Server research and development demand automatic processing method and server research and development management system
By using multimodal intelligent analysis technology and automated closed-loop acceptance, the problems of low efficiency in demand processing and inconsistent acceptance standards in traditional server R&D have been solved, achieving efficient and accurate demand understanding and acceptance, and reducing costs and risks.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-04-10
AI Technical Summary
In traditional server development, demand processing relies on manual methods, which are inefficient and prone to deviating from actual needs. The acceptance process lacks unified and objective standards, leading to increased development costs and project risks.
By employing multimodal intelligent parsing technology to integrate heterogeneous data from multiple sources such as text and images, we can achieve efficient integration of requirements and accurate semantic understanding, build an automated closed-loop acceptance system, automatically generate and execute test cases, and generate feedback suggestions in real time by comparing with acceptance standards.
This improved the efficiency and accuracy of demand processing, forming a closed loop of continuous optimization, ensuring that the R&D direction accurately meets actual needs, and reducing R&D costs and project risks.
Smart Images
Figure CN121832894A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of server R&D technology, and in particular to a method for automatically processing server R&D requirements and a server R&D management system. Background Technology
[0002] In server development, requirements processing and deliverable acceptance are two crucial stages. Currently, traditional server development requirements processing relies primarily on manual methods. Developers need to spend significant time and effort interpreting and analyzing requirements information from various channels and formats, including technical documents, design drawings, and meeting minutes. This manual approach is not only inefficient but also prone to misunderstandings, causing the development direction to deviate from actual needs, increasing development costs and project risks.
[0003] In terms of R&D results acceptance, the existing acceptance process also has significant shortcomings. Manual acceptance relies on subjective judgment and lacks unified, objective standards, making it difficult to comprehensively and accurately assess the quality of R&D results. Furthermore, the traditional acceptance process fails to establish an effective feedback mechanism, failing to provide timely and accurate feedback to the R&D team on identified issues, resulting in a slow and inefficient improvement process for R&D results. Summary of the Invention
[0004] This application provides an automatic processing method and a server R&D management system for server R&D requirements, in order to at least solve the problem of R&D direction deviating from actual needs in related technologies.
[0005] This application provides an automatic processing method for server R&D requirements, comprising: performing feature fusion and parsing on multimodal requirement data to obtain a requirement semantic understanding result; splitting the requirement semantic understanding result to obtain multiple sub-requirements and corresponding acceptance criteria; generating multiple target test cases according to each sub-requirement and the corresponding acceptance criteria, wherein one sub-requirement corresponds to at least one target test case and one target test case corresponds to one acceptance criterion; executing multiple target test cases to obtain test result information, and determining whether the test result of the target test case meets the corresponding acceptance criterion based on the test result information.
[0006] This application also provides a server R&D management system, including: a memory for storing computer programs; and a processor for executing the computer programs to implement the steps of any of the server R&D requirement automatic processing and closed-loop acceptance methods.
[0007] Through this application, the above-mentioned automatic processing method for server R&D requirements utilizes multimodal intelligent parsing technology to integrate heterogeneous data from multiple sources such as text and images in the requirement processing stage, achieving efficient integration and accurate semantic understanding of requirements. In the acceptance stage, an automated closed-loop acceptance system is constructed, automatically generating and executing test cases, and comparing real-time test results with preset acceptance standards. Feedback suggestions can be generated for non-conformities, prompting the R&D team to make improvements and re-acceptance, forming a closed loop of continuous optimization. This solves the problem of R&D direction deviating from actual needs in related technologies. Attached Figure Description
[0008] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0009] Figure 1 This is a hardware structure block diagram of a mobile terminal for an automatic processing method of server R&D requirements according to an embodiment of this application.
[0010] Figure 2 This is a flowchart illustrating an embodiment of the method for automatic processing and closed-loop acceptance of server R&D requirements in this application.
[0011] Figure 3 This is a structural block diagram of a server R&D management system according to an embodiment of this application;
[0012] Figure 4 This is a flowchart illustrating a multimodal intelligent parsing module according to an embodiment of this application;
[0013] Figure 5 This is a flowchart illustrating an automatic demand breakdown module according to an embodiment of this application.
[0014] Figure 6 This is a structural block diagram of a server R&D requirement automatic processing and closed-loop acceptance method according to an embodiment of this application. Detailed Implementation
[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.
[0016] It should be noted that, in the description of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "first," "second," etc., in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.
[0017] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0018] The specific application environment architecture or specific hardware architecture on which the automatic processing method based on server R&D needs depends is described here.
[0019] The methods and embodiments provided in this application can be executed on a server device or a similar computing device. Taking running on a server device as an example, Figure 1 This is a hardware structure block diagram of the server R&D requirement automatic processing method according to an embodiment of this application. Figure 1 As shown, the server device may include one or more ( Figure 1 Only one is shown in the image. A processor 102 (which may include, but is not limited to, a central processing unit (CPU), microprocessor (MCU), or programmable logic device (FPGA), etc.) and a memory 104 for storing data are also shown. The server device may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the server equipment described above. For example, the server equipment may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.
