Defect management method and device, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a defect management method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: capturing to-be-detected data of a target item at an item display end, and obtaining item information corresponding to the target item; building prompt information based on the project information and the to-be-detected data, and inputting the prompt information into a pre-trained defect analysis model to obtain defect management information; and generating a defect management report based on the defect management information, and sending the defect management report to the project management end. The to-be-detected content capture data and the corresponding project information are directly obtained from the project display end, dependence on a third-party tool is not needed, the installation cost and the safety risk are reduced, the analysis speed and accuracy are improved, the requirement for manual intervention is reduced, efficient and accurate defect recording and tracking are achieved, the whole defect processing flow is optimized, and the defect processing efficiency is improved. And the team cooperation efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a defect management method, device, electronic device, storage medium and program product. Background Art

[0002] Defect management is crucial in software development. Defect management teams often use screenshots to capture system status or problem scenarios to help developers quickly find, understand, and reproduce errors.

[0003] In related technologies, defect management often relies on installing third-party screenshot software, which increases costs and may introduce security risks. In addition, manual analysis of these images and video clips is time-consuming and error-prone, making it difficult to meet the needs of rapid iteration. The lack of intelligent support also leads to slow processing, affecting overall work efficiency and accuracy.

[0004] Therefore, a more efficient and secure defect management method is urgently needed. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a defect management method. One or more embodiments of the present invention also relate to a defect management apparatus, a computing device, a computer-readable storage medium and a computer program product to solve the technical defects existing in the prior art.

[0006] According to a first aspect of an embodiment of the present invention, there is provided a defect management method, comprising: Capture the data to be detected of the target project on the project display end, and obtain the project information corresponding to the target project; Construct prompt information based on project information and data to be tested, input the prompt information into the pre-trained defect analysis model to obtain defect management information; Generate a defect management report based on the defect management information and send the defect management report to the project management end.

[0007] According to a second aspect of an embodiment of the present invention, there is provided a defect management apparatus, comprising: A capture module is configured to capture the data to be detected of the target project at the project display terminal and obtain project information corresponding to the target project; An analysis module is configured to construct prompt information based on project information and data to be detected, and input the prompt information into a pre-trained defect analysis model to obtain defect management information; The generation module is configured to generate a defect management report based on the defect management information and send the defect management report to the project management end.

[0008] According to a third aspect of an embodiment of the present invention, there is provided a computing device, including: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions. When the computer programs / instructions are executed by the processor, the steps of the above-mentioned defect management method are implemented.

[0009] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program / instruction, and the computer program / instruction implements the steps of the above-mentioned defect management method when executed by a processor.

[0010] According to a fifth aspect of an embodiment of the present invention, there is provided a computer program product, comprising a computer program / instruction, which implements the steps of the above-mentioned defect management method when executed by a processor.

[0011] An embodiment of the present invention realizes capturing the data to be detected of the target project at the project display end, and obtaining the project information corresponding to the target project; constructing prompt information based on the project information and the data to be detected, inputting the prompt information into a pre-trained defect analysis model, and obtaining defect management information; generating a defect management report based on the defect management information, and sending the defect management report to the project management end. The content capture data to be detected and the corresponding project information are directly obtained from the project display end without relying on third-party tools, reducing installation costs and security risks, and using a pre-trained defect analysis model to automatically analyze the prompt information composed of the content capture data and project information to generate detailed defect management information, thereby improving the speed and accuracy of the analysis and reducing the need for manual intervention. Finally, a report is automatically generated based on the defect management information output by the model and sent to the project management end, achieving efficient and accurate defect recording and tracking, optimizing the entire defect handling process, and improving team collaboration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is an interactive schematic diagram of a defect management system provided by an embodiment of the present invention; Figure 2 is a flow chart of a defect management method provided by an embodiment of the present invention; Figure 3 is a process flow chart of a defect management method provided by an embodiment of the present invention; Figure 4 is a structural schematic diagram of a defect management device provided by an embodiment of the present invention; Figure 5 It is a structural block diagram of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention, so the present invention is not limited to the specific implementation disclosed below.

[0014] The terms used in one or more embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of the present invention. The singular forms of "a", "said" and "the" used in one or more embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of the present invention refers to and includes any or all possible combinations of one or more associated listed items.

[0015] It should be understood that, although the terms first, second, etc. may be used to describe various information in one or more embodiments of the present invention, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of the present invention, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0016] In addition, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0017] Throughout the project life cycle, from planning, design, development, testing to deployment and maintenance, defects are ubiquitous and inevitable. They are key challenges that project managers and technical teams must face, because defects not only affect product quality and user experience, but may also lead to increased costs and schedule delays. Effective defect management can ensure that problems are discovered, recorded, analyzed and resolved in a timely manner, thereby improving the quality and stability of the software. Especially in an agile development environment, rapid iteration and continuous delivery require a quick response to defects to ensure that each version meets the expected functional requirements and provides a good user experience.

[0018] Current defect management tools usually use a combination of screenshots and text to describe defects, which makes the defect content more intuitive and easy to understand. In particular, capturing the scene where the defect occurs through screenshots and videos can more clearly show the specific situation of the problem, which helps to speed up understanding and processing. However, testers often need to go through a series of complex steps when submitting defects, including logging into the platform, creating a new report, filling in detailed information such as the person in charge, attaching relevant attachments, specifying the affected version, etc. Although these processes help ensure the completeness and accuracy of defect reports, frequent repetitive operations also increase the workload.

[0019] Although existing defect management tools provide a relatively intuitive method for submitting defects, there are still many inconveniences in actual application. First, relying on third-party screenshot or recording tools for defect capture increases the complexity of use. Users need to download and install these tools separately, which not only increases the learning cost, but also may introduce security risks. Secondly, due to the compatibility and functional differences between different tools, users encounter more obstacles during use. Finally, for enterprises, purchasing and maintaining these third-party tools will also bring additional costs. Therefore, when choosing a defect management tool, convenience and security become two important considerations. Defect management tools that directly integrate screenshot and video functions can significantly improve work efficiency and reduce unnecessary complexity and potential safety hazards.

