CN111078443B - Method and device for automatically collecting and reporting defects and server - Google Patents
Method and device for automatically collecting and reporting defects and server Download PDFInfo
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- CN111078443B CN111078443B CN201811235640.5A CN201811235640A CN111078443B CN 111078443 B CN111078443 B CN 111078443B CN 201811235640 A CN201811235640 A CN 201811235640A CN 111078443 B CN111078443 B CN 111078443B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0766—Error or fault reporting or storing
- G06F11/0781—Error filtering or prioritizing based on a policy defined by the user or on a policy defined by a hardware/software module, e.g. according to a severity level
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- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0766—Error or fault reporting or storing
- G06F11/0775—Content or structure details of the error report, e.g. specific table structure, specific error fields
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention is suitable for the technical field of computers, and provides a method, a device and a server for automatically collecting and reporting defects, wherein the method for automatically collecting and reporting the defects comprises the following steps: receiving abnormal log information captured by a mobile terminal according to a preset log information filtering rule; analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing; adjusting and optimizing the log information filtering rule according to a defect processing code of a machine learning responsible person; and sending the adjusted and optimized log information filtering rule to the mobile terminal. According to the invention, the abnormal log information is captured according to the preset log information filtering rule, the abnormal log information is distributed to the corresponding responsible person for defect processing, and the log information filtering rule is adjusted and optimized according to the defect processing code of the machine learning responsible person, so that the whole process is automatic, and the efficiency is greatly improved.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a method, a device and a server for automatically acquiring and reporting defects.
Background
The process of collecting and solving the product defects (bugs) is required from development to marketing of current internet mobile end products. The existing bug collection management method has the disadvantages that log collection is overstaffed, some unnecessary log information can be completely uploaded to a background management system, network transmission flow is increased, and certain interference is brought to development and elimination problems. Meanwhile, the bug management is not automatic enough, the distribution management needs to be carried out manually, and the efficiency is low.
In the prior art, a method for automatically collecting logs and locating and tracking problems of android (android) equipment comprises the following steps: step 1, log capture service is registered in an android background service process, the log capture service acquires logs recorded during system operation according to a standard time format, and the recorded logs are uploaded to a log processing server through an IPTV set top box in an active uploading mode; step 2, the log processing server collects information of the recorded logs and transmits abnormal information to a big data analysis technology and a BUG LIST (BUG LIST) platform; and 3, analyzing the abnormal information and mining favorable information by using a big data analysis technology, recording the abnormal information by using the BUG LIST platform, comparing the abnormal information with historical abnormal information, comparing whether an abnormal processing method with similar types exists or not, and feeding all the obtained information back to the log processing platform. Therefore, the collected log information is not filtered, the network data volume is increased during uploading, and meanwhile, invalid log information brings certain interference to the problem troubleshooting of a developer; in the bug management background, the bug can only be manually allocated to the relevant responsible person through manual work, the allocator may not know who the responsible person corresponding to the bug is, and meanwhile, the manual allocation efficiency is low.
Disclosure of Invention
The embodiment of the invention provides a method, a device and a server for automatically acquiring and reporting defects, and aims to solve the problems that in the prior art, because log information is not filtered, and meanwhile, the defects need to be manually distributed to responsible persons, the network data volume is increased, and the efficiency is low.
A method for automatically collecting and reporting defects comprises the following steps:
receiving abnormal log information captured by a mobile terminal according to a preset log information filtering rule;
analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
adjusting and optimizing the log information filtering rule according to a defect processing code of a machine learning responsible person;
and sending the adjusted and optimized log information filtering rule to the mobile terminal.
Preferably, the receiving the log captured by the mobile terminal according to the preset log information filtering rule further includes:
the defect is associated with the principal.
Preferably, said associating the defect with the responsible person further comprises:
submitting a defect processing code record through the Gitlab by using the code annotation to associate the defect processing code with a responsible person;
and associating the responsible person mails by using office software.
Preferably, the abnormal log information includes code person in charge, mail, number of times of submitting gitlab, time of submitting gitlab, job status, number of times of recording defect operation, and characteristic value of defect operation time.
Preferably, the analyzing the abnormal log information and distributing the abnormal log information to a corresponding person in charge for defect handling includes:
carrying out format analysis on the abnormal log information, and persisting the analyzed abnormal log information in a database;
automatically distributing the information to the code responsible persons according to the information of the code responsible persons;
and sorting the categories according to the severity of the defects, and simultaneously notifying a person in charge by using an email.
