CN110795266A - Method and device for reporting software exception, electronic equipment and storage medium - Google Patents
Method and device for reporting software exception, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a method, a device, electronic equipment and a storage medium for reporting software abnormity, relates to the technical field of computers, and is used for at least solving the problem that the software operation abnormity can not be reproduced in the related technology, so that the software operation abnormity can not be accurately positioned and solved, wherein the method comprises the following steps: acquiring operation track information aiming at specified application software, wherein the operation track information is used for indicating an operation behavior set consisting of at least two operation behaviors according to a certain time sequence; determining whether the operation track corresponding to the operation track information is an abnormal operation track or not through a pre-trained track judgment model, wherein the track judgment model is obtained by training an operation track training sample corresponding to a target account, and the operation track training sample is subjected to marking of an abnormal operation track or a non-abnormal operation track; and if the operation track is determined to be an abnormal operation track, reporting the operation track information to a server corresponding to the specified application software.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for reporting a software exception, an electronic device, and a storage medium.
Background
Software is a computer program, procedure, rule, and possibly file, document, and data related to the operation of a computer system, and it is often abnormal in the operation of the software due to various reasons, for example, the operation of the software is stopped due to malicious operations of a user. At present, software operation and maintenance personnel mainly rely on a user to actively feed back after the software is abnormally operated, then the operation and maintenance personnel can obtain data of the software which is abnormally operated according to the feedback, and the operation steps of the software when the software is abnormally operated are reproduced through the obtained data, so that the reason of the abnormal operation of the software is determined, and the software is conveniently maintained and adjusted. However, many users are not willing to feedback after finding the software operation abnormality, the feedback of individual users is not enough to draw attention, and the available data is too small to reproduce the steps of the software operation when the software operation is abnormal, so that the cause of the software operation abnormality cannot be accurately located, and the abnormal condition of the software operation cannot be solved naturally.
Disclosure of Invention
The present disclosure provides a method, an apparatus, an electronic device and a storage medium for reporting software exception, which are used to at least solve the problem that in the related art, when reporting software exception by a user, the software exception cannot be reproduced due to fewer reported users or too few available data, and the software exception cannot be accurately located and solved. The technical scheme of the disclosure is as follows:
according to a first aspect of the embodiments of the present disclosure, a method for reporting a software exception is provided, including:
acquiring operation track information aiming at specified application software, wherein the operation track information is used for indicating an operation behavior set consisting of at least two operation behaviors according to a certain time sequence;
determining whether the operation track corresponding to the operation track information is an abnormal operation track or not through a pre-trained track judgment model, wherein the track judgment model is obtained by training an operation track training sample corresponding to a target account, and the operation track training sample is subjected to marking of the abnormal operation track or the non-abnormal operation track;
and if the operation track is determined to be an abnormal operation track, reporting the operation track information to a server of the application software.
In one possible design, before obtaining a determination result of an operation trajectory corresponding to the operation trajectory information based on a trajectory determination model trained in advance according to the operation trajectory information, the method further includes:
comparing the operation track with a preset abnormal operation track to determine whether the operation track is an abnormal operation track;
if the operation track is determined to be an abnormal operation track, directly reporting operation track information corresponding to the abnormal operation track to a server corresponding to the specified application software;
and if the operation track is a non-abnormal operation track, inputting the non-abnormal operation track into the track judgment model.
In one possible design, the trajectory estimation model training process includes:
obtaining an operation track training sample set, wherein the operation track training sample set is a historical operation track set of the target user for the application software, and each operation track training sample in the operation track training sample set is labeled with an abnormal operation track or a non-abnormal operation track;
and training the initial trajectory judgment model for multiple times through the operation trajectory training sample set until the judgment result of the trained trajectory judgment model is consistent with the label of each operation trajectory training sample, and taking the trajectory judgment model as the trajectory judgment model of the target account.
In one possible design, each training process of the multiple training of the initial trajectory determination model by the operation trajectory training sample set is as follows:
aiming at each operation track training sample in the operation track training sample set, obtaining an operation track characteristic vector of each operation track training sample;
inputting the operation track characteristic vector of each operation track training sample into a track judgment model used in the current training to obtain a judgment result of each operation track training sample, and comparing the judgment result with the label of each operation track training sample to obtain a comparison result corresponding to each operation track training sample;
and adjusting the track judgment model used in the training according to the comparison result.
In one possible design, the method further includes:
and updating the preset abnormal operation track after the specified application software is determined to be updated.
In one possible design, the method further includes:
acquiring an actual operation result of the specified application software after the operation track is executed;
and if the actual operation result is not consistent with the operation result of the appointed application software corresponding to the judgment result obtained according to the judgment model, marking the operation track according to the actual operation result to obtain a new operation track training sample, wherein the operation track training sample is used for retraining the track judgment model.