[0020] In view of the above problems, a defect management method is provided in the present invention. The present invention also relates to a defect management system, a defect management apparatus, a computing device, a computer-readable storage medium, and a computer program product, which are described in detail one by one in the following embodiments.

[0021] See also Figure 1 , Figure 1 It is an interactive schematic diagram of a defect management system provided by an embodiment of the present invention. The defect management system includes a project display end, a service end and a project management end. The project display end is used to display the project content of the target project; the service end is used to capture the data to be detected of the target project at the project display end, and obtain the project information corresponding to the target project, construct prompt information based on the project information and the data to be detected, input the prompt information into a pre-trained defect analysis model, obtain defect management information, generate a defect management report based on the defect management information, and send the defect management report to the project management end; the project management end is used to post-process the defect content based on the defect management report.

[0022] Applied to the defect management system, an efficient and secure defect management process is achieved through the collaborative work of the project display end, the server end, and the project management end. First, the project display end directly captures the target project's to-be-detected content (such as screenshots, screen recordings) and its corresponding project information from the user interface without relying on any third-party tools, thereby reducing installation costs and potential security risks. Then, these content capture data and project information are transmitted to the server end, which automatically analyzes the prompt information using a pre-trained defect analysis model to generate detailed defect management information. This process not only improves the speed and accuracy of the analysis, but also greatly reduces the need for manual intervention. Finally, the server end automatically generates a defect report based on the defect management information output by the model and sends it to the project management end, which is responsible for subsequent processing and tracking of the defect content. Through this three-end linkage approach, the system ensures the efficiency and accuracy of defect recording and tracking, optimizes the entire defect handling process, and significantly improves the efficiency of team collaboration.

[0023] See also Figure 2 , Figure 2 It is a flow chart of a defect management method provided by an embodiment of the present invention, which specifically includes the following steps.

[0024] Step 202: Capture the data to be detected of the target project at the project display terminal, and obtain the project information corresponding to the target project.

[0025] The data to be detected includes screenshots or videos recorded by users on the project display terminal, and the project information refers to background information related to the current project, such as project name, browser version information, module information, etc.

[0026] In actual application, the system allows users to trigger the capture operation through shortcut keys (such as Ctrl + Shift + S for screenshots or Ctrl + Shift + R for screen recording) or buttons on the interface. Once triggered, the system immediately enters the capture mode and captures the target project's data to be detected on the project display end. For the screenshot operation, the system displays a semi-transparent overlay and changes the mouse cursor to a cross shape, so that users can accurately select the screen area to be captured; after the user drags the mouse to define a rectangular area, the system captures the selected part according to these coordinates and generates image data. For the screen recording operation, the system provides a control panel that allows users to pause, stop recording, etc., and uses the browser's MediaRecorder API to capture the screen video stream to ensure that the recording process is smooth and interference-free. At the same time, the system automatically obtains and records environmental information such as the window title, browser URL, and operating system version during capture, and inserts it into the report as additional background information. This information not only helps to understand the context of the problem, but also provides key clues for subsequent problem analysis.

[0027] For the step "Get project information corresponding to the target project", one implementation is that the system automatically extracts relevant information from the current active window or browser tab, such as the page title and URL. Another implementation is that the system can read project metadata from integrated project management tools (such as Jira, Trello) to ensure that the information obtained is accurate and comprehensive. In addition, the system can also automatically record details such as the captured timestamp, operating system version, and browser version to ensure that each captured data point is supported by detailed environmental information.

[0028] In a specific embodiment of the present invention, it is assumed that a developer discovers an interface layout problem when testing a web application. In order to accurately record this defect, the developer presses the shortcut keys Ctrl + Shift + S to enter the screenshot mode and selects the page area where the problem occurs to take a screenshot. At the same time, the system automatically obtains the title and browser URL of the current window, as well as information such as the operating system and browser version used by the developer. Later, when the developer encounters an interaction problem during the continued testing process, he uses the shortcut keys Ctrl + Shift + R again to start recording the screen to show the specific process of the problem. After the recording is completed, the system also records the timestamp and environmental information of the recording. All captured content and its associated information are organized into a structured format, ready for subsequent defect report generation.

[0029] In another specific embodiment of the present invention, a developer discovers an interface layout problem while testing a web application. In order to record this defect more accurately, the developer presses the shortcut keys Ctrl + Shift + S to enter the screenshot mode and selects the area of ​​the page where the problem occurs to take a screenshot. At the same time, the system not only automatically obtains information such as the title of the current window, browser URL, operating system, and browser version, but also provides powerful annotation and marking tools. The developer can load an image editor, add arrows, text boxes, or use drawing tools to annotate on the image to accurately identify the problem. After editing is completed, the image with detailed annotations will be saved and ready for submission, ensuring that all captured content and its associated information are organized into a structured format, providing detailed support for subsequent defect report generation.

[0030] In another specific embodiment of the present invention, when the developer encounters an interaction problem during the continued testing process, the shortcut keys Ctrl + Shift + R are used again to start recording the screen to show the specific process of the problem. After the recording is completed, the system also records the timestamp and environmental information during the recording, and provides video recording and editing functions. The user can fully record the problem by starting the recording, managing the recording process using the control panel, and capturing the video stream through the MediaRecorder API. In addition, the system supports automatic attachment of recorded videos to defect reports, including uploading videos to the back-end server, storing videos in the cloud service and returning the URL, and finally inserting a video link in the report for viewing. For situations where further editing is required, the user can also load a video editor to perform basic editing operations such as cropping and extracting clips of the video, and save the editing results to replace the original video to ensure that the final submitted material is the most optimized version.

[0031] In another specific embodiment of the present invention, in order to improve the efficiency of multiple problem reports, the system also supports multiple consecutive screenshots and submitting multiple defect reports at one time. Developers can continuously capture multiple screen areas, and each screenshot is automatically saved. After completing all screenshots, the system will automatically generate a corresponding defect report based on each screenshot, reducing the workload of manual creation. Finally, the system submits the generated defect reports one by one to ensure that each problem can receive timely attention and processing, greatly improving the efficiency and accuracy of multiple problem reports.