Preferably, the adjusting and optimizing the log information filtering rule according to the defect handling code of the machine learning leader comprises:
when the same type of defects are encountered, a defect solving method is automatically given and recommended to the responsible person, and the defect solving code is corrected according to the submission record of the responsible person.
The invention also provides a device for automatically acquiring and reporting the defects, which comprises:
the information receiving unit is used for receiving abnormal log information captured by the mobile terminal according to a preset log information filtering rule;
the information analysis unit is connected with the information receiving unit and used for analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
the rule adjusting unit is connected with the information analyzing unit and used for adjusting and optimizing the log information filtering rule according to the defect processing code of the machine learning responsible person;
and the rule issuing unit is connected with the rule adjusting unit and is used for issuing the log information filtering rule after adjustment and optimization to the mobile terminal.
The invention also provides a server, which comprises an automatic defect acquisition and reporting device, wherein the automatic defect acquisition and reporting device comprises:
the information receiving unit is used for receiving abnormal log information captured by the mobile terminal according to a preset log information filtering rule;
the information analysis unit is connected with the information receiving unit and used for analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
the rule adjusting unit is connected with the information analyzing unit and used for adjusting and optimizing the log information filtering rule according to the defect processing code of the machine learning responsible person;
and the rule issuing unit is connected with the rule adjusting unit and is used for issuing the log information filtering rule after adjustment and optimization to the mobile terminal.
The present invention also provides a memory storing a computer program, wherein the computer program is executed by a processor to perform the steps of:
receiving abnormal log information captured by a mobile terminal according to a preset log information filtering rule;
analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
adjusting and optimizing the log information filtering rule according to a defect processing code of a machine learning responsible person;
and sending the adjusted and optimized log information filtering rule to the mobile terminal.
The invention also provides a service terminal, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the following steps:
receiving abnormal log information captured by a mobile terminal according to a preset log information filtering rule;
analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
adjusting and optimizing the log information filtering rule according to a defect processing code of a machine learning responsible person;
and sending the adjusted and optimized log information filtering rule to the mobile terminal.
In the embodiment of the invention, the abnormal log information is captured according to the preset log information filtering rule and is distributed to the corresponding responsible person for defect processing, and the log information filtering rule is adjusted and optimized according to the defect processing code of the machine learning responsible person, so that the whole process is automatic, and the efficiency is greatly improved.
Drawings
Fig. 1 is a flowchart of a method for automatically acquiring and reporting defects according to a first embodiment of the present invention;
fig. 2 is a flowchart of a preferred method of an automatic defect collection and reporting method according to a first embodiment of the present invention;
fig. 3 is a structural diagram of an automatic defect collection and reporting apparatus according to a second embodiment of the present invention;
fig. 4 is a structural diagram of a preferred embodiment of an apparatus for automatically collecting and reporting defects according to a first embodiment of the present invention;
fig. 5 is a structural diagram of a service terminal according to a fifth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In an embodiment of the present invention, an automatic defect acquisition and reporting method includes: receiving abnormal log information captured by a mobile terminal according to a preset log information filtering rule; analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing; adjusting and optimizing the log information filtering rule according to a defect processing code of a machine learning responsible person; and sending the adjusted and optimized log information filtering rule to the mobile terminal.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
The first embodiment is as follows:
fig. 1 is a flowchart illustrating a method for automatically collecting and reporting defects according to a first embodiment of the present invention, where the method includes:
s1, receiving abnormal log information captured by a mobile terminal according to a preset log information filtering rule;
specifically, in the mobile terminal code, if an abnormality occurs, the relevant abnormal log information including a code person in charge, a timestamp, a call stack, a screenshot and the like is actively collected according to a filtering rule issued by the server, and is uploaded to the log processing server according to a standard format. And the server receives the abnormal log information uploaded by the mobile terminal. The format of the exception log information is preferably json format, as follows:
in this embodiment, the abnormality log information includes the code person in charge, the mail, the number of times of submitting gitlab, the time of submitting gitlab, the job status, the number of times of recording the defect operation, and the characteristic value of the defect operation time.
S2, analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
specifically, format analysis is carried out on the abnormal log information, the analyzed abnormal log information is persisted in a database, and meanwhile the abnormal log information is sent to a Buglist platform. Automatically distributing the code to the code responsible person through a Buglist platform according to the information of the code responsible person; sorting according to the severity, category and the like of the defects, and simultaneously notifying a person in charge by an email.