According to a second aspect of the embodiments of the present disclosure, an apparatus for reporting a software exception is provided, including:
an acquisition unit configured to perform acquisition of operation trajectory information for a specific application software, wherein the operation trajectory information is used for indicating an operation behavior set composed of at least two operation behaviors in a time sequence;
the first track judging unit is configured to execute a track judging model trained in advance to determine whether an operation track corresponding to the operation track information is an abnormal operation track, wherein the track judging model is obtained by training an operation track training sample corresponding to a target account, and the operation track training sample is subjected to marking of an abnormal operation track or a non-abnormal operation track;
and the exception reporting unit is configured to report the operation track information to the server corresponding to the specified application software when the operation track is determined to be an exception operation track.
In one possible design, the apparatus further includes: a second trajectory determination unit configured to perform:
comparing the operation track with a preset abnormal operation track to determine whether the operation track is an abnormal operation track;
if the operation track is determined to be an abnormal operation track, directly reporting operation track information corresponding to the abnormal operation track to a server corresponding to the specified application software;
and if the operation track is a non-abnormal operation track, inputting the non-abnormal operation track into the track judgment model.
In one possible design, the trajectory estimation model training is obtained by training a model training unit configured to perform:
obtaining an operation track training sample set, wherein the operation track training sample set is a historical operation track set of the target user for the application software, and each operation track training sample in the operation track training sample set is labeled with an abnormal operation track or a non-abnormal operation track;
and training the initial trajectory judgment model for multiple times through the operation trajectory training sample set until the judgment result of the trained trajectory judgment model is consistent with the label of each operation trajectory training sample, and taking the trajectory judgment model as the trajectory judgment model of the target account.
In one possible design, the model training unit is specifically configured to perform:
aiming at each operation track training sample in the operation track training sample set, obtaining an operation track characteristic vector of each operation track training sample;
inputting the operation track characteristic vector of each operation track training sample into a track judgment model used in the current training to obtain a judgment result of each operation track training sample, and comparing the judgment result with the label of each operation track training sample to obtain a comparison result corresponding to each operation track training sample;
and adjusting the track judgment model used in the training according to the comparison result.
In one possible design, the apparatus further includes an updating unit configured to perform:
and updating the preset abnormal operation track after the specified application software is determined to be updated.
In one possible design, the apparatus further includes a sample labeling unit configured to perform:
acquiring an actual operation result of the specified application software after the operation track is executed;
and if the actual operation result is not consistent with the operation result of the appointed application software corresponding to the judgment result obtained according to the judgment model, marking the operation track according to the actual operation result to obtain a new operation track training sample, wherein the operation track training sample is used for retraining the track judgment model.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the first aspect of the embodiments of the present disclosure described above and any method to which the first aspect relates.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a storage medium, wherein instructions that, when executed by a processor of an electronic device, enable the electronic device to perform the first aspect of the embodiments of the present disclosure and any of the methods that the first aspect relates to may relate to.
According to a fifth aspect of the embodiments of the present disclosure, there is provided a computer program product, which, when run on an electronic device, causes the electronic device to perform a method for implementing the first aspect of the embodiments of the present disclosure and any possible method related to the first aspect.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
in the embodiment of the disclosure, when it is detected that a target account uses a specific application software, operation track information for the specific application software is obtained, where the operation track information is used to indicate an operation behavior set composed of two operation behaviors according to a certain time sequence, and then a pre-trained track judgment model may be input into an operation track corresponding to the obtained operation track information to judge whether the operation track is an abnormal operation track or an abnormal operation track, and if the operation track is determined to be the abnormal operation track, the obtained operation track information is reported to a server corresponding to the specific application software, so that it is not necessary to confirm feedback (i.e. report) manually by a user to specify an abnormal operation condition of the application software, and it is not necessary to obtain data when the application software sends the abnormal condition according to the feedback to reproduce the abnormal condition, so that it is possible to find the occurrence of the abnormal condition of the application software and to reproduce the abnormal condition directly according to the reported operation track information in time And (4) positioning the reasons causing the abnormal problems so as to facilitate software operation and maintenance personnel to solve the abnormal problems in time.
And when the abnormal condition is reported, the operation track information of the user can be directly reported to the background service of the application software, so that the background server of the application software is prevented from acquiring data according to the reported abnormal condition, the problem of data loss is avoided, the running condition of the application software when the abnormal condition occurs can be timely and accurately reproduced according to the reported operation track information, a scheme for solving the abnormal condition can be timely found, and the solution efficiency of the abnormal condition is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure.