[0020] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the server R&D requirement automatic processing method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the server device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
[0021] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider for the server device. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.
[0022] The embodiments of this application provide an automatic processing method for server R&D requirements. The method is described in detail below in conjunction with the execution flow of the automatic processing method for server R&D requirements.
[0023] This embodiment provides a method for automatically processing server development requirements. Figure 2 This is a flowchart of an automatic processing method for server development requirements according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps:
[0024] Step S202: Perform feature fusion and parsing on the multimodal demand data to obtain the demand semantic understanding results;
[0025] Specifically, such as Figure 3 As shown, the server R&D management system includes a multimodal intelligent parsing module, which comprises a multi-source data acquisition submodule, a data preprocessing submodule, a multimodal feature fusion submodule, and an intelligent semantic understanding submodule. The multi-source data acquisition submodule is used to collect multimodal requirement data, the data preprocessing submodule is used to extract features from the multimodal requirement data, the multimodal feature fusion submodule is used to fuse the features of the multimodal requirement data, and the intelligent semantic understanding submodule is used to parse the fused features to obtain the requirement semantic understanding result.
[0026] Step S204: Decompose the above semantic understanding results of requirements to obtain multiple sub-requirements and corresponding acceptance criteria;
[0027] Specifically, such as Figure 3 As shown, the aforementioned server R&D management system also includes an automatic requirement decomposition module. This module comprises a requirement classification submodule, a constraint extraction submodule, a requirement decomposition rule engine submodule, and a sub-requirement association analysis submodule. The requirement classification submodule is used to classify requirements according to their categories. The constraint extraction submodule is used to extract constraints from the requirement data. The requirement decomposition rule engine submodule is used to decompose the semantic understanding results of the requirements to obtain multiple sub-requirements and corresponding acceptance criteria. The sub-requirement association analysis submodule is used to analyze the logical relationships between sub-requirements.
[0028] Step S206: Generate multiple target test cases based on each of the above sub-requirements and the corresponding above acceptance criteria. Each of the above sub-requirements corresponds to at least one target test case, and each of the above target test cases corresponds to one of the above acceptance criteria.
[0029] Specifically, such as Figure 3 As shown, the aforementioned server R&D management system also includes a closed-loop acceptance module, which includes an automated test case generation submodule. This automated test case generation submodule is used to automatically generate test cases.
[0030] Step S208: Execute multiple target test cases to obtain test result information, and determine whether the test results of the target test cases meet the corresponding acceptance criteria based on the test result information.
[0031] Specifically, such as Figure 3 As shown, the closed-loop acceptance module also includes a test execution and monitoring submodule, an intelligent evaluation and analysis submodule, and a feedback and improvement submodule. The test execution and monitoring submodule is used to execute test cases and collect test result information. The intelligent evaluation and analysis submodule is used to determine whether the test results of the target test cases meet the corresponding acceptance criteria. The feedback and improvement submodule is used to generate feedback suggestions for non-conformities and promote the R&D team to improve and re-accept the test.
[0032] Through the above steps, the automated processing method for server R&D requirements utilizes multimodal intelligent parsing technology to integrate heterogeneous data from multiple sources, such as text and images, to achieve efficient integration and accurate semantic understanding of requirements. In the acceptance phase, an automated closed-loop acceptance system is constructed, automatically generating and executing test cases. Real-time test results are compared with preset acceptance standards, generating feedback suggestions for non-conformities. This prompts the R&D team to make improvements and re-accept the tests, forming a continuous optimization loop and solving the problem of R&D direction deviating from actual needs in related technologies.
[0033] To achieve accurate analysis of R&D needs, in one optional implementation, step S202 includes:
[0034] Step S2022: Obtain the aforementioned multimodal requirement data, which includes the requirement specification, server hardware layout diagram, and recordings of requirement discussion meetings.
[0035] Step S2024: Preprocess the above multimodal demand data to obtain the features of the above multimodal demand data;
[0036] Step S2026: After mapping the features of the above multimodal demand data to the same semantic space, fuse them to obtain fused multimodal features;
[0037] Step S2028: Use a pre-trained language model and knowledge graph to perform semantic understanding on the above-mentioned fused multimodal features to obtain the above-mentioned semantic understanding results of the requirements. The above-mentioned pre-trained language model is a model that understands the user's natural language requirements by pre-training with text containing server development requirement information. The above-mentioned knowledge graph is a semantic network that stores the knowledge of server development domain and assists the model in accurate reasoning.
