CN113254329A - Bug processing method, system, equipment and storage medium based on machine learning - Google Patents
Bug processing method, system, equipment and storage medium based on machine learning Download PDFInfo
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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Abstract
The invention discloses a Bug processing method, a system, equipment and a storage medium based on machine learning, wherein the method comprises the following steps: marking a Bug problem type label on the historical Bug log; inputting the marked historical Bug logs as training data into a machine learning algorithm, and training to obtain a Bug type determination model; and the Bug type determination model takes the Bug log as input and takes the Bug problem type as output. The problem type can be further determined quickly based on the model, technical personnel can conveniently acquire the solution scheme for processing the Bug with the same problem type from the historical processing scheme quickly based on the problem type for reference, the input of labor cost in Bug problem processing is reduced, and meanwhile the Bug processing efficiency is also improved.
Description
Technical Field
The invention belongs to the field of computer software engineering, and particularly relates to a Bug processing method, system, equipment and storage medium based on machine learning.
Background
In the production process of a company, a plurality of bugs often appear in the operation of a software program, and at the moment, a problem list needs to be proposed and summarized based on the bugs, and the Bug list is finally distributed to technicians in different technical fields to analyze and solve the bugs. As production accumulates, many newly-appearing bugs already have solutions or similar solutions, but due to the flow of company personnel, the technical personnel of the new catcher is not clear what the cause of the Bug is, and still needs to re-analyze and propose the solutions, which obviously needs to invest more labor cost and also seriously affects the production efficiency of the company.
Disclosure of Invention
The technical problem to be solved by the present invention is to overcome the above-mentioned drawbacks, and to provide a Bug processing method, system, device and storage medium based on machine learning.
The invention solves the technical problems through the following technical scheme:
a machine learning-based Bug processing method, the method comprising:
marking a Bug problem type label on the historical Bug log;
inputting the marked historical Bug logs as training data into a machine learning algorithm, and training to obtain a Bug type determination model;
and the Bug type determination model takes the Bug log as input and takes the Bug problem type as output.
Preferably, the step of inputting the marked historical Bug log as training data into the machine learning algorithm specifically includes:
extracting the Bug character strings corresponding to different data processing modules in the historical Bug logs;
coding all the Bug character strings to obtain Bug matrix data;
and inputting the Bug matrix data and the Bug type label into a machine learning algorithm as training data.
Preferably, after the step of extracting the Bug character strings corresponding to different data processing modules in the historical Bug log, the method further includes:
marking the Bug occurrence time of the historical Bug log;
the step of inputting the Bug matrix data and the Bug type label into a machine learning algorithm as training data further comprises:
and inputting the Bug occurrence time as training data into a machine learning algorithm.
Preferably, the machine learning algorithm comprises an unsupervised machine learning algorithm.
Preferably, after the step of training to obtain the Bug type determination model, the method further comprises:
acquiring a current Bug log;
and inputting the current Bug log into the Bug type determination model, and outputting the current Bug problem type corresponding to the current Bug log.
Preferably, after the step of outputting the current Bug problem type corresponding to the current Bug log, the method further includes:
and acquiring a corresponding Bug solution based on the current Bug problem type.
Preferably, after the step of obtaining the current Bug log, the method further includes:
acquiring the current Bug occurrence time of the current Bug log;
the step of inputting the current Bug log into the Bug type determination model specifically includes:
and inputting the current Bug log and the current Bug occurrence time into the Bug type determination model.
A machine learning based Bug processing system, the system comprising:
the marking module is used for marking a Bug problem type label on the historical Bug log;
the training module is used for inputting the marked historical Bug logs as training data into a machine learning algorithm and training to obtain a Bug type determination model;
and the Bug type determination model takes the Bug log as input and takes the Bug problem type as output.
Preferably, the training module specifically includes:
the character string extraction unit is used for extracting the Bug character strings corresponding to different data processing modules in the historical Bug logs;
the coding unit is used for coding all the Bug character strings to obtain Bug matrix data;
and the training unit is used for inputting the Bug matrix data and the Bug type label into a machine learning algorithm as training data.