[0032] When developers are working on a complex multi-module project, the system can extract detailed information about the project, such as project name, version number, and module, from the integrated project management tool. This way, even when switching between multiple projects, the system can accurately match the captured data with the correct project information, ensuring that each defect report contains complete contextual information.

[0033] Furthermore, capturing the data to be detected of the target project at the project display end includes: determining the starting position and the ending position of the screenshot in response to the screenshot operation of the front-end user at the project display end; capturing the image to be detected at the project display end based on the starting position and the ending position of the screenshot; determining the recording start time and the recording end time in response to the recording operation of the front-end user at the project display end; capturing the video to be detected at the project display end based on the recording start time and the recording end time, and using the image to be detected and the video to be detected as the data to be detected.

[0034] The screenshot start position and screenshot end position define the boundaries of the screen area selected by the user; the recording start time and recording end time identify the time range of the video recording. This information is used to ensure that the system can accurately capture the content specified by the user.

[0035] In actual applications, when the front-end user triggers the screenshot operation on the project display end, the system will respond to the user's shortcut key (such as Ctrl + Shift + S) or click the button on the interface to enter the screenshot mode. At this time, the system will display a semi-transparent overlay on the screen and change the mouse cursor to a cross shape, prompting the user to start selecting the screen area to be captured. The user drags the mouse to determine the starting position and the ending position of the screenshot, and the system accurately captures the image in the area as the image to be detected based on these two coordinate points. For the recording operation, the user can also start the screen recording function through shortcut keys (such as Ctrl + Shift + R) or buttons. The system records the moment when the user presses the shortcut key as the recording start time until the user presses the shortcut key again or clicks the stop button. All screen activities during this period will be recorded to form a video to be detected. During this process, the system uses the browser's MediaRecorderAPI to capture the screen video stream to ensure that the recording process is smooth and interference-free. Finally, the system organizes the captured images and videos to be detected into structured data to be detected, ready for subsequent analysis.

[0036] In a specific embodiment of the present invention, it is assumed that a developer finds an interface layout problem when testing a web application. In order to accurately record this defect, the developer presses the shortcut keys Ctrl + Shift + S to enter the screenshot mode and selects the page area where the problem occurs to take a screenshot. The system responds immediately, displays a semi-transparent overlay on the screen and changes the mouse cursor to a cross shape, which facilitates the developer to accurately select the screen area to be captured. After the developer drags the mouse to define the rectangular area, the system successfully captures the image in the area according to the selected screenshot start position and screenshot end position. Subsequently, when the developer encounters an interaction problem during the continued testing process, the shortcut keys Ctrl + Shift + R are used to start recording the screen to show the specific process of the problem. The system records the moment when the shortcut key is pressed as the recording start time, and continues recording until the developer presses the shortcut key again. During the entire recording period, the system uses the MediaRecorder API to capture the screen video stream to ensure that the recording process proceeds smoothly. Finally, the system organizes the captured images and videos into data to be tested, ready for generating a detailed defect report.

[0037] Based on this, the system eliminates the reliance on third-party tools through integrated screenshot and screen recording functions, reducing installation costs and security risks. This design simplifies the user's operation steps and makes the process of capturing content more intuitive and convenient.

[0038] Furthermore, the project information corresponding to the target project is obtained, including: obtaining the current window information of the project display terminal and determining the capture timestamp of the data to be detected; obtaining and parsing the metadata of the target project to obtain the project environment information; integrating the capture timestamp and the project environment information into the project information.

[0039] Project information refers to background information related to the target project, including current window information, capture timestamp, and project environment information. Current window information refers to the interface status when the user operates on the project display terminal, such as page title and URL; capture timestamp records the specific time of screenshot or recording; project environment information covers the metadata of the project, such as project name, version number, module information, etc.

[0040] In actual application, the system first obtains the current window information of the project display end and determines the capture timestamp of the data to be detected. When the user triggers the screenshot or screen recording operation, the system automatically reads the status of the current active window, including the window title and browser URL, which can help accurately describe the context of the problem. At the same time, the system records the exact time of the user operation as the capture timestamp to ensure that each captured data point has a clear time identifier. Next, the system obtains and parses the metadata of the target project to obtain the project environment information. This step can be achieved by querying the integrated project management tools (such as Jira, Trello) to extract key information such as the project name, version number, and module. For the step "obtaining and parsing the metadata of the target project", one implementation method is that the system directly obtains the latest updated metadata from the API interface of the project management tool; another implementation method is that the system regularly synchronizes the data in the project management tool to ensure that the locally stored metadata is the latest. Finally, the system integrates the capture timestamp and project environment information into complete project information. This integration not only ensures that each captured data point can be associated with a specific project background, but also provides rich reference materials for subsequent problem analysis.

[0041] In a specific embodiment of the present invention, it is assumed that a developer discovers an interface layout problem while testing a web application. The developer uses a shortcut key to trigger the screenshot operation and selects the area of ​​the page where the problem occurs to take a screenshot. At the same time, the system automatically obtains the title of the current window (e.g., "Login Page - WebApp v2.0") and the browser URL (e.g., "https: / / example.com / login"), as well as the exact timestamp of the operation. Subsequently, the system obtains the metadata of the web application through the integrated project management tool API, including the project name ("WebApp"), version number ("v2.0"), and the module to which it belongs ("User Authentication"). All of this information is integrated into detailed project information and attached to the generated defect report. Developers can also view and confirm this information through the interface provided by the system to ensure accuracy. Once confirmed, the system automatically generates a defect report containing complete project information and sends it to the project management end for further processing by team members.

[0042] Based on this, the system acquires and integrates project information through automated processes, reducing the need for manual intervention and ensuring that each captured data point has detailed context support. The combination of capture timestamps and project environment information makes defect reports more detailed and reliable, helping to quickly locate and resolve problems.

[0043] Step 204: construct prompt information based on the project information and the data to be detected, input the prompt information into the pre-trained defect analysis model, and obtain defect management information.