[0038] In the above embodiments, such as Figure 3As shown, the multi-source data acquisition submodule is responsible for collecting server R&D requirement data from various channels, including internal project management systems, document repositories, design software output files, and external industry standard documents and technical white papers. It supports the acquisition of data in various formats, such as text, images, audio, video, and tables, including text-formatted requirement specifications, image-formatted server hardware layout diagrams, and audio-formatted requirement discussion meeting recordings. The data preprocessing submodule employs appropriate preprocessing methods for different data types. For text data, it performs word segmentation, part-of-speech tagging, named entity recognition, and stop word removal to extract key information. For image data, it utilizes computer vision technology for image enhancement, edge detection, and feature extraction to identify hardware components, interface locations, and other information within the image. For audio data, it converts it into text content using speech recognition technology before further processing. For table data, it performs data cleaning, format conversion, and standardization to ensure data consistency and usability. The multimodal feature fusion submodule uses deep learning algorithms, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and their variants (LSTM, GRU), to fuse features extracted from different modalities. By constructing cross-modal mapping relationships, features from different modalities such as text and images are mapped to the same semantic space, achieving complementarity and synergy of multimodal information and forming a more comprehensive and accurate representation of requirements. The intelligent semantic understanding submodule, based on the fused multimodal features, utilizes pre-trained language models (such as BERT and GPT series) and knowledge graph technology to perform semantic understanding of server development requirements. By analyzing the semantic relationships, logical structures, and domain knowledge within the requirements, it uncovers the deeper meaning and potential connections of the requirements, accurately understanding the user's true intentions and providing a solid semantic foundation for subsequent requirement decomposition and acceptance.
[0039] To facilitate the development process, in one optional implementation, step S204 includes:
[0040] Step S2042: Classify the server development requirements from the above semantic understanding results to obtain the requirement categories of each of the above server development requirements from the above semantic understanding results.
[0041] Step S2044: Extract constraints from the above requirements semantic understanding results to obtain the above constraints for server development.
[0042] Step S2046: Based on the above requirement categories and constraints, the above server development requirements derived from the above requirement semantic understanding are decomposed to obtain multiple above sub-requirements and corresponding above acceptance criteria.
[0043] In the above embodiments, such as Figure 4As shown, the requirement classification submodule, based on the semantic understanding results of the requirements output by the multimodal intelligent parsing module and combined with preset requirement classification standards and rules, automatically classifies server R&D requirements into several major categories, such as hardware requirements, software requirements, performance requirements, reliability requirements, and scalability requirements, and further subdivides them into specific subcategories. For example, hardware requirements can be subdivided into requirements for components such as CPU, GPU, memory, storage, and motherboard; software requirements can be subdivided into requirements for operating systems, databases, application software, and drivers. The constraint extraction submodule extracts various constraints from the requirement information, including performance constraints (such as CPU clock speed not less than 2.5GHz, memory bandwidth not less than 50GB / s), time constraints (such as a project delivery cycle of 3 months), cost constraints (such as R&D costs controlled within 5 million yuan), and environmental constraints (such as the server needing to operate stably in an environment of -10℃ to 40℃). Simultaneously, it analyzes the interrelationships and impacts between constraints to ensure that the decomposed sub-requirements meet all constraint requirements. The requirement decomposition rule engine submodule establishes a comprehensive requirement decomposition rule library, including general decomposition rules based on domain knowledge and experience, as well as customized rules for specific projects or requirement types. Based on requirement classification and constraints, the rule engine automatically matches the corresponding decomposition rules, breaking down complex R&D requirements into specific, executable sub-requirements or task units. Each sub-requirement clearly defines detailed information such as task objectives, input / output requirements, technical indicators, and acceptance criteria, facilitating subsequent development work by the R&D team.
[0044] To improve efficiency, in an optional implementation, after decomposing the server development requirements derived from the semantic understanding of the requirements according to the aforementioned requirement categories and constraints to obtain multiple sub-requirements and corresponding acceptance criteria, the method further includes:
[0045] Step S302: Perform correlation analysis on the above sub-requirements to obtain the logical relationships between the above sub-requirements. The above logical relationships include dependency relationships, parallel relationships and conflict relationships.
[0046] Step S304: Based on the logical relationship between the above sub-requirements, plan the execution order of the task units of each of the above sub-requirements. The above task units are the development tasks to realize the above sub-requirements.
[0047] In the above embodiments, such as Figure 4As shown, the sub-requirement correlation analysis submodule performs correlation analysis on the decomposed sub-requirements, identifying dependencies, parallel relationships, and conflict relationships between them. By constructing a requirement correlation graph or dependency network, it visually displays the logical relationships between sub-requirements, providing a reference for project management and resource scheduling. For example, in server hardware development, the selection of the CPU may affect the motherboard design and memory configuration. The sub-requirement correlation analysis submodule can accurately identify such dependencies and make reasonable plans in task allocation and schedule scheduling, significantly improving the efficiency and accuracy of requirement processing.
[0048] In an optional implementation for automatically generating test cases, step S206 above includes:
[0049] Step S2062: After completing all the above-mentioned task units in the execution order of the above-mentioned task units according to the above-mentioned sub-requirements, generate multiple target test cases according to each of the above-mentioned sub-requirements and the corresponding above-mentioned acceptance criteria using multiple test case templates. The above-mentioned test case templates include test cases for functional testing, performance testing, stress testing, compatibility testing, and security testing.
[0050] In the above embodiments, such as Figure 5 As shown, the automated test case generation submodule automatically breaks down the sub-requirements and acceptance criteria output by the main module based on the requirements. Using test case generation algorithms and templates, it automatically generates comprehensive and effective test cases. These test cases cover multiple aspects, including functional testing, performance testing, stress testing, compatibility testing, and security testing, ensuring coverage of all key features and requirements of the server's development results. For example, for the server's data storage function, it generates functional test cases including data writing, reading, modification, and deletion operations, as well as data storage performance test cases under high concurrency conditions.
