Category Determination Method, Device, Electronic Device, and Storage Medium

Abstract

The present disclosure provides a method, apparatus, electronic device, and storage medium for category determination. The method includes: obtaining a plurality of abnormal stack contents, each stack content indicating a corresponding abnormal problem, parsing each stack content to obtain the application package name, theme information, and call stack information corresponding to each stack content, determining at least one target stack content belonging to the same application according to the application package name, determining the category of the abnormal problem indicated by the target stack content and the number of abnormal problems in each category according to the theme information and call stack information corresponding to the target stack content, and classifying and statistically counting the abnormal problems of the same application based on various information obtained by parsing the stack contents with abnormalities and based on the matching degree of the information, thereby improving the accuracy and efficiency of category recognition of abnormal problems.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, an electronic device, and a storage medium for determining categories. Background Art

[0002] When an Android electronic device occasionally has a Java crash problem during operation, a corresponding stack file is usually generated for research and development analysis. When the quantity level of the products is relatively large, there will be a large number of abnormal files, which will consume manpower and a lot of time to check the abnormal files one by one to confirm which module has which type of problem, and notify the research and development to solve it according to the priority based on the identified abnormal problem situation, with low efficiency. At the same time, when the data volume is too large, it is impossible to check and identify the categories of problems one by one, and there will be omissions, and more serious problems cannot be solved in a timely and accurate manner. Summary of the Invention

[0003] The present disclosure aims to at least solve one of the technical problems in the related art to some extent.

[0004] To this end, the present disclosure provides a method, an apparatus, an electronic device, and a storage medium for determining categories to improve the efficiency and accuracy of problem category identification.

[0005] An embodiment of one aspect of the present disclosure provides a method for determining categories, including:

[0006] Obtaining stack contents of multiple exceptions; wherein each of the stack contents indicates a corresponding exception problem;

[0007] Parsing each of the stack contents to obtain the application package name, theme information, and call stack information corresponding to each of the stack contents;

[0008] Determining at least one target stack content belonging to the same application according to the application package name;

[0009] Determining the category of the exception problem indicated by each of the target stack contents and the number of exception problems in each category according to the theme information and call stack information corresponding to each of the target stack contents.

[0010] An embodiment of another aspect of the present disclosure provides a device for determining categories, including:

[0011] An obtaining module, configured to obtain stack contents of multiple exceptions; wherein each of the stack contents indicates a corresponding exception problem;

[0012] A parsing module, configured to parse each of the stack contents to obtain the application package name, theme information, and call stack information corresponding to each of the stack contents;

[0013] A determining module, configured to determine at least one target stack content belonging to the same application according to the application package name;

[0014] The determining module is further configured to determine the category of the exception problems indicated by each of the target stack contents and the number of exception problems in each category according to the theme information and call stack information corresponding to each of the target stack contents.

[0015] Another embodiment of the present disclosure provides an electronic device, including:

[0016] At least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the foregoing aspect.

[0018] Another embodiment of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause the computer to execute the method described in the foregoing aspect.

[0019] Another embodiment of the present disclosure provides a computer program product, including computer instructions, where the computer instructions implement the method described in the foregoing aspect when executed by a processor.

[0020] The technical solution provided by the embodiments of the present disclosure has the following beneficial effects:

[0021] Obtain the stack contents of multiple exceptions, each stack content of the exception indicates the corresponding exception problem, parse each stack content to obtain the application package name, theme information and call stack information corresponding to each stack content, and determine at least one target stack content belonging to the same application according to the application package name. According to the theme information and call stack information corresponding to each target stack content, determine the category of the exception problems indicated by each target stack content and the number of exception problems in each category. Based on multiple types of information obtained by parsing the stack contents with exceptions, and based on the matching degree of the information, automatically classify and identify the exception problems of the same application and count the number, improving the accuracy and efficiency of the category identification of the exception problems.