S3, adjusting and optimizing the log information filtering rule according to the defect processing code of the machine learning responsible person;
specifically, the responsible person processes the defects, the background utilizes machine learning to count according to the processing mode of the responsible person on the defects, such as the number of times of opening the bug screenshot and the number of times of opening the call stack. If the number of times of opening is more than the number of times of not opening, the data is considered to need statistical learning, and the data corresponding to different defects needs to be collected. And then adjusting the log information filtering rule configuration.
S4, sending the log information filtering rule after adjustment and optimization to the mobile terminal;
later defects can use the latest log information filtering rules in the acquisition process, the information which does not need to be acquired is filtered, the operation is circulated in the way, manual intervention is not needed in the whole process, and the automation of the whole process is realized.
In the embodiment, the abnormal log information is captured according to the preset log information filtering rule, the abnormal log information is distributed to the corresponding responsible person for defect processing, the log information filtering rule is adjusted and optimized according to the defect processing code of the machine learning responsible person, the whole process is automatic, and the efficiency is greatly improved.
In a preferable embodiment of this embodiment (see fig. 2), before the step S1, the method further includes:
and step S10, associating the defect with the responsible person.
Specifically, the defect handling code is associated with the responsible person by submitting a defect handling code record through the Gitlab by using the code annotation; the responsible person is associated with the mail by office software such as nails. After the abnormal log information is uploaded to the log processing server, the responsible person corresponding to the defect can be automatically identified, and meanwhile, the nail mail is used for informing and reminding.
The background bug management system utilizes a naive Bayesian algorithm in machine learning, codes are used for annotating mails (weight 1), the number of times of submitting gitlab (weight 3), the time of submitting gitlab (weight 2), the on-duty state (weight 5), the number of times of recording defect operations (weight 3), and the time of recording defect operations (weight 2) are used as characteristic values, each time a code is submitted, the on-duty state changes, the defect operations can calculate and update a weight list of code associators, and the first weight value is the principal of the defect. And the weights of the individual characteristic values can be adjusted by means of the error samples. The aim of whole-process automation can be achieved by automatically updating the defect associated person list.
In a preferred embodiment of the present invention, after the log information filtering rule after being adjusted and optimized is issued to the mobile terminal in step S4, when the same type of defect is encountered, a defect solving method is automatically given and recommended to a responsible person, and a defect solving code is corrected according to a submission record of the responsible person.
In the embodiment, the abnormal log information is captured according to the preset log information filtering rule and is distributed to the corresponding responsible person for defect processing, and the log information filtering rule is adjusted and optimized according to the defect processing code of the machine learning responsible person, so that the whole process is automatic, and the efficiency is greatly improved.
And secondly, automatically updating the defect associated person list through machine learning, thereby achieving the purpose of whole-process automation.
The second embodiment:
as shown in fig. 3, a structural diagram of an automatic defect collection and reporting apparatus according to a second embodiment of the present invention is provided, where the automatic defect collection and reporting apparatus includes: the information receiving unit 1, the information analysis unit 2 connected with the information receiving unit 1, the rule adjustment unit 3 connected with the information analysis unit 2, and the rule issuing unit 4 connected with the rule adjustment unit 3, wherein:
the information receiving unit 1 is used for receiving abnormal log information captured by the mobile terminal according to a preset log information filtering rule;
specifically, in the mobile terminal code, if an abnormality occurs, relevant abnormal log information including a code responsible person, a timestamp, a call stack, a screenshot and the like is actively acquired according to a filtering rule issued by a server, and is uploaded to a log processing server according to a standard format. The information receiving unit 1 receives the abnormal log information uploaded by the mobile terminal. The format of the abnormal log information is preferably json format.
In this embodiment, the abnormality log information includes the code person in charge, the mail, the number of times of submitting gitlab, the time of submitting gitlab, the job status, the number of times of recording the defect operation, and the characteristic value of the defect operation time.
The information analysis unit 2 is used for analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
specifically, the information analysis unit 2 performs format analysis on the abnormal log information, persists the analyzed abnormal log information in a database, and sends the abnormal log information to the Buglist platform. The information analysis unit 2 also automatically distributes the information to the code responsible person through a Buglist platform according to the information of the code responsible person; sorting according to the severity, category and the like of the defects, and simultaneously notifying a person in charge by an email.
The rule adjusting unit 3 is used for adjusting and optimizing the log information filtering rule according to the defect processing code of the machine learning responsible person;
specifically, the responsible person processes the defect, and the rule adjusting unit 3 performs statistics, such as the number of times of opening the bug screenshot and the number of times of opening the call stack, according to the processing mode of the responsible person on the defect by using machine learning. If the number of times of opening is more than the number of times of not opening, the data is considered to need statistical learning, and the data corresponding to different defects needs to be collected. The rule adjusting unit 3 then adjusts the log information filtering rule configuration.
A rule issuing unit 4, configured to issue the log information filtering rule after adjustment and optimization to the mobile terminal;
and then, the mobile terminal can use the latest log information filtering rule in the acquisition process to filter the information which does not need to be acquired, the process is circulated in such a way, manual intervention is not needed in the whole process, and the automation of the whole process is realized.
In the embodiment, the abnormal log information is captured according to the preset log information filtering rule, the abnormal log information is distributed to the corresponding responsible person for defect processing, the log information filtering rule is adjusted and optimized according to the defect processing code of the machine learning responsible person, the whole process is automatic, and the efficiency is greatly improved.
In a preferable aspect of this embodiment, as shown in fig. 4, the apparatus further includes: an association unit 5 connected to the information receiving unit 1, wherein:
an association unit 5 for associating the defect with a person in charge;
specifically, the association unit 5 associates the defect handling code with the person in charge by submitting the defect handling code record through the Gitlab using the code annotation; the responsible person is not aware of the association of the mail with office software such as nails. After the abnormal log information is uploaded to the log processing server, the responsible person corresponding to the defect can be automatically identified, and meanwhile, the nail mail is used for informing and reminding.
The association unit 5 annotates the mail (weight 1), the number of times of submission of gitlab (weight 3), the time of submission of gitlab (weight 2), the on-duty state (weight 5), the number of times of recording of the defect operation (weight 3), and the time of the defect operation (weight 2) as characteristic values by using a naive Bayesian algorithm in machine learning through a background bug management system, calculates and updates a weight list of code associators each time the code is submitted and the on-duty state changes, wherein the first weight value is a person in charge of the defect. The associating unit 5 may also adjust the weight of the respective characteristic value by the erroneous sample. The purpose of whole-process automation can be achieved by automatically updating the defect associated person list.
In a preferred scheme of this embodiment, after the rule issuing unit 4 issues the log information filtering rule after adjustment and optimization to the mobile terminal, when the same type of defects are encountered, a defect solution method is automatically given and recommended to a responsible person, and a defect solution code is corrected according to a submission record of the responsible person.
In the embodiment, the abnormal log information is captured according to the preset log information filtering rule, the abnormal log information is distributed to the corresponding responsible person for defect processing, the log information filtering rule is adjusted and optimized according to the defect processing code of the machine learning responsible person, the whole process is automatic, and the efficiency is greatly improved.
And secondly, automatically updating the defect associated person list through machine learning, thereby achieving the purpose of whole-process automation.
Example three:
based on the second embodiment, the present invention further provides a server, where the server includes the apparatus for automatically acquiring and reporting defects as described in the second embodiment, and the specific structure and the working principle of the apparatus for automatically acquiring and reporting defects may refer to the description of the second embodiment, and are not described herein again.
In the embodiment, the abnormal log information is captured according to the preset log information filtering rule, the abnormal log information is distributed to the corresponding responsible person for defect processing, the log information filtering rule is adjusted and optimized according to the defect processing code of the machine learning responsible person, the whole process is automatic, and the efficiency is greatly improved.
Example four:
fig. 5 is a block diagram illustrating a service terminal according to a fifth embodiment of the present invention, where the service terminal includes: a memory (memory) 51, a processor (processor) 52, a communication Interface (Communications Interface) 53 and a bus 54, wherein the processor 52, the memory 51 and the communication Interface 53 complete mutual communication through the bus 54.
A memory 51 for storing various data;
specifically, the memory 51 is used for storing various data, such as log information filtering rules, received abnormal log information, and the like, and is not limited thereto, and includes a plurality of computer programs.
A communication interface 53 for information transmission between communication devices of the service terminal;
the processor 52 is configured to call various computer programs in the memory 51 to execute the method for automatically collecting and reporting defects provided in the first embodiment, for example:
receiving abnormal log information captured by a mobile terminal according to a preset log information filtering rule;
analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
adjusting and optimizing the log information filtering rule according to a defect processing code of a machine learning responsible person;
and sending the adjusted and optimized log information filtering rule to the mobile terminal.