[0051] In order to understand the testing progress and identify problems in a timely manner, in one optional implementation, step S208 above includes:
[0052] Step S2082: Execute multiple target test cases to obtain running data and test result information. The running data includes CPU utilization, memory usage, disk I / O and network traffic. The test result information includes whether the test failed, error logs and performance metrics data.
[0053] Step S2084: Display the above-mentioned running data and test result information of each of the above-mentioned target test cases.
[0054] In the above embodiments, such as Figure 5As shown, the test execution and monitoring submodule integrates with automated testing tools to automatically execute generated test cases and monitor the testing process in real time. During test execution, it collects various server operational data, such as CPU utilization, memory usage, disk I / O, and network traffic, as well as test result information, such as test pass / fail status, error logs, and performance metrics. Using visualization technology, the test process and results are presented to testers and the development team in intuitive charts and reports, facilitating their timely understanding of test progress and problem identification.
[0055] In order to analyze the reasons for non-compliance with the standard, in an optional implementation, step S208 above further includes:
[0056] Step S2086: Compare and analyze the test result information of the target test case with the corresponding acceptance criteria to determine whether the test result of the target test case meets the corresponding acceptance criteria and the reasons why the test result does not meet the corresponding acceptance criteria. The reasons include performance bottlenecks and functional defects.
[0057] In the above embodiments, such as Figure 5 As shown, the intelligent evaluation and analysis submodule intelligently compares and analyzes the test results against preset acceptance criteria. Utilizing machine learning algorithms and data analysis techniques, it comprehensively evaluates the quality of server R&D achievements. It not only determines whether the test results meet the standards but also delves into the reasons for non-compliance, such as performance bottlenecks and specific manifestations of functional defects. By comparing with historical test data and industry standards, it provides objective and accurate evaluation reports, offering a basis for improving R&D achievements.
[0058] To improve the quality of research and development results, in one optional implementation, after determining whether the test results of the target test cases meet the corresponding acceptance criteria based on the aforementioned test result information, the method further includes:
[0059] Step S402: If the test result information of the target test case does not meet the corresponding acceptance criteria, an improvement suggestion is generated, and one improvement suggestion corresponds to at least one improvement suggestion.
[0060] Step S404: After completing the improvements according to the above improvement suggestions for the test cases, perform a second test on the test cases and accept the results.
[0061] In the above embodiments, such as Figure 5As shown, the feedback and improvement submodule automatically generates detailed feedback and improvement suggestions based on the evaluation report generated by the intelligent evaluation and analysis submodule, and promptly provides this information to the R&D team. The R&D team then makes targeted improvements and optimizations to the R&D results based on the feedback information before resubmitting for acceptance. The system records the process and results of each feedback and improvement, forming a complete R&D improvement log. This achieves a closed-loop process from problem identification during acceptance to improvement and re-acceptance, continuously improving the quality of R&D results until all requirements are fully met.
[0062] To achieve tracking and management of R&D projects, in one optional implementation, the above method further includes:
[0063] Step S502: Track the progress of each of the above task units to obtain a task unit tracking chart. The task unit tracking chart is used to display the overall progress of the development project, the completion status and time nodes of each of the above task units. The overall progress of the development project is the number of the above task units completed.
[0064] Step S504: If the overall progress of the aforementioned development project deviates from the target progress at the current time, an early warning is issued.
[0065] The above implementation scheme further includes a user access control submodule, a project progress tracking submodule, a data sharing and collaboration submodule, and a system configuration and maintenance submodule. 1. User Access Control Submodule: This submodule assigns roles and permissions to system users, including system administrators, developers, testers, project managers, and other roles. Different roles have different operation permissions and data access permissions to ensure the security and confidentiality of system data. For example, system administrators have the highest permissions and can perform system configuration and user management operations; developers can only view and operate requirements and development tasks related to their assigned projects; testers are mainly responsible for test execution and result viewing. 2. Project Progress Tracking Submodule: Based on the sub-requirements and task units generated by the automatic requirement decomposition module, and combined with the project plan and schedule, this submodule tracks and monitors the progress of the server development project in real time. It displays the overall project progress, the completion status of each sub-task, and time nodes through visual project progress Gantt charts, task lists, etc. When project progress is delayed or deviates, it issues timely warnings and provides relevant data analysis and suggestions to help project managers adjust project plans and resource allocation to ensure timely project completion. Data Sharing and Collaboration Submodule: Establishes a unified data sharing platform to enable data sharing and interaction between the multimodal intelligent parsing module, the automatic requirement decomposition module, and the closed-loop acceptance module. It provides collaboration tools and communication channels to facilitate information exchange, document sharing, and collaborative work among R&D team members. For example, R&D personnel can upload and download relevant design documents and code files on the platform; testers can promptly report problems discovered during testing to R&D personnel for discussion and communication. System Configuration and Maintenance Submodule: Allows system administrators to configure and maintain the system, including system parameter settings, data backup and recovery, software version updates, and server performance monitoring. It regularly backs up system data to ensure data security and integrity; monitors the system's operating status and performance indicators to promptly identify and resolve system faults and performance issues; and upgrades and optimizes the system's functions based on user needs and technological advancements to maintain its advanced nature and applicability.