[0022] The additional aspects and advantages of the present disclosure will be partly given in the following description, partly will become obvious from the following description, or will be understood through the practice of the present disclosure. Description of the Drawings

[0023] The above and / or additional aspects and advantages of the present disclosure will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0024] Figure 1 A flowchart of a method for determining a category provided by an embodiment of the present disclosure;

[0025] Figure 2 A flowchart of another method for determining a category provided by an embodiment of the present disclosure;

[0026] Figure 3 A flowchart of another method for determining a category provided by an embodiment of the present disclosure;

[0027] Figure 4 A schematic structural diagram of a category determination device provided by an embodiment of the present disclosure;

[0028] Figure 5 A structural block diagram of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0029] The embodiments of the present disclosure will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0030] The category determination method, device, electronic device, and storage medium of the embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0031] Figure 1 A flowchart of a method for determining a category provided by an embodiment of the present disclosure.

[0032] As Figure 1 shown, the method includes the following steps:

[0033] Step 101, obtain the stack contents of multiple exceptions, where each stack content indicates a corresponding exception problem.

[0034] Among them, the stack content of each exception indicates a corresponding exception problem, that is to say, each stack content is generated when an exception occurs. When an exception occurs, for example, a java crash, that is, an exception problem of java crash, a file corresponding to the stack content of the exception problem will be generated in the DropBox directory. Among them, DropBox is a mechanism introduced by Android to persistently store system data, mainly used to record the logs when serious problems occur in the kernel, system processes, user processes, etc. during the operation of Android. In the embodiments of the present disclosure, the stack contents of the exceptions included in each file are obtained from each file corresponding to the exception problem stored in the DropBox directory.

[0035] Step 102: Parse the content of each stack to obtain the application package name, theme information, and call stack information corresponding to the content of each stack.

[0036] In the embodiments of the present disclosure, the content of each stack is parsed to obtain key information indicating each exception problem, including the application package name, theme information, and call stack information. Among them, the application package name indicates the application to which each stack content belongs, that is, the stack content generated when an exception problem occurs in which application. The theme information is used to indicate the large classification to which the exception problem belongs, and the call stack information is used to indicate the small classification to which the exception problem belongs. For example, according to the theme information, it can be determined that the large-grained classification to which the exception problem belongs is a null pointer exception, and according to the call stack information, it can be determined that the small-grained classification to which the exception problem belongs is a null pointer exception occurring in the 2nd line of code, or a null pointer exception occurring in the 109th line of code, or a null pointer exception occurring in the 182nd line of code. Here, they are not listed one by one. That is to say, the small-grained classification further locates the exception problem, making it easier to quickly handle the exception problem.

[0037] Step 103: Determine at least one target stack content belonging to the same application according to the application package name.

[0038] In the embodiments of the present disclosure, according to the application package names corresponding to the content of each stack, the application package names are compared to determine at least one target stack content belonging to the same application, and then the target stack contents under the same application are compared and identified.

[0039] For example, there are 6 stack contents, denoted as A - F. Among them, A, C, and D belong to Application 1, and B, E, and F belong to Application 2. Then, the target stack contents A, C, and D belonging to the same Application 1 are compared, and the target stack contents B, E, and F belonging to the same Application 2 are compared.

[0040] Step 104: Determine the category of the exception problem indicated by each target stack content and the number of exception problems under each category according to the theme information and call stack information corresponding to each target stack content.

[0041] In the embodiments of the present disclosure, for the target stack content belonging to any application program, since the theme information and the call stack information carry the key information corresponding to the stack content and play a major role in determining the category of the abnormal problem indicated by the stack content, therefore, by classifying according to the theme information and the call stack information corresponding to the stack content, the fine-grained category of the abnormal problem indicated by the stack content can be accurately determined. At the same time, during the process of determining the category, the quantity of each category is counted, which improves the accuracy of determining the abnormal problem category. Compared with the method of manually determining the abnormal problem category, it realizes automated processing, reduces costs, improves efficiency and accuracy. At the same time, the determined fine-grained category and quantity facilitate quickly locating serious abnormal problems in the subsequent process.