[0044] In actual application, the system first obtains the data to be detected of the target project and its corresponding project information. For the step construction prompt information, one implementation method is that the system automatically extracts key features from the captured data, such as edges in the image, color distribution, or frame-to-frame differences in the video, and combines project information such as project name, version number, etc. to form structured prompt information. Another implementation method is that the system uses natural language processing technology to parse the text description and extract keywords and phrases as part of the prompt information. Subsequently, the system inputs the constructed prompt information into a pre-trained defect analysis model. The model can be a deep learning model that has been trained with a large amount of historical defect data and can identify and classify different types of defects. When the prompt information is input into the model, the model analyzes it and outputs defect management information, including but not limited to defect type, severity, possible impact range, etc. The system uses this information to automatically generate a detailed defect report to ensure that each defect is recorded in a timely and accurate manner.

[0045] In a specific embodiment of the present invention, suppose a developer discovers an interface layout problem in a web application. The developer uses a shortcut key to trigger the screenshot operation and selects the area of ​​the page where the problem occurs to take a screenshot. At the same time, the system automatically obtains the current window title, browser URL, and environment information such as the operating system and browser version. Next, the system combines this information with the captured screenshot. Figure 1 The system then organizes the information into prompts. The prompts not only include visual features in the screenshot, such as abnormal button positions or text overlap, but also background information such as project name and version number. The system inputs this prompt information into a pre-trained defect analysis model, which has been trained on a large number of similar cases and can quickly identify that this is a UI layout problem and assess its scope and severity. Finally, the system generates a detailed defect report based on the defect management information output by the model, including a description of the problem, recommended repair solutions, etc., to help team members understand and solve the problem more quickly.

[0046] Furthermore, prompt information is constructed based on the project information and the data to be detected, including: inputting the data to be detected and the project information into a defect classification model to predict classification label information; adding the classification label information to the data to be detected to obtain labeled data to be detected, and constructing prompt information based on the labeled data to be detected.

[0047] Among them, the prompt information is a structured input generated based on project information and classification labels, which is used to guide subsequent defect analysis.

[0048] In actual applications, the system first inputs the data to be tested and the project information into a pre-trained defect classification model. The model predicts and outputs classification label information based on the input data. These labels can identify the defect type (such as UI problems, functional failures, etc.) and its severity. Next, the system adds the predicted classification label information to the original data to be tested to form labeled data to be tested. Based on these labeled data, the system constructs detailed prompt information to ensure that each defect report contains an accurate problem description and key background information. This process not only improves the accuracy of defect identification, but also simplifies the user's operation steps and reduces the need for manual intervention.

[0049] In a specific embodiment of the present invention, suppose a developer discovers an interface layout problem in a web application and captures relevant evidence through the screenshot function of the system. The system automatically obtains environmental information such as the title of the current window, the browser URL, and the operating system version as project information. Then, the system combines this information with the captured screenshot. Figure 1The system then inputs the problem into the pre-trained defect classification model, which predicts that this is a "UI layout problem" and assesses it as "medium severity". The system adds these classification labels to the original screenshot to form annotated data to be tested. Based on this, the system constructs detailed prompt information, including problem description, recommended repair solutions, etc., to help team members understand and solve the problem faster.

[0050] Based on this, the system processes the data to be tested and project information in an intelligent and automated manner, significantly improving the efficiency and accuracy of defect management. The pre-trained defect classification model can quickly and accurately predict the classification label, ensuring that each defect can be recorded in a timely and appropriate manner.

[0051] Furthermore, after the data to be detected and the project information are input into the defect classification model and the classification label information is predicted, it also includes: sending the classification label information to the front-end user; and updating the classification label information in response to the front-end user's update instruction for the classification label information.

[0052] The classification label information is the label predicted by the defect classification model, which identifies the type and severity of the defect. The front-end user refers to the user who operates on the project display end, such as a developer or tester. The update instruction is the user's operation to modify or confirm the initial classification label information.

[0053] In actual applications, after the data to be detected and the project information are input into the defect classification model and the classification label information is predicted, the system will immediately send the classification label information to the front-end user. The user can view the preliminary classification results on the interface and decide whether to adjust them according to the actual situation. If the user believes that the initial classification is inaccurate or needs additional information, he can issue an update instruction, such as by clicking the edit button or selecting different label options. The system responds to the user's update instructions and updates the classification label information in real time to ensure that the final classification results can accurately reflect the nature of the problem. This two-way interactive mechanism not only improves the accuracy of classification, but also enhances the user's sense of participation and trust. For the step "responding to the front-end user's update instructions for classification label information", one implementation method provides users with an intuitive interface that allows direct modification of label content; another implementation method provides the system with predefined label options, and users only need to select the most appropriate label to complete the update.

[0054] In a specific embodiment of the present invention, suppose a developer discovers an interface layout problem in a web application and captures relevant evidence through the screenshot function of the system. The system automatically obtains environmental information such as the title of the current window, the browser URL, and the operating system version as project information. Then, the system combines this information with the captured screenshot. Figure 1The system inputs the information into the pre-trained defect classification model, and the model predicts that this is a "UI layout problem" and evaluates it as "medium severity". The system immediately sends these classification label information to the developer. After reviewing it, the developer believes that the problem is actually a "high severity" problem, so he issues an update instruction through the interface to select a more appropriate label. In response to this instruction, the system updates the classification label information to "high severity UI layout problem". Based on the updated classification label, the system further constructs detailed prompt information to help team members understand and solve the problem faster.

[0055] Based on this, the system ensures the accuracy and applicability of classification label information through interaction with front-end users. This design not only improves the overall efficiency of defect management, but also enhances user participation and trust. The real-time update mechanism enables the system to respond quickly to user feedback, ensuring that each defect is properly and promptly recorded. The system can efficiently generate high-quality defect reports.

[0056] Step 206: Generate a defect management report based on the defect management information, and send the defect management report to the project management end.