Preferably, the method further comprises:
the time marking unit is used for marking the Bug occurrence time of the historical Bug log;
the training unit is also used for inputting the Bug occurrence time as training data into a machine learning algorithm.
Preferably, the machine learning algorithm comprises an unsupervised machine learning algorithm.
Preferably, the system further comprises:
the current log obtaining module is used for obtaining a current Bug log;
and the problem type determining module is used for inputting the current Bug log into the Bug type determining model and outputting the current Bug problem type corresponding to the current Bug log.
Preferably, the system further comprises:
and the solution obtaining module is used for obtaining a corresponding Bug solution based on the current Bug problem type.
Preferably, the system further comprises:
the time acquisition module is used for acquiring the current Bug occurrence time of the current Bug log;
and the problem type determining module is used for inputting the current Bug log and the current Bug occurrence time into the Bug type determining model.
An electronic device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor implements the machine learning-based Bug processing method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described machine learning-based Bug processing method.
The positive progress effects of the invention are as follows: historical Bug training is carried out based on machine learning algorithm in this application, obtains Bug type determination model, can further realize confirming the problem type fast to the Bug that appears based on this model, makes things convenient for the technical staff to obtain the solution of handling the Bug of same problem type from historical processing scheme fast for the reference based on the problem type, reduces the input of human cost among the Bug problem processing, has also improved the efficiency that Bug was handled simultaneously.
Drawings
Fig. 1 is a flowchart of a Bug processing method based on machine learning according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of step 20 in the Bug processing method based on machine learning according to embodiment 1 of the present invention.
Fig. 3 is a flowchart of step 20 in the Bug processing method based on machine learning according to embodiment 2 of the present invention.
Fig. 4 is a flowchart of a Bug processing method based on machine learning according to embodiment 2 of the present invention.
Fig. 5 is a block diagram of a Bug processing system based on machine learning according to embodiment 3 of the present invention.
Fig. 6 is a schematic block diagram of a training module in the Bug processing system based on machine learning according to embodiment 3 of the present invention.
Fig. 7 is a schematic structural diagram of an electronic device according to embodiment 4 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
A Bug processing method based on machine learning, as shown in fig. 1, the method comprising:
The Bug type determination model takes the Bug log as input and takes the Bug problem type as output.
and step 40, inputting the current Bug log into the Bug type determination model, and outputting the current Bug problem type corresponding to the current Bug log.
And step 50, acquiring a corresponding Bug solution based on the current Bug problem type.
It should be noted that, after processing the Bug according to the analysis result, the technician may mark the occurrence time and type of the Bug, and supplement the Bug to the training data, and through continuous accumulation of data, periodically start model optimization of the machine learning algorithm, thereby continuously optimizing the accuracy of the model.
In this embodiment, as shown in fig. 2, step 20 specifically includes:
And step 23, inputting the Bug matrix data and the Bug type label as training data into a machine learning algorithm, and training to obtain a Bug type determination model.
In consideration of the fact that a plurality of data processing modules are involved in the operation of a software system in practical application, one Bug may need a plurality of data processing modules to be comprehensively analyzed, so that all the modules are learned together for Bug analysis, and the accuracy of Bug automatic analysis can be further improved.
In the embodiment, historical bugs are trained based on a machine learning algorithm to obtain a Bug type determination model, and problem types can be further determined rapidly for the bugs on the basis of the model, so that technical personnel can conveniently obtain solutions for processing bugs with the same problem types from historical processing schemes rapidly for reference based on the problem types, the input of labor cost in Bug problem processing is reduced, and meanwhile the Bug processing efficiency is improved.