[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.
[0067] Embodiments of this application also provide an automatic processing device for server R&D requirements. Figure 6 This is a structural block diagram of an automatic processing device for server R&D requirements according to an embodiment of this application, such as... Figure 6 As shown, the device includes:
[0068] The parsing unit 602 is used to perform feature fusion and parsing on multimodal demand data to obtain the demand semantic understanding results;
[0069] Specifically, such as Figure 3 As shown, the server R&D management system includes a multimodal intelligent parsing module, which comprises a multi-source data acquisition submodule, a data preprocessing submodule, a multimodal feature fusion submodule, and an intelligent semantic understanding submodule. The multi-source data acquisition submodule is used to collect multimodal requirement data, the data preprocessing submodule is used to extract features from the multimodal requirement data, the multimodal feature fusion submodule is used to fuse the features of the multimodal requirement data, and the intelligent semantic understanding submodule is used to parse the fused features to obtain the requirement semantic understanding result.
[0070] The splitting unit 604 is used to split the above requirement semantic understanding results to obtain multiple sub-requirements and corresponding acceptance criteria;
[0071] Specifically, such as Figure 3 As shown, the aforementioned server R&D management system also includes an automatic requirement decomposition module. This module comprises a requirement classification submodule, a constraint extraction submodule, a requirement decomposition rule engine submodule, and a sub-requirement association analysis submodule. The requirement classification submodule is used to classify requirements according to their categories. The constraint extraction submodule is used to extract constraints from the requirement data. The requirement decomposition rule engine submodule is used to decompose the semantic understanding results of the requirements to obtain multiple sub-requirements and corresponding acceptance criteria. The sub-requirement association analysis submodule is used to analyze the logical relationships between sub-requirements.
[0072] The first generation unit 606 is used to generate multiple target test cases according to each of the above-mentioned sub-requirements and the corresponding above-mentioned acceptance criteria. Each of the above-mentioned sub-requirements corresponds to at least one target test case, and each of the above-mentioned target test cases corresponds to one of the above-mentioned acceptance criteria.
[0073] Specifically, such as Figure 3 As shown, the aforementioned server R&D management system also includes a closed-loop acceptance module, which includes an automated test case generation submodule. This automated test case generation submodule is used to automatically generate test cases.
[0074] The determination unit 608 is used to execute multiple of the above-mentioned target test cases to obtain test result information, and to determine whether the test results of the above-mentioned target test cases meet the corresponding acceptance criteria based on the above-mentioned test result information.
[0075] Specifically, such as Figure 3As shown, the closed-loop acceptance module also includes a test execution and monitoring submodule, an intelligent evaluation and analysis submodule, and a feedback and improvement submodule. The test execution and monitoring submodule is used to execute test cases and collect test result information. The intelligent evaluation and analysis submodule is used to determine whether the test results of the target test cases meet the corresponding acceptance criteria. The feedback and improvement submodule is used to generate feedback suggestions for non-conformities and promote the R&D team to improve and re-accept the test.
[0076] Through the above devices, the above-mentioned automatic processing method for server R&D requirements utilizes multimodal intelligent parsing technology to integrate heterogeneous data from multiple sources such as text and images in the requirement processing stage, achieving efficient integration and accurate semantic understanding of requirements. In the acceptance stage, an automated closed-loop acceptance system is constructed, automatically generating and executing test cases, and comparing real-time test results with preset acceptance standards. Feedback suggestions can be generated for non-conformities, prompting the R&D team to make improvements and re-accept the tests, forming a closed loop of continuous optimization. This solves the problem of R&D direction deviating from actual needs in related technologies.
[0077] To achieve accurate analysis of R&D needs, in one optional implementation, the analysis unit includes:
[0078] The acquisition module is used to acquire the aforementioned multimodal requirement data, which includes requirement specifications, server hardware layout diagrams, and recordings of requirement discussion meetings.
[0079] The preprocessing module is used to preprocess the aforementioned multimodal demand data to obtain the features of the aforementioned multimodal demand data.
[0080] The fusion module is used to map the features of the aforementioned multimodal requirement data to the same semantic space and then fuse them to obtain fused multimodal features;
[0081] The parsing module is used to perform semantic understanding on the above-mentioned fused multimodal features using a pre-trained language model and a knowledge graph to obtain the above-mentioned semantic understanding results of the requirements. The above-mentioned pre-trained language model is a model that understands the user's natural language requirements by pre-training with text containing server development requirement information. The above-mentioned knowledge graph is a semantic network that stores the knowledge of server development domain and assists the model in accurate reasoning.