[0057] Defect management information is the key results output from the pre-trained defect analysis model, including but not limited to defect type, severity, impact, etc. Defect management reports are detailed documents generated based on this information, used to record and describe the specific circumstances of the defects. The project management end refers to the system or platform responsible for receiving and processing defect reports, usually used by project managers and technical teams.

[0058] In actual application, the system automatically generates a defect management report based on the defect management information obtained from the defect analysis model. First, the system organizes all relevant data to ensure that the report contains complete background information, such as the captured timestamp, operating system version, browser version, and key visual evidence in screenshots or videos. Next, the system uses the smart fill function to automatically recommend appropriate tags, priorities, and other relevant information based on historical data and contextual information, and pre-fills these contents into the report form. For the step "Generate Defect Management Report", one implementation method is to provide the system with a rich selection of templates, and users can choose the template that best meets their needs for customized filling according to actual conditions; another implementation method is that the system fully automates the report generation without user intervention, and directly generates the final report based on the information output by the model. The generated report automatically attaches relevant screenshots, videos, and their cloud storage URLs for easy viewing and sharing. Finally, the system sends the defect management report to the project management end through a secure communication protocol to ensure that the report can reach the relevant personnel in a timely and accurate manner. The whole process not only simplifies the user's operation steps, but also improves the consistency and professionalism of the report.

[0059] In a specific embodiment of the present invention, it is assumed that a developer discovers an interface layout problem in a web application and captures relevant evidence through the screenshot function of the system. The system constructs prompt information based on the captured data and environmental information, inputs it into the pre-trained defect analysis model, and obtains detailed defect management information, including the type, severity and possible impact of the problem. Next, the system automatically generates a defect management report based on this information. The report contains a clear description of the problem, recommended repair solutions, and related screenshots and video links. In addition, the system also recommends appropriate tags and priorities based on historical data, and pre-fills them in the report form. The developer only needs to review and confirm that it is correct, and the system will automatically send this detailed defect report to the project management end. Project managers and technical teams can immediately access the report to understand the details of the problem and arrange corresponding repair work.

[0060] Furthermore, a defect management report is generated based on the defect management information, including: obtaining an initial defect report corresponding to the target project, wherein the initial defect management report includes at least two report fields; for the target field, obtaining target defect information corresponding to the target field in the defect management information, wherein the target field is any one of the at least two report fields; writing the target defect information into the field position of the target field in the initial defect report to obtain a defect management report.

[0061] Among them, defect management information is the key results output from the pre-trained defect analysis model, including but not limited to defect type, severity, impact scope, etc. The initial defect report is a template pre-set by the system, which contains at least two report fields for filling in defect details. The target field refers to any one of these report fields, and the target defect information is the specific content corresponding to the target field extracted from the defect management information.

[0062] In actual application, the system first obtains the initial defect report corresponding to the target project. This initial report is a structured template that contains multiple fields for filling in defect details, such as problem description, discovery time, priority, etc. Next, for each target field, the system searches for the corresponding target defect information in the defect management information. For example, if the target field is "problem description", the system extracts a detailed defect description from the defect management information; if it is the "priority" field, the corresponding priority evaluation is extracted. For the step "obtaining the target defect information corresponding to the target field", one implementation method is that the system automatically matches the relevant content in the defect management information according to the predefined mapping rules; another implementation method is that the system provides an intelligent recommendation function to recommend the most appropriate filling content based on historical data and context information. Finally, the system writes the extracted target defect information to the location of the corresponding field in the initial defect report, thereby generating a complete defect management report. This automated process not only improves the efficiency of report generation, but also ensures the consistency and accuracy of the report.

[0063] In a specific embodiment of the present invention, it is assumed that a developer discovered an interface layout problem in a web application and captured relevant evidence through the screenshot function of the system. The system automatically generates an initial defect report, which contains multiple fields, such as "problem description", "discovery time", "priority", etc. Based on the defect management information obtained from the defect analysis model, the system writes a detailed defect description in the "problem description" field: "The login button is not displayed correctly on small-screen devices." In the "discovery time" field, the system writes the current timestamp. For the "priority" field, the system fills in "high priority" based on the severity assessment of the model output. Finally, the system accurately writes all target defect information into the corresponding field locations and generates a detailed defect management report. The developer can immediately review and submit this report for further processing by team members.

[0064] Based on this, the system generates defect management reports in an intelligent and automated manner, reducing the need for manual intervention and ensuring the consistency and professionalism of the reports. The preset templates and intelligent recommendation functions further improve work efficiency, allowing each defect to be recorded promptly and accurately. At the same time, the automated filling mechanism ensures that defect reports can be quickly communicated to relevant personnel, promoting the improvement of team collaboration efficiency. The system can efficiently generate high-quality defect reports, optimize the entire defect handling process, ensure that problems can be quickly identified and resolved, and thus improve the overall quality and progress control of the project.

[0065] An embodiment of the present invention realizes capturing the data to be detected of the target project at the project display end, and obtaining the project information corresponding to the target project; constructing prompt information based on the project information and the data to be detected, inputting the prompt information into a pre-trained defect analysis model, and obtaining defect management information; generating a defect management report based on the defect management information, and sending the defect management report to the project management end. The content capture data to be detected and the corresponding project information are directly obtained from the project display end without relying on third-party tools, reducing installation costs and security risks, and using a pre-trained defect analysis model to automatically analyze the prompt information composed of the content capture data and project information to generate detailed defect management information, thereby improving the speed and accuracy of the analysis and reducing the need for manual intervention. Finally, a report is automatically generated based on the defect management information output by the model and sent to the project management end, achieving efficient and accurate defect recording and tracking, optimizing the entire defect handling process, and improving team collaboration efficiency.

[0066] The following combination Figure 3 , taking the application of the defect management method provided by the present invention in software development as an example, the defect management method is further described. Figure 3 It is a processing flow chart of a defect management method provided by an embodiment of the present invention, including a defect management stage and model training. The defect management stage specifically includes the following steps.

[0067] Step 302: The user triggers screenshot or screen recording.