[0042] In one implementation manner of the embodiments of the present disclosure, the content of the theme information corresponding to the target stack content can be processed, including removing numbers, special symbols, and spaces. Furthermore, classification recognition is performed according to the processed theme information corresponding to the stack content, which further improves the accuracy and efficiency of classification recognition.

[0043] In the category determination method of the embodiments of the present disclosure, multiple abnormal stack contents are obtained, and each stack content indicates a corresponding abnormal problem. Each stack content is parsed to obtain the application program package name, theme information, and call stack information corresponding to each stack content. According to the application program package name, at least one target stack content belonging to the same application program is determined. According to the theme information and the call stack information corresponding to each target stack content, the category of the abnormal problem indicated by each target stack content and the quantity of abnormal problems in each category are determined. Based on various information obtained by parsing the stack content with abnormalities and based on the matching degree of the information, the abnormal problems of the same application program are classified and recognized, and the quantity is counted, which improves the accuracy and efficiency of category recognition of abnormal problems.

[0044] Based on the previous embodiment, the embodiments of the present disclosure provide another category determination method. Figure 2 It is a schematic flowchart of another category determination method provided by the embodiments of the present disclosure.

[0045] As Figure 2 shown, the method may include the following steps:

[0046] Step 201, obtain multiple abnormal stack contents.

[0047] Step 202, parse each stack content to obtain the application program package name, theme information, and call stack information corresponding to each stack content.

[0048] Step 203, according to the application program package name, determine at least one target stack content belonging to the same application program.

[0049] Specifically, reference may be made to the explanations in the foregoing method embodiments, and details will not be repeated in this embodiment.

[0050] Step 204: Determine multiple first sets according to the matching degrees between the topic information corresponding to the target stack contents.

[0051] In the embodiments of the present disclosure, for the target stack contents belonging to the same application program, one-by-one matching is performed according to the topic information corresponding to the target stack contents, and multiple first sets can be determined. Among them, a first set may include a first target stack content that does not match the topic information corresponding to any target stack content; or a first set includes multiple second target stack contents that match each other.

[0052] Among them, when performing matching based on topic information, matching can be performed according to distance, such as Euclidean distance, etc., to determine multiple first sets.

[0053] For example, there are 7 stack contents corresponding to Application A, numbered 1-7 respectively. After matching based on the topic information of the 7 stack contents, it is determined that the topic information of 1, 4, and 5 matches, the topic information of 3 and 7 matches, and the topic information of 2 does not match any of 1-7, and the topic information of 6 does not match any of 1-7. Then 4 first sets are determined, respectively marked as K1-K4. Among them, the first set K1 includes 1, 4, and 5, the first set K2 includes 3 and 7, the first set K3 includes 2, and the first set K4 includes 6.

[0054] Step 205: When any first set includes a first target stack content, determine the first category of the abnormal problem indicated by the first target stack content.

[0055] Among them, the first target stack content does not match the topic information corresponding to any target stack content.

[0056] For example, if the first set K3 includes a first target stack content 2, it is determined that the abnormal problem indicated by the target stack content 2 is of category M, and the number of abnormal problems corresponding to category M is 1. The first set K4 includes a first target stack content 6, and it is determined that the abnormal problem indicated by the target stack content 6 is of category N, and the number of abnormal problems corresponding to category N is 1.

[0057] Step 206: When any first set includes multiple second target stack contents, determine the category of the abnormal problem indicated by the second target stack contents and the number of abnormal problems in each category according to the call stack information corresponding to the multiple second target stack contents.

[0058] Among them, the topic information corresponding to the multiple second target stack contents matches each other.

