation is used to assign the corresponding target functional module to the problem. This process makes full and effective use of the keywords in the problem description information. Then, the object of at least one position corresponding to the target functional module is determined as the target object, which improves the accuracy of the allocation and realizes multi-position collaboration. In addition, by correcting the weights of the historical statistical information and the related words in the vocabulary, the subsequent allocation results can be optimized and the allocation accuracy can be further improved.

[0152] Based on the problem-solving method provided in the above embodiments, an embodiment of the present disclosure provides a problem-solving device. Figure 9 This is a schematic diagram of the structure of a problem processing device provided by an embodiment of the present disclosure. The device can be implemented by software and / or hardware and can generally be integrated into an electronic device. It can determine the corresponding target functional module for the problem by executing the problem processing method. Figure 9 As shown, the device includes:

[0153] The problem acquisition module 902 is used to acquire problems and problem description information in the software project to be processed; wherein the software project to be processed includes multiple functional modules;

[0154] A relevance determination module 904 is configured to determine, based on keywords included in the problem description information, a first relevance between the problem description information and each functional module;

[0155] The target determination module 906 is used to determine the target functional module corresponding to the problem according to the first correlation.

[0156] In one embodiment, the correlation determination module 904 is specifically configured to:

[0157] Extract keywords that appear in a preset vocabulary from the problem description information; wherein the vocabulary is used to record relevant words and weights of the functional modules; and determine the first relevance of the problem description information to each functional module based on the keywords.

[0158] In one embodiment, the correlation determination module 904 is specifically configured to:

[0159] Record historical statistical information related to keywords in the software project to be processed; wherein the historical statistical information includes: the number of times the keywords are assigned to each functional module, the number of occurrences of historical problem description information containing the keywords, and the weight of the keywords in the historical problem description information; based on the historical statistical information, determine the second correlation between each keyword and each functional module; and, based on the second correlation corresponding to each functional module, determine the first correlation between the problem description information and each functional module.

[0160] In one embodiment, the target determination module 906 is specifically configured to:

[0161] The maximum correlation among the multiple first correlations is compared with a preset correlation threshold; if the comparison result is that the maximum correlation is not less than the correlation threshold, the functional module corresponding to the maximum correlation is determined as the target functional module corresponding to the problem.

[0162] In one embodiment, the target determination module 906 is specifically configured to:

[0163] If the comparison result is that the maximum correlation is less than the correlation threshold, then the relevant words corresponding to each functional module are obtained respectively according to the pre-configured correspondence relationship; wherein the correspondence relationship includes: the relationship between the functional module and at least one relevant word; the keywords extracted from the problem description information are matched with the relevant words corresponding to each functional module respectively; the number of matching words that match the keywords in the relevant words corresponding to each functional module is determined according to the matching result; it is judged whether there is only one maximum number of matching words among multiple matching word numbers; if so, the functional module corresponding to the maximum number of matching words is determined as the target functional module corresponding to the problem; if not, the target functional module corresponding to the problem is determined from multiple functional modules corresponding to the maximum number of matching words.

[0164] In one embodiment, determining a target functional module corresponding to the question from a plurality of functional modules corresponding to the maximum number of matching words includes:

[0165] Determine multiple function modules corresponding to the maximum number of matching words as candidate function modules; determine the matching word length that matches the keyword in the relevant words corresponding to each candidate function module; and determine the candidate function module corresponding to the maximum matching word length as the target function module corresponding to the question.

[0166] In one embodiment, the apparatus further includes an object determination module, which is configured to: determine the object corresponding to the target functional module as the target object of the question according to a preset correspondence between the functional modules and the objects.

[0167] In one embodiment, the apparatus further comprises a configuration module configured to:

[0168] Configure project information of at least one software project; configure object information of objects of multiple positions for maintaining the software project; configure module information of multiple functional modules for maintaining the software project, the module information includes: related words and objects of at least one position.

[0169] In one embodiment, the configuration module is further configured to assign weights to related words, and synchronize the weighted related words to a preset vocabulary.

[0170] In one embodiment, the configuration module is also used to: when adding or deleting target-related words corresponding to the current functional module, modify the weights of the target-related words in the vocabulary, and modify the number of times the target-related words are assigned to the current functional module.

[0171] The problem processing device provided in the embodiments of the present disclosure can execute the problem processing method provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0172] Figure 10 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Figure 10 As shown, the electronic device 1000 includes one or more processors 1001 and a memory 1002 .

[0173] The processor 1001 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 1000 to perform desired functions.

[0174] The memory 1002 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 1001 may execute the program instructions to implement the method for processing the problem of the embodiment of the present disclosure described above and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0175] In one example, the electronic device 1000 may further include an input device 1003 and an output device 1004 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0176] In addition, the input device 1003 may also include, for example, a keyboard, a mouse, and the like.

[0177] The output device 1004 can output various information to the outside, including determined distance information, direction information, etc. The output device 1004 can include, for example, a display, a speaker, a printer, a communication network and its connected remote output device, etc.

[0178] Of course, to simplify, Figure 10 Only some of the components related to the present disclosure in the electronic device 1000 are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, according to specific application scenarios, the electronic device 1000 may further include any other appropriate components.

[0179] In addition to the above methods and devices, the embodiments of the present disclosure may also be a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor, the processor executes the method for processing the problem described in the embodiments of the present disclosure.

[0180] The computer program product may be written in any combination of one or more programming languages ​​to implement the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0181] In addition, the embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processor executes the problem processing method provided by the embodiment of the present disclosure.