[0068] Specifically, when users are performing defect management, they can quickly enter screenshot mode or start recording the screen by using shortcut keys (such as Ctrl + Shift + S for screenshots or Ctrl + Shift + R for screen recording) or clicking the corresponding button on the interface. Once triggered, the system will immediately respond and prepare to capture the user-selected screen area or the entire screen operation process. For screenshots, the system will display a translucent overlay and change the mouse cursor to a cross shape, allowing users to accurately select the area to capture; for screen recording, a control panel is provided to allow users to pause, stop recording, etc. This process ensures that users can efficiently collect key visual evidence for defect reporting, thereby providing a solid foundation for subsequent problem analysis and resolution.

[0069] Step 304: Automatically save and preview.

[0070] Specifically, after taking a screenshot or recording, the system will automatically upload the captured content to the backend server immediately. The backend processes the image or video data and stores it in the cloud service to ensure the security and accessibility of the data. The server then returns a unique cloud storage URL (Uniform Resource Locator), which will be used to reference the captured content in the defect report. At the same time, the system will display a preview of the captured content in the defect submission form, allowing users to check the quality and accuracy of the image or video before submitting it. This instant feedback mechanism not only improves the user experience, but also reduces the duplication of work caused by incorrect capture.

[0071] Step 306: Automatically fill in the defect management report.

[0072] Specifically, in order to simplify the user's operation process, the system will automatically generate a preliminary defect management report based on the captured content and related environmental information. First, the system will collect background information and historical data of the current project to obtain context. Next, the pre-trained machine learning model is called to analyze the captured content and automatically recommend appropriate tags, priorities, and other relevant information. These recommended information will be pre-filled in the relevant fields of the submission form, such as the issue title, description template, etc. In addition, the system will automatically extract and record environmental information such as the window title, browser URL, and operating system version at the time of capture, and insert it into the report as additional background information. In this way, users only need to confirm or fine-tune the recommended information, which greatly reduces the workload of manual input.

[0073] Step 308: Receive user input and supplement the defect management report.

[0074] Specifically, although the system provides intelligent recommendations and pre-filling functions, the final defect report still needs to be reviewed and supplemented by the user. Users can fill in additional descriptive information in the automatically generated form, such as detailed defect description, reproduction steps, etc. If necessary, users can also adjust the system-recommended tags, priorities, etc. to ensure the accuracy and completeness of the report. In addition, the system supports the creation and application of templates, allowing users to save commonly used configurations to quickly fill in forms in future submissions. At the same time, users can quickly perform screenshots, record videos, and submit reports by setting shortcut key combinations, further improving work efficiency.

[0075] Step 310: Send the defect management report to the project management end.

[0076] Specifically, after completing all necessary edits and confirmations, the user can submit a defect management report. At this point, the system will send the complete report to the project management end to ensure that other members of the team can view and process it in a timely manner. During the sending process, the system will verify whether all required fields of the report have been filled in correctly, and ensure that all attachments (such as screenshots, videos) have been successfully uploaded. Once the report is successfully sent, it will be marked as pending on the project management end, waiting for the relevant personnel to take the next step. The entire process is designed to ensure that every link from defect discovery to resolution can be carried out efficiently and accurately, thereby optimizing the entire defect handling process and improving team collaboration efficiency.

[0077] Through the above steps 302-310, the system optimizes the overall efficiency and quality of the defect reporting and repair process by integrating a series of intelligent and automated functions. The details are as follows: First, the software implements the quick submission and automatic saving functions. After completing the screenshot or video recording, the user can immediately fill in and submit the report form, which greatly simplifies the reporting process. This effectively reduces the steps of manual uploading and linking, which not only saves time costs, but also significantly reduces the possibility of operational errors. Users only need to trigger the shortcut key or click the button to start the screenshot or screen recording, and immediately enter the submission interface, ensuring that the captured content can be quickly converted into a formal defect report.

[0078] Secondly, the system excels in enhancing communication effectiveness. By integrating visual description and annotation tools, developers can understand and locate problems more intuitively. This intuitive communication method greatly improves the accuracy and efficiency of repairs, and effectively avoids the ambiguity and misunderstanding that may be caused by text descriptions. Users can directly add annotations such as arrows and text boxes on screenshots or videos to clearly point out the problem, making communication between team members more efficient and clear.

[0079] At the same time, the system also ensures the integrity of the data. It automatically captures and records key environmental information, such as operating system version and browser version, which is essential for reproducing and analyzing the problem. In addition, the software supports batch submission of multiple defects, which further improves the efficiency of processing. Whether submitting multiple screenshots or multiple video clips at one time, the system can properly manage and generate independent reports for each defect to ensure that nothing is missed.

[0080] In terms of quality control, the software introduces the smart fill function, which can intelligently recommend appropriate tags and priorities based on historical data and contextual information, thereby reducing the occurrence of human errors. The preset templates ensure the consistency and professionalism of the reports, further improving the standard of work quality. Intelligent recommendations based on machine learning models not only improve the accuracy of tags and classifications, but also help new employees get started faster and reduce training costs.

[0081] In terms of user experience, the system also performs well. It has a simple and easy-to-use interface and provides shortcut key support, allowing users to quickly get started and improve efficiency. In addition, the software also provides a wealth of customization options, allowing users to create exclusive templates and shortcut key settings according to personal needs, enhancing the personalization and convenience of the software. Whether you are a novice or an experienced user, you can find the working method that suits you best.

[0082] Finally, in terms of collaboration and tracking, the system helps teams better organize and prioritize defects through automatic classification and tag recommendation. The associated information extraction function provides comprehensive problem background information for team collaboration, helping teams collaborate and solve problems more efficiently. By automatically obtaining window titles, URLs, and system environment information, team members can reproduce problem situations more quickly, speed up the problem-solving process, and improve overall work efficiency.

[0083] The model training phase includes the following steps.

[0084] Step 312: Collect defect training data.