[0059] In the embodiments of the present disclosure, when it is determined that there is a first set according to the fusion information corresponding to each target stack content, in order to improve the accuracy of category recognition and obtain a more fine-grained category, in the embodiments of the present disclosure, for multiple second target stack contents included in any first set, the corresponding call stack information is used to continue the matching to determine the category of the abnormal problem indicated by the second target stack content and the number of abnormal problems under each category.

[0060] In the category determination method of the embodiments of the present disclosure, during the matching process according to the theme information, the first category of the abnormal problem indicated by the first target stack content that does not match the fusion information corresponding to any target stack content. When it is determined that there is a first set including multiple second target stack contents with mutually matching theme information, in order to improve the accuracy and finer granularity of category recognition, in the embodiments of the present disclosure, the information of the call stack is used to continue the matching to improve the accuracy and finer granularity of category recognition, and the number of each category is counted to facilitate subsequent rapid positioning of serious abnormal problems.

[0061] Based on the above embodiments, the embodiments of the present disclosure provide another category determination method. Figure 3 For the flowchart of another category determination method provided by the embodiments of the present disclosure, as Figure 3 shown, this method includes the following steps:

[0062] Step 301, obtain multiple abnormal stack contents.

[0063] Step 302, parse each stack content to obtain the application package name, theme information, and call stack information corresponding to each stack content.

[0064] Step 303, determine at least one target stack content belonging to the same application according to the application package name.

[0065] Specifically, reference may be made to the explanation in the foregoing method embodiments, and details are not described herein again.

[0066] Step 304, fuse the theme information and keyword information corresponding to each target stack content to obtain the fusion information corresponding to each target stack content.

[0067] In the embodiments of the present disclosure, the keyword information is also used to indicate the large category of the abnormal problem corresponding to the target stack content.

[0068] In one implementation of the embodiments of the present disclosure, after processing the subject information and keyword information corresponding to each target stack content to remove useless information, the corresponding subject information and keyword information can be converted into corresponding vectors, and the vectors are concatenated to obtain fusion information. Among them, when concatenating, the order of the subject information and keyword information is not limited in this embodiment.

[0069] In another implementation of the embodiments of the present disclosure, after processing the subject information and keyword information corresponding to each target stack content to remove useless information, the corresponding subject information and keyword information can be converted into corresponding vectors, and the vectors are added to obtain fusion information. Without changing the dimension, the subject information and keyword information are retained to improve the matching efficiency while ensuring the matching accuracy when performing matching based on the fusion information subsequently.

[0070] Step 305: Determine multiple first sets according to the matching degree between the fusion information corresponding to each target stack content.

[0071] In the embodiments of the present disclosure, according to the obtained fusion information and based on the matching degree of the fusion information, multiple first sets are determined. Since the fusion information carries more key information, the accuracy of determining the first sets is improved.

[0072] Specifically, reference can be made to the method of determining the first set according to the matching degree between the subject information of the target stack content, which will not be elaborated in this embodiment.

[0073] Step 306: When any first set contains a first target stack content, determine the first category of the abnormal problem indicated by the first target stack content.

[0074] Among them, the subject information corresponding to the first target stack content does not match the subject information corresponding to any target stack content.

[0075] Among them, the relevant explanations in the foregoing method embodiments are also applicable to the steps of this embodiment, and the meta-interests are the same, which will not be elaborated in this embodiment.

[0076] In one implementation of the embodiments of the present disclosure, according to the fusion information corresponding to the first target stack content, determine the first category of the abnormal problem indicated by the corresponding first target stack content.

[0077] Step 307: For the same first set, determine multiple second sets according to the matching degree of the call stack information corresponding to multiple second target stack contents.

[0078] Among them, the call stack indicates the called function and the line number of the function, which can be used to indicate a finer-grained exception, that is, an exception that occurs in a specific line of code.