[0182] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0183] The embodiments of the present disclosure further provide a computer program product, including a computer program / instruction, which implements the method in the embodiments of the present disclosure when executed by a processor.

[0184] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0185] The foregoing description is intended only to provide specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments described herein, but rather to be construed in the broadest manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for solving a problem, characterized in that: include: Obtaining problems and problem description information in a pending software project; wherein the pending software project includes multiple functional modules, and the multiple functional modules are used to implement different functions of the pending software project; Determining first relevance between the problem description information and each of the function modules based on characteristics of each of the function modules and keywords included in the problem description information; wherein the characteristics of the function modules include related words; the keywords are keywords extracted from the problem description information and appear in a preset vocabulary, the vocabulary being used to record related words of the function modules and weights of the related words; Determining a target functional module corresponding to the problem based on the first correlation; The determining, based on the characteristics of each functional module and the keywords included in the problem description information, a first correlation between the problem description information and each functional module, includes: Determining, based on historical statistical information related to the keyword in the software project to be processed, a second correlation between each of the keywords and each of the functional modules; The first correlation between the problem description information and each of the functional modules is determined according to the second correlation corresponding to each of the functional modules.

2. The method according to claim 1, characterized in that The determining, based on the keywords included in the problem description information, the first relevance between the problem description information and each of the functional modules, includes: Extracting keywords that appear in a preset vocabulary from the problem description information; wherein the vocabulary is used to record relevant words of the functional module and the weights of the relevant words; A first correlation between the problem description information and each of the functional modules is determined based on the keywords.

3. The method according to claim 2, characterized in that The determining, based on the keywords, the first relevance between the problem description information and each of the functional modules includes: Recording historical statistical information related to the keyword in the software project to be processed; wherein the historical statistical information includes: the number of times the keyword is assigned to each of the functional modules, the number of occurrences of historical problem description information containing the keyword, and the weight of the keyword in the historical problem description information; Determining, based on the historical statistical information, a second correlation between each of the keywords and each of the functional modules; And, according to the second correlation corresponding to each functional module, the first correlation between the problem description information and each functional module is determined.

4. The method according to claim 1, wherein Determining the target functional module corresponding to the problem according to the first correlation includes: comparing a maximum correlation among the plurality of first correlations with a preset correlation threshold; If the comparison result shows that the maximum correlation is not less than the correlation threshold, the functional module corresponding to the maximum correlation is determined as the target functional module corresponding to the problem.

5. The method according to claim 4, characterized in that The method further comprises: If the comparison result shows that the maximum correlation is less than the correlation threshold, then obtaining the relevant words corresponding to each of the functional modules according to a pre-configured correspondence relationship; wherein the correspondence relationship includes: a relationship between a functional module and at least one relevant word; Matching the keywords extracted from the problem description information with the relevant words corresponding to each of the functional modules; Determine the number of matching words that match the keyword in the relevant words corresponding to each functional module according to the matching results; Determining whether there is only one maximum number of matching words among the plurality of matching word numbers; If yes, determining the functional module corresponding to the maximum number of matching words as the target functional module corresponding to the question; If not, determine a target functional module corresponding to the question from a plurality of functional modules corresponding to the maximum number of matching words.

6. The method according to claim 5, characterized in that The determining of a target functional module corresponding to the question from the plurality of functional modules corresponding to the maximum number of matching words includes: Determine multiple function modules corresponding to the maximum number of matching words as candidate function modules; Determining the length of the matching words that match the keyword in the relevant words corresponding to each candidate functional module; The candidate function module corresponding to the maximum matching word length is determined as the target function module corresponding to the question.

7. The method according to claim 1, characterized in that The method further comprises: According to a pre-configured correspondence relationship, the object corresponding to the target functional module is determined as the target object of the problem; wherein the correspondence relationship includes: a relationship between a functional module and an object.

8. The method according to claim 1, characterized in that The method further comprises: configuring project information of at least one software project; Configuring object information of objects for maintaining multiple positions of the software project; Module information of multiple functional modules used to maintain the software project is configured, wherein the module information includes: related words and objects of at least one position.

9. The method according to claim 8, characterized in that The method further comprises: Assign weights to the related words, and synchronize the weighted related words to a preset vocabulary.

10. The method according to claim 9, characterized in that The method further comprises: When the target-related words corresponding to the current functional module are added or deleted, the weights of the target-related words in the vocabulary are modified, and the number of times the target-related words are assigned to the current functional module is modified.

11. A problem handling device, characterized in that: include: A problem acquisition module is used to acquire problems and problem description information in the software project to be processed; wherein the software project to be processed includes multiple functional modules, and the multiple functional modules are used to implement different functions of the software project to be processed; a relevance determination module, configured to determine a first relevance between the problem description information and each of the functional modules based on characteristics of the functional modules and keywords included in the problem description information; wherein the characteristics of the functional modules include related words; the keywords are keywords extracted from the problem description information and appear in a preset vocabulary, wherein the vocabulary is configured to record related words of the functional modules and weights of the related words; a target determination module, configured to determine a target functional module corresponding to the problem based on the first correlation; The correlation determination module is further configured to: Determining, based on historical statistical information related to the keyword in the software project to be processed, a second correlation between each of the keywords and each of the functional modules; The first correlation between the problem description information and each of the functional modules is determined according to the second correlation corresponding to each of the functional modules.

12. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a computer device, the computer device implements the method according to any one of claims 1 to 10.

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