[0085] In order to build an effective defect analysis model, it is first necessary to obtain necessary data from historical defect reporting systems and project management tools (such as Jira, Trello, etc.). This data includes but is not limited to text descriptions, labels, classification fields, and related screenshots and videos. By identifying reliable data sources and extracting features from them, a solid foundation can be provided for subsequent model training. For image and video data, preprocessing is also required to extract image features or obtain video features using methods such as inter-frame differences. In addition, data cleaning is an indispensable part. Removing noise data and processing missing values ​​ensures the quality of the data, thereby improving the effectiveness and accuracy of model training.

[0086] Step 314: Data preprocessing.

[0087] After collecting the raw data, the next step is to preprocess the data in detail. This step covers manual or automatic data annotation, defining the labels and classification criteria required for prediction, and using specialized annotation tools to mark key areas in images and videos. In order to ensure the accuracy of the annotations, they are usually checked through manual review or cross-validation. At the same time, the entire data set is randomly sampled and divided into 70% for training, 20% for validation, and 10% for testing to ensure the diversity and representativeness of the data set and avoid overfitting. This process lays a good foundation for subsequent feature engineering and model training.

[0088] Step 316: Feature extraction.

[0089] After data preparation is completed, the feature extraction stage begins. The text description will go through processing steps such as word segmentation, stop word removal, and TF-IDF (Term Frequency-Inverse Document Frequency) calculation to better capture the core information of the text content. For screenshots, edge detection, shape recognition and other technologies are used, or convolutional neural networks (CNN) are used to extract high-dimensional features to enhance the model's ability to understand images. Video processing is more complicated, extracting motion features through methods such as frame differences and optical flow, or combining long short-term memory networks (LSTM) with CNN to process time series information. These operations not only improve the learning efficiency of the model, but also increase its generalization ability, ensuring that the model can work effectively in various environments.

[0090] Step 318: Model selection and construction.

[0091] Select a suitable machine learning or deep learning model according to the task objectives, and build a specific architecture. For classification problems, you can choose logistic regression, random forest or deep neural network; for image processing, you tend to use CNN architectures such as ResNet (Residual Network) and VGG (Visual Geometry Group Model). After building the model architecture using frameworks such as TensorFlow (a symbolic mathematical system) and PyTorch (an open source deep learning framework for machine learning and deep learning), you also need to adjust hyperparameters through methods such as grid search or Bayesian optimization to optimize model performance. In particular, in terms of algorithm selection, the system uses convolutional neural networks in deep learning, which significantly improves analysis accuracy due to its powerful feature extraction capabilities and good capture of visual patterns. In addition, the integrated adaptive learning mechanism enables the model to continuously learn and optimize its own performance from the increasing amount of new data, ensuring long-term effectiveness and accuracy.

[0092] Step 320: Model training.

[0093] Model training is the process of iteratively updating model parameters by using training set data. First, define a suitable loss function, such as cross entropy loss, and select optimization algorithms such as gradient descent to update parameters. During the training process, monitor indicators such as accuracy, precision, and recall in real time to prevent overfitting. In particular, when training deep learning models that involve a large number of matrix operations, the use of efficient parallel computing and hardware acceleration technologies (such as GPU acceleration) can significantly improve processing speed. At the same time, optimizing the data input pipeline and batch processing mechanism further speeds up the loading and processing of large batches of data, ensuring the efficiency of model training.

[0094] Step 322: Model verification and evaluation.

[0095] After training is completed, the validation set data is used to evaluate the model and confirm its generalization ability. By calculating evaluation indicators such as accuracy, F1 score, AUC (Area Under the Curve), analyzing the confusion matrix to understand the classification errors, and comparing the performance of the training set and validation set to determine whether there is overfitting. This step is to ensure that the model can not only perform well on the training data, but also maintain high-precision prediction capabilities on unseen data. Through rigorous verification and evaluation, potential problems can be discovered and corresponding improvement measures can be taken to improve the overall performance of the model.

[0096] Step 324: Deploy the trained model to the production environment.

[0097] Once the model has passed verification and evaluation, the next step is to deploy it to a production environment to provide actual application services. Export the model to formats such as ONNX (Open Neural Network Exchange) and SavedModel, and choose cloud deployment or edge device deployment. In order for the front-end system to call the model service, it is also necessary to use frameworks such as Flask (lightweight web application framework) and FastAPI (fast API framework) to build a RESTful API interface. The deployed model can be directly applied to the defect management process, helping users quickly generate intelligent recommendation information, simplifying the defect submission process, and improving overall work efficiency.

[0098] Step 326: Monitor model performance in real time and update the model regularly.

[0099] After deployment, a monitoring system is set up to track the model's predictive performance in real time, collect user feedback, and identify model defects and room for improvement. By periodically retraining the model and adding the latest data, the model performance is continuously optimized to ensure its long-term effectiveness and accuracy. This dynamic update process not only improves the model's adaptability, but also enables the system to quickly respond to changes in the environment and needs. In addition, with the help of intelligent data labeling and classification functions, many steps that originally required human participation are taken over by the automated management system, which greatly reduces the frequency and burden of manual operations and further enhances the practical value of the system.

[0100] Corresponding to the above method embodiment, the present invention also provides a defect management device embodiment, Figure 4 FIG. 2 shows a schematic diagram of a defect management device provided by an embodiment of the present invention. Figure 4 As shown, the device comprises: The capture module 402 is configured to capture the to-be-detected data of the target project at the project display terminal and obtain the project information corresponding to the target project; The analysis module 404 is configured to construct prompt information based on the project information and the data to be detected, and input the prompt information into a pre-trained defect analysis model to obtain defect management information; The generating module 406 is configured to generate a defect management report based on the defect management information and send the defect management report to the project management end.

[0101] Optionally, the capture module 402 is further configured to determine the screenshot starting position and the screenshot ending position in response to the screenshot operation of the front-end user at the project display end; capture the image to be detected at the project display end based on the screenshot starting position and the screenshot ending position; determine the recording start time and the recording end time in response to the recording operation of the front-end user at the project display end; capture the video to be detected at the project display end based on the recording start time and the recording end time, and use the image to be detected and the video to be detected as the data to be detected.