[0079] Among them, the second set may include a third target stack content that does not match the call stack information corresponding to any second target stack content; or the second set includes multiple fourth target stack contents that match each other.

[0080] Among them, when matching based on the call stack information, matching can be performed according to the distance, such as the Euclidean distance, etc., to determine multiple second sets.

[0081] For example, if the second target stack contents included in the first set K1 are 1, 4, and 5, then based on the matching degree of the call stack information corresponding to the second target stack contents, if the second target stack content 5 does not match the stack information of 1 and 4, it is determined that the second target stack content included in the second set L2 is 5. For the sake of distinction, 5 is called the third target stack content; if the stack information of the second target stack contents 1 and 4 matches, it is determined that the second target stack contents included in the second set L1 are 1 and 4. For the sake of distinction, 1 and 4 are called the fourth target stack contents.

[0082] Step 308, when any second set includes a third target stack content, determine the second category of the abnormal problem indicated by the third target stack content.

[0083] Among them, the third target stack content does not match the call stack information corresponding to any second target stack content.

[0084] In an implementation manner of the embodiments of the present disclosure, according to the fusion information and call stack information corresponding to the third target stack content, determine the second category of the abnormal problem indicated by the corresponding third target stack content. Among them, the granularity of the second category is finer than that of the first category. For example, the first category is a large-granularity category, that is, array out-of-bounds, and the second category is a fine-granularity category, that is, the 4th array out-of-bounds.

[0085] In the embodiments of the present disclosure, the fusion information corresponding to the third target stack content is the same as that corresponding to any second target stack content, but the call stack information is different. That is to say, the third target stack content and any second target stack content have the same large-granularity classification, but have different small-granularity classifications.

[0086] For example, the second target stack contents include 1, 4, and 5. Among them, 5 is the third target stack content. If the large-granularity classification determined according to the corresponding fusion information is array out-of-bounds, and after matching through the call stack information, it is determined that the small-granularity classification of the abnormal problem indicated by the third target stack content 5 is the 2nd array out-of-bounds, and 1 and 4 also indicate array out-of-bounds, but not the 2nd array out-of-bounds.

[0087] Step 309, when any second set includes multiple fourth target stack contents, determine the third category of the abnormal problem indicated by any fourth target stack content and the number of abnormal problems under the third category.

[0088] Among them, the call stack information corresponding to multiple fourth target stack contents matches each other.

[0089] In the embodiments of the present disclosure, the fusion information of multiple fourth target stack contents included in any second set matches, and the call stack information also matches. Therefore, the third category indicated by the fourth target stack content can be determined according to the fusion information and call stack information corresponding to any fourth target stack content, and the number of abnormal problems in the third category can be determined according to the number of fourth target stack contents included in the second set.

[0090] For example, the number of second sets is 2. The first second set contains 3 fourth target stack contents, namely b1, b2, and b4, and the second second set contains 4 fourth target stack contents, namely b3, b5, b6, and b7. Therefore, according to the fusion information and call stack information of the fourth target stack content b1, it is determined that the category corresponding to the 3 fourth target stack contents in the first second set is a null pointer exception at line 8 of the code, and the number of abnormal problems of the null pointer exception at line 8 of the code is 3; according to the fusion information and call stack information of the fourth target stack content b5, it is determined that the category corresponding to the 4 fourth target stack contents in the second second set is a null pointer exception at line 80 of the code, and the number of abnormal problems of the null pointer exception at line 80 of the code is 4.

[0091] In the embodiments of the present disclosure, matching is first performed according to the fusion information, and then matching is performed according to the information of the call stack. Those skilled in the art can also first match according to the information of the call stack and then perform matching according to the fusion information. The embodiments of the present disclosure do not make limitations.