[0082] In the above embodiments, such as Figure 3As shown, the multi-source data acquisition submodule is responsible for collecting server R&D requirement data from various channels, including internal project management systems, document repositories, design software output files, and external industry standard documents and technical white papers. It supports the acquisition of data in various formats, such as text, images, audio, video, and tables, including text-formatted requirement specifications, image-formatted server hardware layout diagrams, and audio-formatted requirement discussion meeting recordings. The data preprocessing submodule employs appropriate preprocessing methods for different data types. For text data, it performs word segmentation, part-of-speech tagging, named entity recognition, and stop word removal to extract key information. For image data, it utilizes computer vision technology for image enhancement, edge detection, and feature extraction to identify hardware components, interface locations, and other information within the image. For audio data, it converts it into text content using speech recognition technology before further processing. For table data, it performs data cleaning, format conversion, and standardization to ensure data consistency and usability. The multimodal feature fusion submodule uses deep learning algorithms, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and their variants (LSTM, GRU), to fuse features extracted from different modalities. By constructing cross-modal mapping relationships, features from different modalities such as text and images are mapped to the same semantic space, achieving complementarity and synergy of multimodal information and forming a more comprehensive and accurate representation of requirements. The intelligent semantic understanding submodule, based on the fused multimodal features, utilizes pre-trained language models (such as BERT and GPT series) and knowledge graph technology to perform semantic understanding of server development requirements. By analyzing the semantic relationships, logical structures, and domain knowledge within the requirements, it uncovers the deeper meaning and potential connections of the requirements, accurately understanding the user's true intentions and providing a solid semantic foundation for subsequent requirement decomposition and acceptance.
[0083] To facilitate development, in one optional implementation, the aforementioned splitting unit includes:
[0084] The classification module is used to classify the server development requirements based on the semantic understanding results of the above requirements, and obtain the requirement categories of each of the above server development requirements based on the semantic understanding results of the above requirements.
[0085] The extraction module is used to extract constraints from the above-mentioned semantic understanding of requirements, thereby obtaining the constraints for the above-mentioned server development.
[0086] The decomposition module is used to decompose the above server development requirements based on the above requirement categories and the above constraints, resulting in multiple above sub-requirements and corresponding above acceptance criteria.
[0087] In the above embodiments, such as Figure 4As shown, the requirement classification submodule, based on the semantic understanding results of the requirements output by the multimodal intelligent parsing module and combined with preset requirement classification standards and rules, automatically classifies server R&D requirements into several major categories, such as hardware requirements, software requirements, performance requirements, reliability requirements, and scalability requirements, and further subdivides them into specific subcategories. For example, hardware requirements can be subdivided into requirements for components such as CPU, GPU, memory, storage, and motherboard; software requirements can be subdivided into requirements for operating systems, databases, application software, and drivers. The constraint extraction submodule extracts various constraints from the requirement information, including performance constraints (such as CPU clock speed not less than 2.5GHz, memory bandwidth not less than 50GB / s), time constraints (such as a project delivery cycle of 3 months), cost constraints (such as R&D costs controlled within 5 million yuan), and environmental constraints (such as the server needing to operate stably in an environment of -10℃ to 40℃). Simultaneously, it analyzes the interrelationships and impacts between constraints to ensure that the decomposed sub-requirements meet all constraint requirements. The requirement decomposition rule engine submodule establishes a comprehensive requirement decomposition rule library, including general decomposition rules based on domain knowledge and experience, as well as customized rules for specific projects or requirement types. Based on requirement classification and constraints, the rule engine automatically matches the corresponding decomposition rules, breaking down complex R&D requirements into specific, executable sub-requirements or task units. Each sub-requirement clearly defines detailed information such as task objectives, input / output requirements, technical indicators, and acceptance criteria, facilitating subsequent development work by the R&D team.
[0088] To improve efficiency, in one optional embodiment, the above-mentioned apparatus further includes:
[0089] The association analysis unit is used to decompose the server development requirements based on the above requirement categories and constraints according to the above requirement semantic understanding results, and obtain multiple sub-requirements and corresponding acceptance criteria. Then, it performs association analysis on the multiple sub-requirements to obtain the logical relationships between the multiple sub-requirements. The logical relationships include dependency relationships, parallel relationships and conflict relationships.
[0090] The planning unit is used to plan the execution order of the task units of each of the above-mentioned sub-requirements according to the logical relationship between them. The task units are the development tasks to realize the above-mentioned sub-requirements.
[0091] In the above embodiments, such as Figure 4As shown, the sub-requirement correlation analysis submodule performs correlation analysis on the decomposed sub-requirements, identifying dependencies, parallel relationships, and conflict relationships between them. By constructing a requirement correlation graph or dependency network, it visually displays the logical relationships between sub-requirements, providing a reference for project management and resource scheduling. For example, in server hardware development, the selection of the CPU may affect the motherboard design and memory configuration. The sub-requirement correlation analysis submodule can accurately identify such dependencies and make reasonable plans in task allocation and schedule scheduling, significantly improving the efficiency and accuracy of requirement processing.