[0102] Optionally, the capture module 402 is further configured to obtain the current window information of the project display terminal and determine the capture timestamp of the data to be detected; obtain and parse the metadata of the target project to obtain the project environment information; and integrate the capture timestamp and the project environment information into project information.

[0103] Optionally, the analysis module 404 is further configured to input the data to be detected and the project information into the defect classification model to predict the classification label information; add the classification label information to the data to be detected to obtain the labeled data to be detected, and construct prompt information based on the labeled data to be detected.

[0104] Optionally, the analysis module 404 is further configured to send the classification label information to the front-end user; and update the classification label information in response to an update instruction of the front-end user for the classification label information.

[0105] Optionally, the generation module 406 is further configured to obtain an initial defect report corresponding to the target project, wherein the initial defect management report includes at least two report fields; for the target field, obtain target defect information corresponding to the target field in the defect management information, wherein the target field is any one of the at least two report fields; write the target defect information into the field position of the target field in the initial defect report to obtain the defect management report.

[0106] Applied to the defect management device, the capture module 402 allows users to easily obtain the required content without using additional tools, simplifying the operation steps; the analysis module 404 uses intelligent analysis capabilities to ensure the professionalism and efficiency of data processing; and the generation module 406 provides a clear defect management view, which facilitates the team to respond and solve problems in a timely manner. Through the collaborative work of these three modules, the device ensures that the defect management process is smoother, safer and more efficient.

[0107] The above is a schematic scheme of a defect management device of this embodiment. It should be noted that the technical scheme of the defect management device and the technical scheme of the defect management method described above are of the same concept, and the details not described in detail in the technical scheme of the defect management device can be found in the description of the technical scheme of the defect management method described above.

[0108] Figure 5 The block diagram of a computing device 500 provided according to an embodiment of the present invention is shown. The components of the computing device 500 include but are not limited to a memory 510 and a processor 520. The processor 520 is connected to the memory 510 via a bus 530, and the database 550 is used to store data.

[0109] The computing device 500 also includes an access device 540 that enables the computing device 500 to communicate via one or more networks 560. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 540 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that is wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0110] In one embodiment of the present invention, the above components of the computing device 500 and Figure 5 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 5 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of the present invention. Those skilled in the art may add or replace other components as needed.

[0111] The computing device 500 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 500 may also be a mobile or stationary server.

[0112] The processor 520 is used to execute the following computer program / instruction, which implements the steps of the above-mentioned defect management method when executed by the processor.

[0113] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computing device embodiment, since it is basically similar to the defect management method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the defect management method embodiment.

[0114] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program / instruction, which implements the steps of the above-mentioned defect management method when executed by a processor.

[0115] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer-readable storage medium embodiment, since it is basically similar to the defect management method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the defect management method embodiment.

[0116] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which implements the steps of the above-mentioned defect management method when executed by a processor.

[0117] The above is a schematic scheme of a computer program product of this embodiment. It should be noted that the technical scheme of the computer program product and the technical scheme of the defect management method described above are of the same concept, and the details not described in detail in the technical scheme of the computer program product can be found in the description of the technical scheme of the defect management method described above.

[0118] The above describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0120] It should be noted that, for the convenience of description, the aforementioned method embodiments are all described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the described action sequence, because according to the embodiments of the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of the present invention.

[0121] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The optional embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of the present invention. The present invention selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A defect management method, characterized in that: include: Capturing the data to be detected of the target project at the project display terminal, and obtaining the project information corresponding to the target project; Constructing prompt information based on the project information and the data to be detected, and inputting the prompt information into a pre-trained defect analysis model to obtain defect management information; A defect management report is generated based on the defect management information, and the defect management report is sent to the project management end.

2. The method according to claim 1, characterized in that The capturing of the to-be-detected data of the target item at the item display terminal includes: In response to a screenshot operation of a front-end user at a project display terminal, determining a screenshot start position and a screenshot end position; Capturing the image to be detected at the project display end based on the screenshot starting position and the screenshot ending position; In response to the recording operation of the front-end user at the project display end, determining a recording start time and a recording end time; The video to be detected is captured at the project display end based on the recording start time and the recording end time, and the image to be detected and the video to be detected are used as data to be detected.

3. The method according to claim 1, characterized in that: The obtaining of project information corresponding to the target project includes: Acquire the current window information of the project display terminal, and determine the capture timestamp of the data to be detected; Acquire and parse the metadata of the target project to obtain project environment information; The capture timestamp and the project environment information are integrated into project information.

4. The method according to claim 1, characterized in that: The constructing prompt information based on the project information and the data to be detected includes: Inputting the data to be detected and the project information into a defect classification model to predict and obtain classification label information; The classification label information is added to the data to be detected to obtain the labeled data to be detected, and prompt information is constructed based on the labeled data to be detected.

5. The method according to claim 4, characterized in that After the data to be detected and the project information are input into the defect classification model to predict the classification label information, the method further includes: Sending the classification label information to a front-end user; In response to an update instruction from the front-end user for the classification label information, the classification label information is updated.

6. The method according to claim 1, characterized in that The generating a defect management report based on the defect management information includes: Obtaining an initial defect report corresponding to the target project, wherein the initial defect management report includes at least two report fields; For a target field, obtaining target defect information corresponding to the target field in the defect management information, wherein the target field is any one of the at least two report fields; The target defect information is written into the field position of the target field in the initial defect report to obtain a defect management report.

7. A defect management device, characterized in that: include: A capture module is configured to capture the data to be detected of the target project at the project display terminal and obtain the project information corresponding to the target project; An analysis module is configured to construct prompt information based on the project information and the data to be detected, and input the prompt information into a pre-trained defect analysis model to obtain defect management information; The generating module is configured to generate a defect management report based on the defect management information and send the defect management report to the project management end.

8. A computing device, characterized in that include: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions. When the computer programs / instructions are executed by the processor, the steps of the defect management method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: It stores a computer program / instruction, which, when executed by a processor, implements the steps of the defect management method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program / instruction, which, when executed by a processor, implements the steps of the defect management method according to any one of claims 1 to 6.

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