[0092] In the method for determining categories in the embodiments of the present disclosure, during the process of matching according to the fusion information, the category of an abnormal problem that does not match the fusion information of any abnormal problem is determined as the first category. When it is determined that there is a first set according to the fusion information of each abnormal problem, in order to improve the accuracy of category recognition, in the embodiments of the present disclosure, the information of the call stack is used to continue the matching. When the fusion information matches but the information of the call stack does not match, the category of the corresponding abnormal problem is determined as the second category; when the fusion information matches and the information of the call stack also matches, the category of the corresponding abnormal problem is determined as the third category. By matching the fusion information and the information of the call stack, the fine granularity of category division is improved, the accuracy of category division is improved, and it is convenient for subsequent identification and positioning of important abnormal problems.

[0093] Based on the above embodiments, in one implementation manner of the embodiments of the present disclosure, after determining the category of the abnormal problem indicated by the target stack content and the number of abnormal problems in each category, it further includes:

[0094] Obtain the display dimensions for each category; the dimensions include one or more of a time period, an item corresponding to an application, and a version number, and display each category according to the display dimensions.

[0095] That is to say, in the above embodiment, after determining the first category, the second category, and the third category, as well as the quantities of the first category, the second category, and the third category for the exception problems indicated by the target stack content, display the first category, the second category, and the third category according to the display dimensions. As an implementation manner, it can be displayed in descending order according to the quantity of each category; as another implementation manner, the ranking can be determined according to the popularity or importance degree of each category, and displayed in descending order according to the ranking, and the quantity of exception problems under the category is displayed while the category is displayed, so as to quickly locate the more serious exception problems based on the display, or quickly pay attention to the categories with higher rankings.

[0096] To implement the above embodiment, the present disclosure also proposes a category determination device.

[0097] Figure 4 It is a schematic structural diagram of a category determination device provided by an embodiment of the present disclosure.

[0098] As Figure 4 shown, the device includes:

[0099] An obtaining module 41, configured to obtain stack contents of multiple exceptions; each of the stack contents indicates a corresponding exception problem.

[0100] An analyzing module 42, configured to analyze each of the stack contents to obtain the application package name, theme information, and call stack information corresponding to each of the stack contents.

[0101] A determining module 43, configured to determine at least one target stack content belonging to the same application according to the application package name.

[0102] The determining module 43 is further configured to determine the category of the exception problem indicated by each of the target stack contents and the quantity of exception problems under each category according to the theme information and call stack information corresponding to each of the target stack contents.

[0103] Further, in an implementation manner of the embodiment of the present disclosure, the determining module 43 is specifically configured to:

[0104] Determine multiple first sets according to the matching degree between the theme information corresponding to the target stack content; when any one of the first sets contains a first target stack content, determine the first category of the abnormal problem indicated by the first target stack content; the first target stack content does not match the theme information corresponding to any of the target stack contents; when any one of the first sets contains multiple second target stack contents, determine the category of the abnormal problem indicated by each second target stack content and the number of abnormal problems in each category according to the call stack information corresponding to the multiple second target stack contents; the theme information corresponding to the multiple second target stack contents matches each other.

[0105] Further, in an implementation manner of the embodiments of the present disclosure, the target stack content further has corresponding keyword information, and the determining module 43 is specifically further configured to:

[0106] Fuse the theme information and keyword information corresponding to each of the target stack contents to obtain the fusion information corresponding to each of the target stack contents; determine multiple first sets according to the matching degree between the fusion information corresponding to each of the target stack contents.

[0107] Further, in an implementation manner of the embodiments of the present disclosure, the determining module 43 is specifically further configured to:

[0108] For the same first set, determine multiple second sets according to the matching degree of the call stack information corresponding to the multiple second target stack contents; when any one of the second sets contains a third target stack content, determine the second category of the abnormal problem indicated by the third target stack content; the third target stack content does not match the call stack information corresponding to any of the second target stack contents; when any one of the second sets contains multiple fourth target stack contents, determine the third category of the abnormal problem indicated by any one of the fourth target stack contents and the number of abnormal problems in the third category; the call stack information corresponding to the multiple fourth target stack contents matches each other.