FIG. 1 illustrates an application scenario diagram in accordance with an exemplary embodiment;
fig. 2 is a flowchart illustrating a method for reporting a software exception according to an exemplary embodiment;
FIG. 3 illustrates a flowchart of each trajectory determination model training process, according to an exemplary embodiment;
fig. 4a is a schematic structural diagram illustrating an apparatus for reporting a software exception according to an exemplary embodiment;
fig. 4b is another schematic structural diagram of an apparatus for reporting a software exception according to an exemplary embodiment;
FIG. 5 illustrates a schematic structural diagram of an electronic device in accordance with an exemplary embodiment;
fig. 6 is another schematic diagram of an electronic device shown in accordance with an example embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the technical solutions of the present disclosure. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments described in the present disclosure without any creative effort belong to the protection scope of the technical solution of the present disclosure. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
The terms "first" and "second" in the description and claims of the present disclosure and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the term "comprises" and any variations thereof, which are intended to cover non-exclusive protection. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. The "plurality" in the present disclosure may mean at least two, for example, two, three or more, and the embodiments of the present disclosure are not limited.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this document generally indicates that the preceding and following related objects are in an "or" relationship unless otherwise specified.
As described above, in the prior art, by reporting the abnormality of the software by the user, the abnormal situation is reproduced, and the problem that causes the software abnormality is solved, the reason that the problem of the software cannot be determined is often caused by the fact that the user does not want to report or the reproduction probability of the reported abnormal problem is small, so that the abnormal situation of the software cannot be solved in time. In addition, in the specific process of analyzing the abnormal problem, the characteristics of each user are not concerned, and a uniform judgment standard is adopted for the operation modes of different users, however, because the operation modes of each user for the software are different, some operation modes are judged to be abnormal operation modes without operating the software abnormally, and therefore misjudgment is caused.
In view of this, the present disclosure provides a scheme for reporting software exception, in the scheme, when it is detected that a target account uses application software, operation track information of the target account (i.e., a user) for specified software is actively acquired, and according to the operation track information, an operation track corresponding to the operation track information is determined to be a non-exception operation track or an exception operation track based on a track determination model trained in advance and corresponding to the target account, and then when it is determined that the operation track is an exception operation track, the operation track information is reported to a background server of the application software. Therefore, when the operation track corresponding to the operation track information is determined to be an abnormal operation track, the abnormal operation track can be directly reported to the background service of the application software, so that the phenomenon that the abnormal condition reappears by acquiring the data when the application software sends the abnormal condition after the user feedback (namely reporting) is relied on is avoided, the occurrence of the abnormal condition of the application software can be timely found, the reason for causing the abnormal problem can be timely positioned, and the abnormal problem can be timely solved by software operation and maintenance personnel.
And when the abnormal condition is reported, the operation track information of the user can be directly reported to the background service of the application software, so that the background server of the application software is prevented from acquiring data according to the reported abnormal condition, the problem of data loss is avoided, the running condition of the application software when the abnormal condition occurs can be timely and accurately reproduced according to the reported operation track information, a scheme for solving the abnormal condition can be timely found, and the solution efficiency of the abnormal condition is improved.
Furthermore, the track judgment model corresponding to the target account is adopted for judging different target accounts, the operation habits of the different target accounts on the application software are fully considered, and the normal operation behavior is prevented from being determined as the abnormal operation behavior by mistake, so that the accuracy of the reason causing the abnormal condition is improved.
Some simple descriptions are given below to application scenarios to which the technical solution of the embodiment of the present disclosure can be applied, and it should be noted that the application scenarios described below are only used for describing the embodiment of the present disclosure and are not limited. In specific implementation, the technical scheme provided by the embodiment of the disclosure can be flexibly applied according to actual needs.
Referring to fig. 1, an application scenario is shown, where the application scenario includes an electronic device 101 and a server 102, the electronic device may be a terminal device such as a smart phone, a tablet computer, a computer, and the electronic device in fig. 1 is illustrated by taking a smart phone as an example; the server 102 may be one server, or may be a server cluster or a cloud computing center formed by a plurality of servers; the electronic device 101 and the server 102 may be connected via a network.
The electronic device 101 is provided with application software, and when a user uses the application software, the electronic device 101 may record operation track information formed by the user for an operation behavior of the application software, determine whether an operation track corresponding to the operation track information is an abnormal operation track, and report the operation track information to the server 102 when the operation track corresponding to the operation track information is determined to be the abnormal operation track.
Another possible application scenario includes an electronic device, a first server, and a second server, where the first server may be a server for analyzing whether an operation trajectory corresponding to operation trajectory information of a user for a certain specific application software recorded by the electronic device is an abnormal operation trajectory, the second server may be a background server of a certain application software in the electronic device, and when the first server determines that the operation trajectory is the abnormal operation trajectory, the operation trajectory information is reported to the second server.