Example 2
The Bug processing method based on machine learning in this embodiment is further improved on the basis of embodiment 1, and taking an Android project as an example, each Bug generates a large number of logs (some logs have several G), so that it is unrealistic to let the machine learning algorithm automatically learn from all logs, some data rules need to be made to limit the machine learning direction, and the processing experience of machine learning is accumulated through purposeful learning, so that the accuracy of Bug analysis is improved.
As shown in fig. 3, after step 21, step 20 further includes:
further, referring to fig. 3, step 23 specifically includes:
and 231, inputting the Bug occurrence time, the Bug matrix data and the Bug type label as training data into a machine learning algorithm, and training to obtain a Bug type determination model.
In addition, as shown in fig. 4, after step 30, the method further includes:
further, referring to fig. 3, step 40 specifically includes:
In this embodiment, for a large number of Bug logs, data used in model training is limited by time, so as to ensure validity of machine learning and improve accuracy of the model. It should be noted that the data extracted by training is not only data in the Bug occurrence time period, and the learning range of the data can be adaptively adjusted as needed in the machine learning process.
Example 3
A machine learning based Bug processing system, as shown in fig. 5, the system comprising:
the marking module 1 is used for marking a Bug problem type label on a historical Bug log;
the training module 2 is used for inputting the marked historical Bug logs as training data into a machine learning algorithm and training to obtain a Bug type determination model; the machine learning algorithm comprises an unsupervised machine learning algorithm so as to reduce interference of human factors on model training and optimization.
And the Bug type determination model takes the Bug log as input and takes the Bug problem type as output.
The current log obtaining module 3 is used for obtaining a current Bug log;
and the problem type determining module 4 is used for inputting the current Bug log into the Bug type determining model and outputting the current Bug problem type corresponding to the current Bug log.
And the solution obtaining module 5 is configured to obtain a corresponding Bug solution based on the current Bug problem type.
It should be noted that, after processing the Bug according to the analysis result, the technician may mark the occurrence time and type of the Bug, and supplement the Bug to the training data, and through continuous accumulation of data, periodically start model optimization of the machine learning algorithm, thereby continuously optimizing the accuracy of the model.
In this embodiment, as shown in fig. 6, the training module 2 specifically includes:
a character string extraction unit 201, configured to extract Bug character strings corresponding to different data processing modules in the historical Bug log;
the encoding unit 202 is configured to encode all the Bug character strings to obtain Bug matrix data; and the matrix data obtained after coding accords with the data format of machine learning.
And the training unit 203 is used for inputting the Bug matrix data and the Bug type label as training data into a machine learning algorithm.
In consideration of the fact that a plurality of data processing modules are involved in the operation of a software system in practical application, one Bug may need a plurality of data processing modules to be comprehensively analyzed, so that all the modules are learned together for Bug analysis, and the accuracy of Bug automatic analysis can be further improved.
In this embodiment, taking an Android project as an example, each Bug generates a large number of logs (some logs have several G), so that it is unrealistic to let a machine learning algorithm automatically learn from all logs, some data rules need to be made to limit the machine learning direction, machine learning processing experience is accumulated through purposeful learning, and the accuracy of Bug analysis is improved. Referring to fig. 6, the method the training module 2 further comprises:
the time marking unit 204 is used for marking the Bug occurrence time of the historical Bug log;
the training unit 203 is further configured to input the Bug occurrence time as training data into a machine learning algorithm.
Further, referring to fig. 5, the system further includes:
the time obtaining module 6 is used for obtaining the current Bug occurrence time of the current Bug log;
the problem type determination module 4 is configured to input the current Bug log and the current Bug occurrence time into the Bug type determination model.
For a large number of Bug logs, data used in model training is limited by time, so that the effectiveness of machine learning is ensured, and the accuracy of the model is improved. It should be noted that the data extracted by training is not only data in the Bug occurrence time period, and the learning range of the data can be adaptively adjusted as needed in the machine learning process.