[0109] In an implementation manner of the embodiments of the present disclosure, the apparatus further includes:

[0110] A display module, configured to obtain the display dimensions of each category; the dimensions include one or more of a time period, an item corresponding to an application program, and a version number; display each category according to the display dimensions.

[0111] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and details are not described herein again.

[0112] In the category determination device according to the embodiments of the present disclosure, during the process of matching according to the fusion information, the category of the abnormal problem that does not match the fusion information of any abnormal problem is determined as the first category. When it is determined that there is a first set according to the fusion information of each abnormal problem, in order to improve the accuracy of category recognition, the information of the call stack is used to continue the matching in the embodiments of the present disclosure. When the fusion information matches but the information of the call stack does not match, the category of the corresponding abnormal problem is determined as the second category; when the fusion information matches and the information of the call stack also matches, the category of the corresponding abnormal problem is determined as the third category. By matching the fusion information and the information of the call stack, the granularity of category division is improved, the accuracy of category division is improved, and it is convenient to identify and locate important abnormal problems subsequently.

[0113] To implement the above embodiments, the embodiments of the present disclosure propose an electronic device, including:

[0114] At least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0115] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the foregoing method embodiments.

[0116] To implement the above embodiments, the embodiments of the present disclosure propose a non-transitory computer-readable storage medium storing computer instructions, and the computer instructions are used to cause the computer to execute the method described in the foregoing method embodiments.

[0117] To implement the above embodiments, the embodiments of the present disclosure propose a computer program product, including computer instructions, and the computer instructions implement the method described in the foregoing method embodiments when executed by a processor.

[0118] Figure 5 It is a structural block diagram of an electronic device provided by the embodiments of the present disclosure. Figure 5 The illustrated electronic device is only an example and should not bring any limitation to the functions and usage scope of the embodiments of the present disclosure.

[0119] Such as Figure 5As shown, the electronic device 10 includes a processor 11, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 12 or a program loaded from a memory 16 into a random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 are also stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0120] The following components are connected to the I / O interface 15: a memory 16 including a hard disk, etc.; and a communication section 17 including a network interface card such as a LAN (Local Area Network) card, a modem, etc., and the communication section 17 performs communication processing via a network such as the Internet; a drive 18 is also connected to the I / O interface 15 as needed.

[0121] In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 17. When the computer program is executed by the processor 11, the above functions defined in the method of the present disclosure are executed.

[0122] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory 16 including instructions, and the above instructions can be executed by the processor 11 of the electronic device 10 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0123] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0124] In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0125] Any process or method description shown in a flowchart or described in other ways herein can be understood as representing a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present disclosure pertain.

[0126] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0127] It should be understood that various parts of the present disclosure can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0128] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0129] In addition, each functional unit in various embodiments of the present disclosure may be integrated into a processing module, may exist separately as individual physical units, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0130] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for category determination, characterized in that, it includes the following steps: Obtain the stack contents of multiple exceptions; wherein, each of the stack contents indicates a corresponding exception problem; Parse each of the stack contents to obtain the application package name, theme information, and call stack information corresponding to each of the stack contents, wherein the theme information is used to indicate the major category to which the exception problem belongs; Determine at least one target stack content belonging to the same application according to the application package name; Determine multiple first sets according to the matching degree between the theme information corresponding to each of the target stack contents; When any one of the first sets contains a first target stack content, determine the first category of the exception problem indicated by the first target stack content; the theme information corresponding to the first target stack content does not match that of any of the target stack contents; When any one of the first sets contains multiple second target stack contents, determine the category of the exception problem indicated by each second target stack content and the number of exception problems in each category according to the call stack information corresponding to the multiple second target stack contents; the theme information corresponding to the multiple second target stack contents matches each other.