To further illustrate the technical solutions provided by the embodiments of the present disclosure, the following detailed description is made with reference to the accompanying drawings and the specific embodiments. Although the disclosed embodiments provide method steps as shown in the following embodiments or figures, more or fewer steps may be included in the methods based on conventional or non-inventive efforts. In steps where no necessary causal relationship exists logically, the order of execution of the steps is not limited to that provided by the disclosed embodiments. The method can be executed in sequence or in parallel according to the method shown in the embodiment or the figure when the method is executed in an actual processing procedure or a device.
Fig. 2 is a flowchart illustrating a method for reporting a software exception according to an exemplary embodiment, where the method illustrated in fig. 2 may be executed by the electronic device, and the electronic device is installed with at least one application software, for example, the method for reporting a software exception may be applied to the electronic device illustrated in fig. 1.
The method shown in fig. 2 comprises the following steps.
In step 201, operation track information for a specific application software is obtained, wherein the operation track information is used for indicating an operation behavior set composed of at least two operation behaviors according to a certain time sequence.
In the embodiment of the present disclosure, the specified application software may be any application software installed in the electronic device shown in fig. 1. The operation behavior may refer to operations such as touch, key pressing, and the like of a user for a certain function in the software user interface, which are detected by the device when the user uses one application software. An operation trajectory may refer to a set of operation behaviors that are formed by two or more operation behaviors in a chronological order.
For example, when the WeChat is used, the WeChat icon is clicked to enter the operation behavior A of the WeChat application, then the address book is clicked to perform the operation behavior B of the address book interface, then the operation behavior C with a small and clear contact person is selected, and the three operation behaviors are combined together according to the time sequence to be regarded as an operation track.
In the embodiment of the disclosure, in the process of using the application software, some application software may be used after an account is registered, and some application software may temporarily log in with a device number for installing the electronic device, that is, a user may correspond to one account when using the application software, and therefore, an operation track of a target user for the application software may also be regarded as an operation track of a target account corresponding to the target user for the application software.
In step 202, the operation trajectory is compared with a preset abnormal operation trajectory to determine whether the operation trajectory is an abnormal operation trajectory.
In the embodiment of the present disclosure, the abnormal operation trajectory may be an operation trajectory that may cause an abnormal operation condition of the application software. Specifically, the operation tracks that most users may perform for a certain application software and that may lead to the application software running abnormally may be preset as abnormal operation tracks, that is, which operation tracks are abnormal operation tracks that may cause the application software running abnormally may be determined first, and then when the application software is downloaded and installed in the electronic device, the determined abnormal operation tracks may be downloaded to the electronic device together, or the determined abnormal operation tracks may be set in an installation package of the application software. Therefore, when the operation track information of the target user (namely the target account) for the specified application software is acquired, the operation track corresponding to the operation track information can be compared with the determined abnormal operation track, so that whether the operation track is the abnormal operation track or not can be determined simply and rapidly, and the speed of reporting the abnormal operation track is increased.
Furthermore, because the application software can continuously update the upgrade version along with the time and the user requirements, and the operation modes of different versions may have certain difference, after the version of the application software is updated, the preset abnormal operation track can be updated according to the operation mode of the updated version, so that the abnormal judgment of the operation track is more accurate.
In step 203, if the operation track is determined to be a non-abnormal operation track, inputting the non-abnormal operation track into a pre-trained track judgment model; if the operation track is determined to be an abnormal operation track, step 205 is executed.
In the embodiment of the present disclosure, if the determined operation trajectory is a non-abnormal operation trajectory, that is, the operation trajectory is not included in the preset abnormal operation trajectory, and it cannot be directly determined that the operation trajectory is an abnormal operation trajectory, the operation trajectory may be input into a trajectory determination model trained in advance, and a re-determination may be performed to determine whether the operation trajectory is an abnormal operation trajectory or a non-abnormal operation trajectory. Therefore, whether the operation track is an abnormal operation track or not can be determined more accurately by judging the acquired operation track twice, so that operation track information corresponding to the abnormal operation track is reported, and a server can have enough data to analyze the abnormal operation of the application software, so that the reason of the abnormal operation of the application software caused by rapid positioning can be rapidly solved.
In step 204, it is determined whether the operation trajectory corresponding to the operation trajectory information is an abnormal operation trajectory through a pre-trained trajectory determination model, where the trajectory determination model is obtained by training an operation trajectory training sample corresponding to the target account, and the operation trajectory training sample is labeled with an abnormal operation trajectory or a non-abnormal operation trajectory.
In the embodiment of the application, after the operation track information of a user for a certain application software is obtained, the operation track information can be input into a pre-trained track judgment model, and the track judgment model is used for judging whether the operation track corresponding to the operation track information is an abnormal operation track, so that the judgment process is simplified, and the operation track abnormity judgment efficiency is improved.
The trajectory determination model in the embodiment of the present application may be trained in the following manner:
the method comprises the following steps of firstly, obtaining an operation track training sample set, wherein the operation track training sample set is a historical operation track set of a target user for specified application software, and each operation track training sample in the operation track training sample set is marked with an abnormal operation track or a non-abnormal operation track.
Specifically, when the model is judged in the training trajectory, when a user uses a certain application software on a stable application software version, for the records of the historical operation trajectories of each function in the application software, each operation trajectory in the records of the historical operation trajectories corresponds to an operation trajectory training sample, and each historical operation trajectory sample is labeled with an abnormal operation trajectory or a non-abnormal operation trajectory in a manual labeling manner or a system automatic labeling manner.
And secondly, training the initial track judgment model for multiple times through the operation track training sample set until the judgment result of the track judgment model obtained through training is consistent with the label of each operation track training sample, and taking the track judgment model as the track judgment model of the target account.
As shown in fig. 3, each training process in the multiple training of the initial trajectory determination model by using the obtained operation trajectory training sample set includes the following steps:
step 301: and aiming at each operation track training sample in the operation track training sample set, obtaining the operation track characteristic vector of each operation track training sample.
In this embodiment of the application, for each operation trajectory training sample, operation behavior data of a target account corresponding to each operation trajectory training sample may be obtained first, where the operation behavior data includes operation type data, operation frequency data, and operation object data, and specifically, the operation type data includes click, slide, touch, press, and the like. The operation frequency data is the speed of touch control or key operation performed on the application software by a user when the user uses the application software, the operation frequency is higher when some users use certain application software, and the operation frequency is lower for some users. The operation object data is an object targeted by user operation, such as a pause function in a video player or a function of playing a next video; for another example, the functions of address book, information recommendation, payment, scanning, etc. in the chat interface can be regarded as the operation objects. Since the operating habits of different users on the application software are different, the obtained operating behavior data may also be different, that is, the operating behavior data of different users on the same application software is specific to each user.
Furthermore, after the operation behavior data of the target user (i.e. the target account) is obtained, feature extraction may be performed on the data, so as to extract a feature vector capable of characterizing an operation trajectory of the target user for a certain application software according to the operation behavior data of the target user.
Step 302: and inputting the operation track characteristic vector of each operation track training sample into a track judgment model used in the training to obtain a judgment result of each operation track training sample, and comparing the judgment result with the label of each operation track training sample to obtain a comparison result corresponding to each operation track training sample.
Further, after obtaining the operation trajectory feature vectors of each operation trajectory training sample, the operation trajectory feature vectors may be input into the trajectory determination model used in the current training to train the trajectory determination model used in the current training, so as to obtain a determination result for each operation trajectory training sample, that is, a determination result whether each operation trajectory is an abnormal operation trajectory or a non-abnormal operation trajectory, and further compare the obtained determination result with the label of each operation trajectory, so as to obtain a comparison result.
Step 303: and adjusting the track judgment model used in the training according to the comparison result.
In the embodiment of the application, if the judgment result of each operation track training book is consistent with the label of each operation track training sample, it can be determined that the track judgment model used in the training can be used as the track judgment model of the target account, that is, the track judgment model can be used to judge whether the obtained operation track of the user on a certain application software is an abnormal operation track; if the judgment result of each operation track training book is inconsistent with the label of each operation track training sample, the track judgment model used in the training needs to be adjusted so as to be used in the next training.
For example, if the determination result of the a operation trajectory training sample is a non-abnormal operation trajectory after the a operation trajectory training sample is input into the trajectory determination model used this time, but in reality, the label of the a operation trajectory training sample is an abnormal operation determination trajectory, it may be determined that the determination result obtained by the trajectory determination model used this time is not consistent with the actual label, and the determination result of the trajectory determination model used this time is inaccurate and needs to be adjusted.
Because the operation track training sample used by the training track judgment model is the historical operation track according to the target user (namely the target account), the trained track judgment model accords with the operation habit characteristics of the target user, and the track judgment models corresponding to the target users are different, so the operation abnormal track belonging to each user can be considered through the track judgment model, the situation that some non-abnormal operation tracks are determined as abnormal operation tracks to be reported due to different operation habits of the users is avoided, and the situation of error reporting is effectively reduced.
In the specific practical process, as described above, the version of the application software is updated with time and user requirements during the use process, and then the operation behavior of the user that is used to the application software may also change with the lapse of time or different versions, and if the obtained operation trajectory of the user is still determined by the old trajectory determination model after the version of the application software is updated, whether the obtained operation trajectory is an abnormal operation trajectory or a non-abnormal operation trajectory may be determined, which may result in an inaccurate finally obtained determination result, and a case of erroneous determination may occur, and further a case of missing report or erroneous report may occur.
Therefore, after the version of the application software is updated or after a certain period of time, the actual operation result of the application software corresponding to the operation track can be compared with the operation result of the application software corresponding to the judgment result of the operation track obtained according to the judgment model, if the actual operation result of the application software corresponding to the operation track does not match the operation result of the application software corresponding to the operation track, the operation track can be re-marked to obtain a new operation track training sample, and then the track judgment model can be re-trained according to the new operation track training sample, so that the judgment result of whether the operation track is abnormal (namely normal) or abnormal obtained according to the track judgment model is more accurate, and the situation of missing and reporting the abnormal operation track can be avoided.
In step 205, the operation track information corresponding to the abnormal operation track is reported to the server corresponding to the specified application software.
In the embodiment of the disclosure, after the operation track corresponding to the obtained operation track information is compared with the preset abnormal operation track, it is determined that the operation track is the abnormal operation track, and the operation track information corresponding to the abnormal operation track can be directly reported to the server of the application software, so that the speed of reporting the abnormal operation track can be increased, and the server can timely determine the reason causing the abnormal operation of the software according to the reported information, so that the operation and maintenance personnel of the application software can timely solve the problem, and the use experience of a user is improved while the problem of the abnormal software is solved.
Or, when the operation track is determined to be a non-abnormal operation track, the acquired operation track is input into the track judgment model for judgment again, if the obtained judgment result indicates that the operation track is an abnormal operation track, the operation track information corresponding to the operation track can be reported to a background server of the application software, therefore, the operation track information corresponding to the abnormal operation track can be reported to the background server of the application software as much as possible, so that the background server can analyze the running state of the application software according to the acquired operation track information, therefore, when the application software is abnormal in operation, the reason for the abnormal operation of the application software can be timely positioned according to the reported operation track information, and the abnormal operation state does not need to be reproduced by acquiring data, so that the problem of the abnormal operation of the application software can be rapidly and accurately solved.
In the embodiment of the disclosure, by the above method, the obtained operation track of the target user for the application software is judged, whether the operation track is an abnormal operation track or a non-abnormal operation track is determined, when the operation track is an abnormal operation track, the operation track information corresponding to the abnormal operation track is reported to a background server of the application software without manually selecting whether to report the abnormal operation track by a user, therefore, the efficiency of reporting the abnormal condition of the application software is improved, and simultaneously, the abnormal condition that the application software reports after being received by a background server can be avoided, then acquiring data to reproduce the abnormal problem to locate the reason of the abnormal operation problem of the application software, and then directly according to the reported abnormal operation track information, the reason for the abnormal operation of the application software is positioned, the speed of the abnormal problem caused by positioning is improved, and the use experience of a user is improved.
Fig. 4a is a block diagram of a device for reporting a software exception according to an exemplary embodiment, and referring to fig. 4a, the device for reporting a software exception includes an obtaining unit 401, a first track determining unit 402, and an exception reporting unit 403, where:
an obtaining unit 401 configured to perform obtaining operation trace information for a specific application software, wherein the operation trace information is used for indicating an operation behavior set composed of at least two operation behaviors in a certain time sequence;
a first trajectory determination unit 402 configured to execute a trajectory determination model trained in advance to determine whether an operation trajectory corresponding to the operation trajectory information is an abnormal operation trajectory, where the trajectory determination model is obtained by training an operation trajectory training sample corresponding to a target account, and the operation trajectory training sample is labeled with an abnormal operation trajectory or a non-abnormal operation trajectory;
an exception reporting unit 403, configured to report the operation track information to the server corresponding to the specified application software when the operation track is determined to be the exception operation track.
In a possible design, as shown in fig. 4b, the apparatus for reporting a software exception further includes: a second trajectory determination unit 404, the second trajectory determination unit 404 configured to perform: comparing the operation track with a preset abnormal operation track to determine whether the operation track is the abnormal operation track; if the operation track is determined to be an abnormal operation track, directly reporting operation track information corresponding to the abnormal operation track to a server corresponding to the specified application software; and if the operation track is a non-abnormal operation track, inputting the non-abnormal operation track into a track judgment model.
In one possible design, the trajectory determination model shown in fig. 4b is trained by a model training unit 405, and the model training unit 405 is configured to perform obtaining of an operation trajectory training sample set, where the operation trajectory training sample set is a historical operation trajectory set of a target user for application software, and each operation trajectory training sample in the operation trajectory training sample set is labeled with an abnormal operation trajectory or a non-abnormal operation trajectory; and training the initial trajectory judgment model for multiple times through the operation trajectory training sample set until the judgment result of the trajectory judgment model obtained by training is consistent with the label of each operation trajectory training sample, and taking the trajectory judgment model as the trajectory judgment model of the target account.
In one possible design, the model training unit 405 as shown in fig. 4b is specifically configured to perform training samples for each operation trajectory in the operation trajectory training sample set, obtaining an operation trajectory feature vector of each operation trajectory training sample; inputting the operation track characteristic vector of each operation track training sample into a track judgment model used in the training to obtain a judgment result of each operation track training sample, and comparing the judgment result with the label of each operation track training sample to obtain a comparison result corresponding to each operation track training sample; and adjusting the track judgment model used in the training according to the obtained comparison result.
In a possible design, as shown in fig. 4b, the apparatus for reporting a software exception further includes an updating unit 406, where the updating unit 406 is configured to update the preset exception operation track after determining that the version of the specified application software is updated.
In a possible design, as shown in fig. 4b, the apparatus for reporting a software exception further includes a sample labeling unit 407, where the sample labeling unit 407 is configured to execute an actual operation result of the specified application software after acquiring the execution operation trajectory; and if the actual operation result is not consistent with the operation result of the specified application software corresponding to the judgment result obtained according to the judgment model, marking the operation track according to the actual operation result to obtain a new operation track training sample, wherein the operation track training sample is used for retraining the track judgment model.
With regard to the device for reporting a software exception in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be described in detail here.
The division of the modules in the embodiments of the present disclosure is illustrative, and is only a logical function division, and there may be another division manner in actual implementation, and in addition, each functional module in each embodiment of the present disclosure may be integrated in one processor, may also exist alone physically, or may also be integrated in one module by two or more modules. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
Fig. 5 is a schematic structural diagram of an electronic device, such as the electronic device in fig. 1, according to an exemplary embodiment. As shown in fig. 5, an electronic device in the embodiment of the present disclosure includes at least one processor 501, and a memory 502 and a communication interface 503 connected to the at least one processor 501, a specific connection medium between the processor 501 and the memory 502 is not limited in the embodiment of the present disclosure, in fig. 5, a connection between the processor 501 and the memory 502 is taken as an example, the bus 500 is represented by a thick line in fig. 5, and a connection manner between other components is merely schematically illustrated and is not taken as a limitation. The bus 500 may be divided into an address bus, a data bus, a control bus, etc., and is shown with only one thick line in fig. 5 for ease of illustration, but does not represent only one bus or one type of bus.
In the embodiment of the present disclosure, the memory 502 stores instructions that can be executed by the at least one processor 501, and the at least one processor 501 can execute the steps included in the foregoing method for reporting a software exception by executing the instructions stored in the memory 502.
The processor 501 is a control center of the electronic device, and may connect various parts of the whole electronic device by using various interfaces and lines, and perform various functions and process data of the electronic device by operating or executing instructions stored in the memory 502 and calling data stored in the memory 502, thereby performing overall monitoring on the electronic device. Optionally, the processor 501 may include one or more processing units, and the processor 501 may integrate an application processor and a modem processor, wherein the processor 501 mainly processes an operating system, a user interface, an application program, and the like, and the modem processor mainly processes wireless communication. It will be appreciated that the modem processor described above may not be integrated into the processor 501. In some embodiments, processor 501 and memory 502 may be implemented on the same chip, or in some embodiments, they may be implemented separately on separate chips.
The processor 501 may be a general-purpose processor, such as a Central Processing Unit (CPU), digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like, that may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present disclosure. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present disclosure may be embodied directly in a hardware processor, or in a combination of hardware and software modules.
The communication interface 503 is a transmission interface capable of being used for communication, the electronic device may receive data or send data through the communication interface 503, for example, the acquired operation trajectory information may be sent to a background server of the application software through the communication interface 503, and a determination result of whether the operation trajectory in the operation trajectory information is an abnormal operation trajectory or an abnormal operation trajectory, which is sent by the background server, may also be received through the communication interface 503.
Referring to FIG. 6, a further block diagram of the electronic device is shown, which further includes a basic input/output system (I/O system) 601 for facilitating information transfer between the various components within the electronic device, and a mass storage device 605 for storing an operating system 602, application programs 603, and other program modules 604.
The basic input/output system 601 comprises a display 606 for displaying information and an input device 607, such as a mouse, keyboard, etc., for a user to input information. Wherein a display 606 and an input device 607 are connected to the processor 501 via a basic input/output system 601 connected to the system bus 500. The basic input/output system 601 may also include an input/output controller for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, an input-output controller may also provide output to a display screen, a printer, or other type of output device.
The mass storage device 605 is connected to the processor 501 through a mass storage controller (not shown) connected to the system bus 500. The mass storage device 605 and its associated computer-readable media provide non-volatile storage for the server package. That is, the mass storage device 605 may include a computer-readable medium (not shown), such as a hard disk or CD-ROM drive.
According to various embodiments of the present disclosure, the electronic package may also be operated by a remote computer connected to a network via a network, such as the internet. That is, the electronic device may be connected to the network 608 via the communication interface 503 coupled to the system bus 500, or may be connected to another type of network or remote computer system (not shown) using the communication interface 503.
Based on the foregoing embodiments, an embodiment of the present disclosure further provides a storage medium including an instruction, for example, a memory including an instruction, where the instruction may be executed by a processor in a device for reporting a software exception as shown in fig. 4a and 4b, to implement any one of the foregoing methods for reporting a software exception or any one of the methods that may be involved in any one of the methods for reporting a software exception.
In some possible implementations, the storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and so forth.
In some possible embodiments, the various aspects of the method for reporting a software exception provided by the embodiments of the present disclosure may also be implemented in a form of a program product including program code for causing a computer to perform the steps of the method for reporting a software exception according to the various exemplary embodiments of the present disclosure described above when the program product is run on the computer.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for reporting software exception is characterized by comprising the following steps:
acquiring operation track information aiming at specified application software, wherein the operation track information is used for indicating an operation behavior set consisting of at least two operation behaviors according to a certain time sequence;
determining whether the operation track corresponding to the operation track information is an abnormal operation track or not through a pre-trained track judgment model, wherein the track judgment model is obtained by training an operation track training sample corresponding to a target account, and the operation track training sample is subjected to marking of the abnormal operation track or the non-abnormal operation track;
and if the operation track is determined to be an abnormal operation track, reporting the operation track information to a server of the application software.
2. The method of claim 1, wherein before determining whether the operation trajectory corresponding to the operation trajectory information is an abnormal operation trajectory through a pre-trained trajectory determination model, the method further comprises:
comparing the operation track with a preset abnormal operation track to determine whether the operation track is an abnormal operation track;
if the operation track is determined to be an abnormal operation track, directly reporting operation track information corresponding to the abnormal operation track to a server of the application software;
and if the operation track is a non-abnormal operation track, inputting the non-abnormal operation track into the track judgment model.
3. The method of claim 1, wherein the trajectory estimation model training process comprises:
obtaining an operation track training sample set, wherein the operation track training sample set is a historical operation track set of the target user for the application software, and each operation track training sample in the operation track training sample set is labeled with an abnormal operation track or a non-abnormal operation track;
and training the initial trajectory judgment model for multiple times through the operation trajectory training sample set until the judgment result of the trained trajectory judgment model is consistent with the label of each operation trajectory training sample, and taking the trajectory judgment model as the trajectory judgment model of the target account.
4. The method of claim 3, wherein each of the plurality of training processes for the initial trajectory determination model through the set of operation trajectory training samples is as follows:
aiming at each operation track training sample in the operation track training sample set, obtaining an operation track characteristic vector of each operation track training sample;
inputting the operation track characteristic vector of each operation track training sample into a track judgment model used in the current training to obtain a judgment result of each operation track training sample, and comparing the judgment result with the label of each operation track training sample to obtain a comparison result corresponding to each operation track training sample;
and adjusting the track judgment model used in the training according to the comparison result.
5. The method of claim 2, wherein the method further comprises:
and updating the preset abnormal operation track after the version of the application software is determined to be updated.
6. The method of any of claims 1-5, wherein the method further comprises:
acquiring an actual operation result of the application software after the operation track is executed;
and if the actual operation result is not consistent with the application software operation result corresponding to the judgment result obtained according to the judgment model, marking the operation track according to the actual operation result to obtain a new operation track training sample, wherein the operation track training sample is used for retraining the track judgment model.
7. An apparatus for reporting software exception, comprising:
an acquisition unit configured to perform acquisition of operation trajectory information for a specific application software, wherein the operation trajectory information is used for indicating an operation behavior set composed of at least two operation behaviors in a time sequence;
the first track judging unit is configured to execute a track judging model trained in advance to determine whether an operation track corresponding to the operation track information is an abnormal operation track, wherein the track judging model is obtained by training an operation track training sample corresponding to a target account, and the operation track training sample is subjected to marking of an abnormal operation track or a non-abnormal operation track;
and the exception reporting unit is configured to report the operation track information to the server of the application software when the operation track is determined to be an exception operation track.
8. The apparatus of claim 7, wherein the apparatus further comprises: a second trajectory determination unit configured to perform:
comparing the operation track with a preset abnormal operation track to determine whether the operation track is an abnormal operation track;
if the operation track is determined to be an abnormal operation track, directly reporting operation track information corresponding to the abnormal operation track to a server of the application software;
and if the operation track is a non-abnormal operation track, inputting the non-abnormal operation track into the track judgment model.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of reporting software exceptions of any of claims 1-6.
10. A storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of reporting a software exception as claimed in any one of claims 1-6.
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