In the embodiment, historical bugs are trained based on a machine learning algorithm to obtain a Bug type determination model, and problem types can be further determined rapidly for the bugs on the basis of the model, so that technical personnel can conveniently obtain solutions for processing bugs with the same problem types from historical processing schemes rapidly for reference based on the problem types, the input of labor cost in Bug problem processing is reduced, and meanwhile the Bug processing efficiency is improved.
Example 4
An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the machine learning-based Bug processing method of embodiment 1 or 2 when executing the computer program.
Fig. 7 is a schematic structural diagram of an electronic device provided in this embodiment. FIG. 7 illustrates a block diagram of an exemplary electronic device 90 suitable for use in implementing embodiments of the present invention. The electronic device 90 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 7, the electronic device 90 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 90 may include, but are not limited to: at least one processor 91, at least one memory 92, and a bus 93 that connects the various system components (including the memory 92 and the processor 91).
The bus 93 includes a data bus, an address bus, and a control bus.
The processor 91 executes various functional applications and data processing by running a computer program stored in the memory 92.
The electronic device 90 may also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 95. Also, the electronic device 90 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via a network adapter 96. The network adapter 96 communicates with the other modules of the electronic device 90 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module, according to embodiments of the application. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 5
A computer-readable storage medium on which a computer program is stored, which when executed by a processor, implements the steps of the machine learning-based Bug processing method of embodiment 1 or 2.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation manner, the present invention can also be implemented in the form of a program product including program code for causing a terminal device to execute steps of implementing the machine learning based Bug processing method described in embodiment 1 or 2 when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may be executed entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (10)
1. A Bug processing method based on machine learning, the method comprising:
marking a Bug problem type label on the historical Bug log;
inputting the marked historical Bug logs as training data into a machine learning algorithm, and training to obtain a Bug type determination model;
and the Bug type determination model takes the Bug log as input and takes the Bug problem type as output.
2. The machine learning-based Bug processing method according to claim 1, wherein the step of inputting the labeled historical Bug logs as training data into a machine learning algorithm specifically comprises:
extracting the Bug character strings corresponding to different data processing modules in the historical Bug logs;
coding all the Bug character strings to obtain Bug matrix data;
and inputting the Bug matrix data and the Bug type label into a machine learning algorithm as training data.
3. The machine learning-based Bug processing method according to claim 2, wherein after the step of extracting the Bug character strings corresponding to different data processing modules in the historical Bug log, the method further comprises:
marking the Bug occurrence time of the historical Bug log;
the step of inputting the Bug matrix data and the Bug type label into a machine learning algorithm as training data further comprises:
and inputting the Bug occurrence time as training data into a machine learning algorithm.
4. The machine-learning-based Bug processing method of claim 1, wherein the machine learning algorithm comprises an unsupervised machine learning algorithm.
5. The machine learning-based Bug processing method according to claim 1, wherein after the step of training to obtain the Bug type determination model, the method further comprises:
acquiring a current Bug log;
and inputting the current Bug log into the Bug type determination model, and outputting the current Bug problem type corresponding to the current Bug log.
6. The machine learning-based Bug processing method according to claim 5, wherein after the step of outputting the current Bug problem type corresponding to the current Bug log, the method further comprises:
and acquiring a corresponding Bug solution based on the current Bug problem type.
7. The machine learning-based Bug processing method according to claim 5, wherein after the step of obtaining a current Bug log, the method further comprises:
acquiring the current Bug occurrence time of the current Bug log;
the step of inputting the current Bug log into the Bug type determination model specifically includes:
and inputting the current Bug log and the current Bug occurrence time into the Bug type determination model.
8. A machine learning based Bug processing system, the system comprising:
the marking module is used for marking a Bug problem type label on the historical Bug log;
the training module is used for inputting the marked historical Bug logs as training data into a machine learning algorithm and training to obtain a Bug type determination model;
and the Bug type determination model takes the Bug log as input and takes the Bug problem type as output.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the machine learning-based Bug processing method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium on which a computer program is stored, the program implementing the steps of the machine learning-based Bug processing method according to any of claims 1 to 7 when executed by a processor.
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