2. The method according to claim 1, characterized in that, the target stack content also has corresponding keyword information, and the determining multiple first sets according to the matching degree between the theme information corresponding to each of the target stack contents includes: Fuse the theme information and keyword information corresponding to each of the target stack contents to obtain the fusion information corresponding to each of the target stack contents; Determine multiple first sets according to the matching degree between the fusion information corresponding to each of the target stack contents.

3. The method according to claim 1, characterized in that, when any one of the first sets contains multiple second target stack contents, the determining the category of the exception problem indicated by each second target stack content and the number of exception problems in each category according to the call stack information corresponding to the multiple second target stack contents includes: For the same first set, determine multiple second sets according to the matching degree of the call stack information corresponding to the multiple second target stack contents; When any one of the second sets contains a third target stack content, determine the second category of the exception problem indicated by the third target stack content; the call stack information corresponding to the third target stack content does not match that of any of the second target stack contents; When any one of the second sets contains multiple fourth target stack contents, determine the third category of the exception problem indicated by any one of the fourth target stack contents and the number of exception problems in the third category; the call stack information corresponding to the multiple fourth target stack contents matches each other.

4. The method according to any one of claims 1-3, characterized in that, after determining the category of the exception problem indicated by the target stack content and the number of exception problems in each category, it includes: Obtain the display dimensions of each category; the dimensions include one or more of a time period, a project corresponding to the application, and a version number; Display each category according to the display dimensions.

5. A category determination device, characterized in that, Comprising: An acquisition module, configured to acquire stack contents of multiple exceptions; wherein, each of the stack contents indicates a corresponding exception problem; A parsing module, configured to parse each of the stack contents to obtain the application package name, theme information, and call stack information corresponding to each of the stack contents, wherein the theme information is used to indicate the major classification to which the exception problem belongs; A determination module, configured to determine at least one target stack content belonging to the same application according to the application package name; The determination module is further configured to determine the category of the exception problem indicated by each of the target stack contents and the number of exception problems in each category according to the theme information and call stack information corresponding to each of the target stack contents; Specifically, the determination module is configured to: Determine multiple first sets according to the matching degree between the theme information corresponding to the target stack contents; When any one of the first sets includes a first target stack content, determine the first category of the exception problem indicated by the first target stack content; the first target stack content does not match the theme information corresponding to any of the target stack contents; When any one of the first sets includes multiple second target stack contents, determine the category of the exception problem indicated by each of the second target stack contents and the number of exception problems in each category according to the call stack information corresponding to the multiple second target stack contents; the theme information corresponding to the multiple second target stack contents matches each other.

6. The apparatus according to claim 5, wherein, The target stack content further has corresponding keyword information, and specifically, the determination module is further configured to: Fuse the theme information and keyword information corresponding to each of the target stack contents to obtain the fused information corresponding to each of the target stack contents; Determine multiple first sets according to the matching degree between the fused information corresponding to each of the target stack contents.

7. The apparatus according to claim 5, wherein, Specifically, the determination module is further configured to: For the same first set, determine multiple second sets according to the matching degree of the call stack information corresponding to the multiple second target stack contents; When any one of the second sets includes a third target stack content, determine the second category of the exception problem indicated by the third target stack content; the third target stack content does not match the call stack information corresponding to any of the second target stack contents; When any one of the second sets includes multiple fourth target stack contents, determine the third category of the exception problem indicated by any one of the fourth target stack contents and the number of exception problems in the third category; the call stack information corresponding to the multiple fourth target stack contents matches each other.

8. The apparatus according to any one of claims 5-7, wherein, The apparatus further includes: A display module, configured to acquire the display dimensions of each category; the dimensions include one or more of a time period, an item corresponding to an application program, and a version number; and display each category according to the display dimensions.

9. An electronic device, wherein, Comprising: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-4.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein, the computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

11. A computer program product comprising computer instructions, wherein, the computer instructions, when executed by a processor, implement the method according to any one of claims 1-4.

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