[0092] To automatically generate test cases, in one optional implementation, the first generation unit includes:
[0093] The generation module is used to generate multiple target test cases according to various test case templates, based on each of the aforementioned sub-requirements and the corresponding acceptance criteria, after all the aforementioned task units have been completed in the execution order of the aforementioned task units according to the aforementioned sub-requirements. The aforementioned test case templates include test case templates for functional testing, performance testing, stress testing, compatibility testing, and security testing.
[0094] In the above embodiments, such as Figure 5 As shown, the automated test case generation submodule automatically breaks down the sub-requirements and acceptance criteria output by the main module based on the requirements. Using test case generation algorithms and templates, it automatically generates comprehensive and effective test cases. These test cases cover multiple aspects, including functional testing, performance testing, stress testing, compatibility testing, and security testing, ensuring coverage of all key features and requirements of the server's development results. For example, for the server's data storage function, it generates functional test cases including data writing, reading, modification, and deletion operations, as well as data storage performance test cases under high concurrency conditions.
[0095] To promptly understand test progress and identify problems, in one optional implementation, the determining unit includes:
[0096] The execution module is used to execute multiple target test cases to obtain running data and test result information. The running data includes CPU utilization, memory usage, disk I / O, and network traffic. The test result information includes whether the test failed, error logs, and performance metrics data.
[0097] The display module is used to display the above-mentioned running data and test result information of each of the above-mentioned target test cases.
[0098] In the above embodiments, such as Figure 5As shown, the test execution and monitoring submodule integrates with automated testing tools to automatically execute generated test cases and monitor the testing process in real time. During test execution, it collects various server operational data, such as CPU utilization, memory usage, disk I / O, and network traffic, as well as test result information, such as test pass / fail status, error logs, and performance metrics. Using visualization technology, the test process and results are presented to testers and the development team in intuitive charts and reports, facilitating their timely understanding of test progress and problem identification.
[0099] To analyze the reasons for non-compliance with standards, in one optional implementation, the determining unit further includes:
[0100] The determination module is used to compare and analyze the test result information of the target test cases with the corresponding acceptance criteria to determine whether the test results of the target test cases meet the corresponding acceptance criteria and the reasons why the test results do not meet the corresponding acceptance criteria. The reasons include performance bottlenecks and functional defects.
[0101] In the above embodiments, such as Figure 5 As shown, the intelligent evaluation and analysis submodule intelligently compares and analyzes the test results against preset acceptance criteria. Utilizing machine learning algorithms and data analysis techniques, it comprehensively evaluates the quality of server R&D achievements. It not only determines whether the test results meet the standards but also delves into the reasons for non-compliance, such as performance bottlenecks and specific manifestations of functional defects. By comparing with historical test data and industry standards, it provides objective and accurate evaluation reports, offering a basis for improving R&D achievements.
[0102] To improve the quality of research and development results, in one optional embodiment, the above-mentioned device further includes:
[0103] The second generation unit is used to generate improvement suggestions after determining whether the test results of the target test case meet the corresponding acceptance criteria based on the test result information. If the test result information of the target test case does not meet the corresponding acceptance criteria, one improvement suggestion corresponds to at least one improvement suggestion.
[0104] The testing unit is used to perform secondary testing and acceptance of the above test cases after the improvements have been made in accordance with the above improvement suggestions for the test cases.
[0105] In the above embodiments, such as Figure 5As shown, the feedback and improvement submodule automatically generates detailed feedback and improvement suggestions based on the evaluation report generated by the intelligent evaluation and analysis submodule, and promptly provides this information to the R&D team. The R&D team then makes targeted improvements and optimizations to the R&D results based on the feedback information before resubmitting for acceptance. The system records the process and results of each feedback and improvement, forming a complete R&D improvement log. This achieves a closed-loop process from problem identification during acceptance to improvement and re-acceptance, continuously improving the quality of R&D results until all requirements are fully met.
[0106] To enable the tracking and management of R&D projects, in one optional implementation, the above-mentioned device further includes:
[0107] The tracking unit is used to track the progress of each of the above-mentioned task units and obtain a task unit tracking chart. The task unit tracking chart is used to display the overall progress of the development project, the completion status and time nodes of each of the above-mentioned task units. The overall progress of the development project is the number of task units completed.
[0108] The sending unit is used to issue an early warning when the overall progress of the aforementioned development project deviates from the target progress at the current time.
