Program Bug automatic submission method and device and medium

Abstract

The invention discloses an automatic submission method and device of a program Bug and a medium. The method comprises the following steps: collecting a historical data set containing Bug information; training a pre-constructed Bug recognition network architecture according to the historical data set to generate a Bug recognition model; training the generative adversarial network by using the historical data set to generate a Bug description generation model; performing Bug identification on the program to be identified by adopting the generated Bug identification model to obtain Bug information of the program to be identified; a Bug description generation model is adopted to describe the Bug information, and Bug description of the Bug information is obtained; and automatically submitting the Bug information and the Bug description of the Bug information to a preset system by utilizing a preset program interface.

Description

Technical Field

[0001] The present invention relates to the technical field of program bug processing, and more specifically, to a method, device and medium for automatically submitting program bugs. Background Art

[0002] In the programming process, bugs can be syntax errors, logic errors, or algorithm errors. Syntax errors are caused by the program code not conforming to the syntax rules of the programming language. These errors are usually detected during compilation and corresponding error prompts are given. Logical errors refer to problems with the logical flow of the program, which causes the results of program execution to be inconsistent with expectations. Algorithmic errors refer to problems with the algorithm used in the program, which causes the program to be unable to correctly solve specific problems. Therefore, it is important to perform bug testing on the program. When testers perform functional testing, a large number of bugs will be found in the early stage, but manually submitting bugs will take a lot of time, and there will also be description errors, which will mislead developers. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method, device and medium for automatically submitting program bugs.

[0004] According to one aspect of the present invention, there is provided a method for automatically submitting a program bug, comprising:

[0005] Collect historical data sets containing bug information;

[0006] Train the pre-built bug recognition network architecture based on historical data sets to generate a bug recognition model;

[0007] Use historical data sets to train the generative adversarial network and generate a bug description generation model;

[0008] The generated bug identification model is used to identify the bugs of the program to be identified, and the bug information of the program to be identified is obtained;

[0009] The bug description generation model is used to describe the bug information and obtain the bug description of the bug information;

[0010] The bug information and the bug description of the bug information are automatically submitted to the preset system using the preset program interface.

[0011] Optionally, the bug information and the bug description of the bug information are automatically submitted to a preset system using a preset program interface, including:

[0012] Use the pre-trained classification model to classify the bug information, and determine the priority of each bug information according to the classification result of each bug information;

[0013] The bug information and the bug description of the bug information are automatically submitted to the preset system according to the priority using the preset program interface.

[0014] Optionally, it also includes: adjusting and optimizing the classification model, the bug identification model and the bug description generation model according to the bug verification feedback result of the preset system.

[0015] Optionally, the generative adversarial network includes a generator and a discriminator, the goal of the generator is to minimize the probability that the discriminator cannot correctly distinguish between the generated samples and the real samples, and the goal of the discriminator is to maximize the probability of correctly distinguishing between the generated samples and the real samples, where

[0016] The loss function of generator G is: L(G) = -E[D(G(z))]

[0017] The loss function of the discriminator D is:

[0018] Among them, z is the input noise of the generator, x is the real sample, is the linear interpolation between the real sample and the generated sample, and λ is the weight of the gradient penalty. Indicates that the discriminator is The norm of the gradient at .

[0019] According to another aspect of the present invention, there is provided an automatic submission device for a program bug, comprising:

[0020] The collection module is used to collect historical data sets containing bug information;

[0021] The first generation module is used to train the pre-built bug recognition network architecture according to the historical data set to generate a bug recognition model;

[0022] The second generation module is used to train the generative adversarial network using the historical data set to generate a bug description generation model;

[0023] The first acquisition module is used to perform bug recognition on the program to be recognized by using the generated bug recognition model to obtain bug information of the program to be recognized;

[0024] The second acquisition module is used to describe the bug information using the bug description generation model to obtain the bug description of the bug information;

[0025] The submission module is used to automatically submit the bug information and the bug description of the bug information to the preset system using the preset program interface.

[0026] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0027] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0028] Therefore, this application achieves automatic identification of program bugs and submission of programs by constructing a bug identification model and a bug description generation module, saving the time of submitting bugs, reducing the situation of inaccurate manual descriptions, and reducing the communication costs between development and testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0030] Figure 1 It is a flowchart of a method for automatically submitting a program bug provided by an exemplary embodiment of the present invention;

[0031] Figure 2 It is a structural diagram of an automatic submission device for program bugs provided by an exemplary embodiment of the present invention;

[0032] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0033] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0034] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0035] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0036] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0037] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0038] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0039] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0040] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0041] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0042] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0043] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0044] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0045] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0046] Exemplary Methods

[0047] Figure 1 FIG. 1 is a flow chart of a method for automatically submitting a program bug provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the automatic submission method 100 of a program bug includes the following steps:

[0048] Step 101, collecting historical data sets containing bug information;

[0049] Step 102, training the pre-built bug recognition network architecture according to the historical data set to generate a bug recognition model;

[0050] Step 103, using the historical data set to train the generative adversarial network to generate a bug description generation model;

[0051] Step 104, using the generated bug identification model to perform bug identification on the program to be identified, and obtaining bug information of the program to be identified;

[0052] Step 105, using a bug description generation model to describe the bug information and obtain a bug description of the bug information;

[0053] Step 106: Automatically submit the bug information and the bug description of the bug information to the preset system using a preset program interface.

[0054] Specifically, AI can be used to automatically submit bugs, saving time for manual bug submission and reducing inaccurate descriptions caused by manual typing. The specific implementation steps are as follows:

[0055] 1. Data preparation: Collect and organize data sets containing bug information. This data can come from bug tracking systems, version control systems, log files, etc. Ensure the quality and diversity of the data set.

[0056] 2. Build a bug identification model: Use machine learning or deep learning technology to build a suitable bug identification model. You can use pre-trained natural language processing (NLP) models, such as BERT, GPT, etc., or train a custom model based on a specific task. (The specific content is as follows: Error description correction based on semi-supervised learning: Build a semi-supervised learning model to automatically correct description errors by using a small number of manually corrected description error samples and a large amount of unlabeled data. Semi-supervised learning algorithms, such as self-training or pseudo-labeling, can be used to continuously improve the accuracy of the description during the self-learning process of the model.

[0057] Bug description generation based on generative adversarial network (GAN): Using a generative adversarial network, an existing bug dataset is used as a training set to train a generative model that can automatically generate accurate and clear bug descriptions. The generative model can use a variational autoencoder (VAE) or a variant of a generative adversarial network (GAN) to generate descriptions similar to real bug descriptions. ) In CGAN, the input of the generator G includes not only a random noise vector, but also a conditional vector to guide the generation process. The goal of the generator is to generate samples that match the given conditions. The goal of the discriminator D is to distinguish between generated samples and real samples as accurately as possible.

[0058] At the same time, the improved adversarial network WGAN introduces the Wasserstein distance as the loss function of the discriminator to replace the cross entropy loss function in the original GAN. The Wasserstein distance can measure the distance between the generated sample and the real sample through the real value output by the discriminator network. The improved loss function formula is as follows:

[0059] For the loss function of generator G: L(G) = -E[D(G(z))]

[0060] For the loss function of the discriminator D: L(D) = E[D(G(z))] - E[D(x)]

[0061] Among them, G represents the generator, D represents the discriminator, z is the input noise of the generator, and x is the real sample.

[0062] Innovative changes are made on the above basis. Wasserstein GAN (WGAN) is a generative adversarial network based on Wasserstein distance, and its loss function can be innovatively changed on the basis of the existing formula to adapt to specific tasks or improve performance.

[0063] This application introduces gradient penalty to replace weight clipping to better constrain the gradient of the discriminator. This improvement is called Wasserstein GAN with GradientPenalty (WGAN-GP).

[0064] The loss function formula of WGAN-GP is as follows:

[0065] For the loss function of generator G: L(G) = -E[D(G(z))]

[0066] For the loss function of the discriminator D:

[0067] Among them, G represents the generator, D represents the discriminator, z is the input noise of the generator, and x is the real sample. is the linear interpolation between the real sample and the generated sample, and λ is the weight of the gradient penalty. Indicates that the discriminator is The norm of the gradient at .

[0068] WGAN-GP constrains the gradient of the discriminator through the gradient penalty term to keep it within a reasonable range. This improvement can improve training stability and the quality of generated samples.

[0069] The goal of the generator is to minimize the probability that the discriminator cannot correctly distinguish between generated samples and real samples, while the goal of the discriminator is to maximize this probability.

[0070] 3. Automatically capture bug information: Use AI models to automatically capture bug information in text data. The model will understand and analyze the text and extract key information about the bug, such as bug description, reproduction steps, and environment configuration.

[0071] 4. Bug classification and filing: Based on the captured bug information, AI models are used to classify and file bugs. Bugs can be classified according to indicators such as severity, impact range, and module, so as to better prioritize and assign them.

[0072] 5. Automatically submit bugs: Based on the captured bug information and classification results, use the API to submit the bug information to the bug tracking system or task management tool. Make sure that the submitted bug information contains enough detailed descriptions and related attachments so that developers can understand and reproduce the bug.

[0073] 6. Feedback and verification: After submitting a bug, make necessary adjustments and optimizations based on the feedback from the bug tracking system or the verification results from the developer. The accuracy of the model and the effect of extracting bug information can be improved based on the feedback information.

[0074] Therefore, this application achieves automatic identification of program bugs and submission of programs by constructing a bug identification model and a bug description generation module, saving the time of submitting bugs, reducing the situation of inaccurate manual descriptions, and reducing the communication costs between development and testing.

[0075] Exemplary Devices

[0076] Figure 2 FIG. 1 is a schematic diagram of a structure of an automatic submission device for a program bug provided by an exemplary embodiment of the present invention. Figure 2 As shown, the device 200 includes:

[0077] A collection module 210, used to collect historical data sets containing Bug information;

[0078] A first generating module 220, configured to train a pre-built bug recognition network architecture according to the historical data set to generate a bug recognition model;

[0079] A second generation module 230 is used to train a generative adversarial network using the historical data set to generate a bug description generation model;

[0080] A first acquisition module 240 is used to perform bug recognition on a program to be recognized using the generated bug recognition model, and obtain bug information of the program to be recognized;

[0081] A second acquisition module 250 is used to describe the bug information using the bug description generation model to acquire a bug description of the bug information;

[0082] The submission module 260 is used to automatically submit the bug information and the bug description of the bug information to a preset system using a preset program interface.

[0083] Optionally, the submission module 260 includes:

[0084] A classification submodule, used to classify the Bug information using a pre-trained classification model, and determine the priority of each Bug information according to the classification result of each Bug information;

[0085] The submission submodule is used to automatically submit the bug information and the bug description of the bug information to the preset system according to the priority by using the preset program interface.

[0086] Optionally, it also includes: an optimization module, which is used to adjust and optimize the classification model, the bug identification model and the bug description generation model according to the bug verification feedback result of the preset system.

[0087] Optionally, the generative adversarial network includes a generator and a discriminator, wherein the goal of the generator is to minimize the probability that the discriminator cannot correctly distinguish between the generated samples and the real samples, and the goal of the discriminator is to maximize the probability of correctly distinguishing between the generated samples and the real samples, wherein

[0088] The loss function of generator G is: L(G) = -E[D(G(z))]

[0089] The loss function of the discriminator D is:

[0090] Among them, z is the input noise of the generator, x is the real sample, is the linear interpolation between the real sample and the generated sample, and λ is the weight of the gradient penalty. Represents the norm of the discriminator's gradient at z.

[0091] Exemplary Electronic Devices

[0092] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 3 As shown, the electronic device 30 includes one or more processors 31 and a memory 32 .

[0093] The processor 31 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0094] The memory 32 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 33 and an output device 34, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0095] In addition, the input device 33 may also include, for example, a keyboard, a mouse, and the like.

[0096] The output device 34 can output various information to the outside. The output device 34 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0097] Of course, to simplify, Figure 3 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application conditions, the electronic device may also include any other appropriate components.

[0098] Exemplary computer program products and computer-readable storage media

[0099] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0100] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0101] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method for information mining of historical change records according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0102] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0103] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0104] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0105] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0106] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0107] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0108] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for automatically submitting program bugs, characterized in that: include: Collect historical data sets containing bug information; Training a pre-built bug recognition network architecture according to the historical data set to generate a bug recognition model; Using the historical data set to train a generative adversarial network to generate a bug description generation model; Using the generated bug identification model to perform bug identification on the program to be identified, and obtaining bug information of the program to be identified; Describing the bug information using the bug description generation model to obtain a bug description of the bug information; The bug information and the bug description of the bug information are automatically submitted to a preset system using a preset program interface.

2. The method according to claim 1, characterized in that Automatically submitting the bug information and the bug description of the bug information to a preset system using a preset program interface includes: Classify the bug information using a pre-trained classification model, and determine the priority of each bug information according to the classification result of each bug information; The bug information and the bug description of the bug information are automatically submitted to the preset system according to the priority using the preset program interface.

3. The method according to claim 2, characterized in that Also includes: The classification model, the bug identification model and the bug description generation model are adjusted and optimized according to the bug verification feedback results of the preset system.

4. The method according to claim 1, characterized in that: The generative adversarial network includes a generator and a discriminator. The goal of the generator is to minimize the probability that the discriminator cannot correctly distinguish between generated samples and real samples, while the goal of the discriminator is to maximize the probability of correctly distinguishing between generated samples and real samples. The loss function of the generator G is: L(G) = -E[D(G(z))] The loss function of the discriminator D is: Among them, z is the input noise of the generator, x is the real sample, is the linear interpolation between the real sample and the generated sample, and λ is the weight of the gradient penalty. Indicates that the discriminator is The norm of the gradient at .

5. A device for automatically submitting program bugs, characterized in that: include: The collection module is used to collect historical data sets containing bug information; A first generation module, used to train a pre-built bug recognition network architecture according to the historical data set to generate a bug recognition model; A second generation module is used to train a generative adversarial network using the historical data set to generate a bug description generation model; A first acquisition module, used to use the generated bug recognition model to perform bug recognition on the program to be recognized, and obtain bug information of the program to be recognized; A second acquisition module is used to describe the bug information using the bug description generation model to acquire a bug description of the bug information; The submission module is used to automatically submit the Bug information and the Bug description of the Bug information to a preset system using a preset program interface.

6. The device according to claim 5, characterized in that Submission modules, including: A classification submodule, used to classify the Bug information using a pre-trained classification model, and determine the priority of each Bug information according to the classification result of each Bug information; The submission submodule is used to automatically submit the bug information and the bug description of the bug information to the preset system according to the priority by using the preset program interface.

7. The device according to claim 6, characterized in that Also includes: The optimization module is used to adjust and optimize the classification model, the bug identification model and the bug description generation model according to the bug verification feedback result of the preset system.

8. The device according to claim 5, characterized in that The generative adversarial network includes a generator and a discriminator. The goal of the generator is to minimize the probability that the discriminator cannot correctly distinguish between generated samples and real samples, while the goal of the discriminator is to maximize the probability of correctly distinguishing between generated samples and real samples. The loss function of the generator G is: L(G) = -E[D(G(z))] The loss function of the discriminator D is: Among them, z is the input noise of the generator, x is the real sample, is the linear interpolation between the real sample and the generated sample, and λ is the weight of the gradient penalty. Indicates that the discriminator is The norm of the gradient at .

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 4.

10. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 4.

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