In the embodiment, the abnormal log information is captured according to the preset log information filtering rule, the abnormal log information is distributed to the corresponding responsible person for defect processing, the log information filtering rule is adjusted and optimized according to the defect processing code of the machine learning responsible person, the whole process is automatic, and the efficiency is greatly improved.
The present invention further provides a memory, where the memory stores a plurality of computer programs, and the computer programs are called by the processor to execute the method for automatically collecting and reporting defects according to the first embodiment.
According to the invention, the abnormal log information is captured according to the preset log information filtering rule and is distributed to the corresponding responsible person for defect processing, and the log information filtering rule is adjusted and optimized according to the defect processing code of the machine learning responsible person, so that the whole process is automatic, and the efficiency is greatly improved.
And secondly, automatically updating the defect associated person list through machine learning to achieve the purpose of whole-process automation.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation.
Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for automatically collecting and reporting defects is characterized by comprising the following steps:
receiving abnormal log information captured by a mobile terminal according to a preset log information filtering rule;
analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
adjusting and optimizing the log information filtering rule according to a defect processing code of a machine learning responsible person, wherein the responsible person processes the defect, the background utilizes machine learning to count data required to be acquired corresponding to different defects according to the processing mode of the responsible person on the defect, and the log information filtering rule configuration is adjusted;
and sending the adjusted and optimized log information filtering rule to the mobile terminal.
2. The method according to claim 1, wherein the step of capturing the log by the receiving mobile terminal according to the preset log information filtering rule further comprises:
the defect is associated with the principal.
3. The method for automatically collecting and reporting defects as claimed in claim 2, wherein the associating the defects with the responsible persons further comprises:
submitting a defect processing code record through Gitlab by using the code annotation to associate the defect processing code with a responsible person;
and associating the responsible person mails by using office software.
4. The method according to claim 3, wherein the abnormal log information includes a code person in charge, a mail, a number of times of submission of gitlab, a time of submission of gitlab, an on-duty state, a number of times of recording a defect operation, and a characteristic value of a time of the defect operation.
5. The method for automatically collecting and reporting the defects according to claim 1, wherein the analyzing the abnormal log information and distributing the abnormal log information to a corresponding person in charge for defect handling comprises:
analyzing the format of the log, and persisting the analyzed log in a database;
automatically distributing the information to the code responsible persons according to the information of the code responsible persons;
and sorting the categories according to the severity of the defects, and simultaneously notifying a person in charge by using an email.
6. The method for automatically collecting and reporting the defects as claimed in claim 1, wherein the adjusting and optimizing the log information filtering rules according to the defect handling codes of the machine learning responsible persons comprises:
when the same type of defects are encountered, automatically giving a defect solution and recommending the defect solution to a responsible person, and correcting the defect solution code according to the submission record of the responsible person.
7. An automatic defect collecting and reporting device, comprising:
the information receiving unit is used for receiving abnormal log information captured by the mobile terminal according to a preset log information filtering rule;
the information analysis unit is connected with the information receiving unit and used for analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
the rule adjusting unit is connected with the information analyzing unit and used for adjusting and optimizing the log information filtering rule according to a defect processing code of a machine learning responsible person, wherein the responsible person processes the defect, the background utilizes the machine learning to count data needing to be acquired corresponding to different defects according to the processing mode of the responsible person on the defect, and the log information filtering rule configuration is adjusted;
and the rule issuing unit is connected with the rule adjusting unit and is used for issuing the log information filtering rule after adjustment and optimization to the mobile terminal.
8. A server, characterized by comprising the apparatus for automatically collecting and reporting defects according to claim 7.
9. A memory storing a computer program, wherein the computer program is executed by a processor to perform the steps of:
receiving abnormal log information captured by a mobile terminal according to a preset log information filtering rule;
analyzing the abnormal log information and distributing the abnormal log information to a corresponding responsible person for defect processing;
adjusting and optimizing the log information filtering rule according to a defect processing code of a machine learning responsible person, wherein the responsible person processes the defect, the background utilizes the machine learning to count data required to be collected corresponding to different defects according to the processing mode of the responsible person on the defect, and the log information filtering rule configuration is adjusted;
and sending the adjusted and optimized log information filtering rule to the mobile terminal.
10. A service terminal, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements the steps of the method for automatically collecting and reporting defects according to any one of claims 1 to 6 when executing the computer program.
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| CN108388503A (en) * | 2018-02-13 | 2018-08-10 | 中体彩科技发展有限公司 | Data-base performance monitoring method, system, equipment and computer readable storage medium |
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