[0109] The above implementation scheme further includes a user access control submodule, a project progress tracking submodule, a data sharing and collaboration submodule, and a system configuration and maintenance submodule. 1. User Access Control Submodule: This submodule assigns roles and permissions to system users, including system administrators, developers, testers, project managers, and other roles. Different roles have different operation permissions and data access permissions to ensure the security and confidentiality of system data. For example, system administrators have the highest permissions and can perform system configuration and user management operations; developers can only view and operate requirements and development tasks related to their assigned projects; testers are mainly responsible for test execution and result viewing. 2. Project Progress Tracking Submodule: Based on the sub-requirements and task units generated by the automatic requirement decomposition module, and combined with the project plan and schedule, this submodule tracks and monitors the progress of the server development project in real time. It displays the overall project progress, the completion status of each sub-task, and time nodes through visual project progress Gantt charts, task lists, etc. When project progress is delayed or deviates, it issues timely warnings and provides relevant data analysis and suggestions to help project managers adjust project plans and resource allocation to ensure timely project completion. Data Sharing and Collaboration Submodule: Establishes a unified data sharing platform to enable data sharing and interaction between the multimodal intelligent parsing module, the automatic requirement decomposition module, and the closed-loop acceptance module. It provides collaboration tools and communication channels to facilitate information exchange, document sharing, and collaborative work among R&D team members. For example, R&D personnel can upload and download relevant design documents and code files on the platform; testers can promptly report problems discovered during testing to R&D personnel for discussion and communication. System Configuration and Maintenance Submodule: Allows system administrators to configure and maintain the system, including system parameter settings, data backup and recovery, software version updates, and server performance monitoring. It regularly backs up system data to ensure data security and integrity; monitors the system's operating status and performance indicators to promptly identify and resolve system faults and performance issues; and upgrades and optimizes the system's functions based on user needs and technological advancements to maintain its advanced nature and applicability.
[0110] For a description of the features in the embodiment of the automatic processing device for server R&D requirements, please refer to the relevant description of the embodiment of the automatic processing method for server R&D requirements, which will not be repeated here.
[0111] Embodiments of this application also provide a server R&D management system, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above embodiments of the automatic processing method for server R&D requirements.
[0112] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above embodiments of the automatic processing method for server R&D requirements.
[0113] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.
[0114] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the automatic processing method for server R&D requirements.
[0115] Embodiments of this application also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above embodiments of the automatic processing method for server development requirements.
[0116] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0117] The above provides a detailed description of an automatic processing method for server R&D requirements. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only intended to help understand the method and core ideas of this application. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this application.
Claims
1. A method for automatically processing server R&D requirements, characterized in that, The method comprises the following steps: performing feature fusion and analysis on multi-modal demand data to obtain demand semantic understanding results; splitting the demand semantic understanding results to obtain multiple sub-demands and corresponding acceptance criteria; generating multiple target test cases according to each of the sub-demands and the corresponding acceptance criteria, with one sub-demand corresponding to at least one target test case and one target test case corresponding to one acceptance criterion; executing multiple target test cases to obtain test result information, and determining whether the test results of the target test cases meet the corresponding acceptance criteria according to the test result information.
2. The server R&D demand automatic processing method according to claim 1, wherein, Performing feature fusion and analysis on multi-modal demand data to obtain demand semantic understanding results, comprising: obtaining the multi-modal demand data, which includes demand specification, server hardware layout diagram and demand discussion conference recording; preprocessing the multi-modal demand data respectively to obtain the features of the multi-modal demand data; fusing the features of the multi-modal demand data after mapping them to the same semantic space to obtain fused multi-modal features; performing semantic understanding on the fused multi-modal features using a pre-trained language model and a knowledge graph to obtain the demand semantic understanding results, wherein the pre-trained language model is a model for understanding user natural language demand pre-trained by text containing server development demand information, and the knowledge graph is a semantic network for structurally storing server development field knowledge and assisting model accurate reasoning.
3. The server R&D demand automatic processing method of claim 1, wherein, Splitting the demand semantic understanding results to obtain multiple sub-demands and corresponding acceptance criteria, comprising: classifying the server development demands of the demand semantic understanding results to obtain the demand categories of each of the server development demands of the demand semantic understanding results; extracting constraint conditions from the demand semantic understanding results to obtain the constraint conditions of the server development; decomposing the server development demands of the demand semantic understanding results according to the demand categories and the constraint conditions to obtain multiple sub-demands and corresponding acceptance criteria.
4. The server R&D demand automatic processing method according to claim 3, characterized in that, After decomposing the server development demands of the demand semantic understanding results according to the demand categories and the constraint conditions to obtain multiple sub-demands and corresponding acceptance criteria, the method further comprises: performing correlation analysis on multiple sub-demands to obtain logical relationships between the multiple sub-demands, wherein the logical relationships include dependency relationships, parallel relationships and conflict relationships; planning the execution order of task units of each of the sub-demands according to the logical relationships between the sub-demands, wherein the task units are development tasks for realizing the sub-demands.
5. The server R&D demand automatic processing method according to claim 4, characterized in that, Generating multiple target test cases according to each of the sub-demands and the corresponding acceptance criteria, comprising: generating multiple target test cases according to each of the sub-demands and the corresponding acceptance criteria according to multiple test case templates in the case that all task units are completed in the execution order of the task units of the sub-demands, wherein the test case templates include test case templates for functional testing, performance testing, stress testing, compatibility testing and security testing.
6. The server R&D demand automatic processing method of claim 1, wherein, The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The running data and the test result information of each target test case are displayed.
7. The server R&D demand automatic processing method of claim 1, wherein, The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises:
8. The server R&D demand automatic processing method of claim 1, wherein, The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises:
9. The method of claim 4, wherein the server R&D requirement automatic processing method is characterized by, The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises:
10. A server development management system characterized by comprising: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: The test result information is obtained by executing the target test cases, and the test result information comprises: