14 results about "Class diagram" patented technology

The application discloses a software design and development method and a terminal, a detailed design document corresponding to an architecture design document is generated according to software requirements, a corresponding core class code is generated according to a core class diagram of the detailed design document, and an engineering code is obtained by directly developing based on the core class code by a developer; if the engineering code does not match the core class in the detailed design document, the core class diagram and the corresponding design document are re-generated, thereby greatly improving the consistency of software design and software development; if the engineering code matches the core class of the detailed design document, the engineering code is submitted to a code warehouse, the submitted document is ensured to be correct and reliable, and the design document is ensured to be well landed in the development stage.

The invention discloses a software quality uncertainty modeling method based on a graph neural network and Wasserstein comparative learning, and belongs to the technical field of software quality metrics, and the method comprises the steps: firstly, constructing a UML class graph into a heterogeneous graph containing a plurality of node and edge types; extracting node embedding and generating an instance-level dynamic weight vector by using a pre-trained graph attention network; calculating the difference between the node embedding distribution and the predefined positive and negative ideal distribution by adopting a Sliced Wasserstein distance; and finally calculating to obtain a comprehensive complexity score in combination with the dynamic index. According to the method, the topological structure of the class diagram is effectively utilized, accurate measurement and self-adaptive personalized evaluation of the geometric difference of feature distribution are realized, the robustness under fuzzy and incomplete data is improved, and the uncertainty in quality evaluation is explicitly quantified.

The invention relates to a data archiving configuration method, a data archiving method, a data archiving system and data archiving equipment, and belongs to the technical field of data processing.The data archiving configuration method comprises the steps that in response to configuration operation of a user on the relation between service models through a user interface, a class diagram representing the relation between the service models is constructed; wherein different business models correspond to different business data; acquiring a data archiving condition configured for the business model; and generating an archiving scheme of the business model according to the data archiving condition and the class diagram. The class diagram-based data archiving configuration method provided by the invention is easier to operate by a user.

The application provides an interactive material setting module design method and simulation software construction method and device, which comprises the following steps: determining a material data structure class diagram, a cross-section data structure class diagram and a cross-section assignment data structure class diagram based on a combination mode; constructing an interactive material function framework based on the material data structure class diagram; the interactive material function framework comprises a material creation dialogue window; constructing an interactive cross-section function framework based on the cross-section data structure class diagram; the interactive cross-section function framework comprises a cross-section creation dialogue window; constructing an interactive cross-section assignment function framework based on the cross-section assignment data structure class diagram; the interactive cross-section assignment function framework comprises a cross-section assignment editing dialogue window; reserving a material creation function button in the cross-section creation dialogue window and reserving a cross-section creation button in the cross-section assignment editing dialogue window. The application effectively guarantees the expandability and maintainability of the material setting module and improves the development efficiency.

The invention discloses a software quality adaptive measurement method based on information geometry and reinforcement learning. The method comprises the following steps: acquiring original quality feature data of a software class diagram; extracting low-dimensional manifold coordinates through information geometric mapping and sparse representation; reconstructing an index quantity value according to the manifold coordinates and constructing a composite fuzzy matter element; carrying out nondimensionalization on matter elements to obtain an optimal membership matrix; determining an initial weight based on an entropy weight method; iterative optimization is carried out through a reinforcement learning adaptive feedback mechanism to obtain an optimization weight; and calculating a comprehensive evaluation value by adopting an MARCOS method and outputting a measurement result. According to the method, a breakthrough of software quality measurement from static state to dynamic state self-adaption is realized, the accuracy and practicability of measurement are remarkably improved, and the defects of a traditional method in the aspects of high-dimensional complexity, evaluation fuzziness and static weight limitation are effectively overcome.

The invention belongs to the technical field of information security, and relates to a smart home temperature control system opacity strengthening method based on a tag time Petri network. Establishing a label time Petri network model of the intelligent household temperature control system; constructing a partial correction state class diagram based on time sequence observation of which the current state is not transparent, forming a path constraint model consistent with observation logic, and identifying a path set inconsistent with observation time; screening a path which meets the opaqueness of the current state relative to the secret identifier set in the path set, and constructing a linear programming model to solve a minimum time sequence disturbance function value; and updating the original time sequence observation by using the perturbation function, and generating a new observation sequence meeting the opaqueness of the current state. According to the method, an attacker is effectively prevented from deducing the real state of the system through a time sequence, meanwhile, exhaustive search on a full-state space is avoided, the calculation complexity is reduced, and the applicability and expandability of the method in a complex discrete event system are improved.

The invention discloses a software quality measurement method based on FERMATEAN fuzzy PROMETHEE and quantum particle swarm optimization. The method comprises the following steps: acquiring an original characteristic value of a software class diagram and constructing a composite fuzzy matter element; the fuzzy number is converted into a Fermatean fuzzy number meeting cubic constraint so as to expand an uncertainty representation space; calculating a score function and an optimal membership degree to form a standardized matrix; constructing a fitness function fusing the distinction degree and the balance penalty, and adopting a quantum particle swarm optimization algorithm to adaptively learn an optimal index weight; on the basis of the optimal weight and the optimal membership degree, a net flow value of each object is calculated by using a PROMETHEE method; and calculating a comprehensive evaluation index according to the net flow value and dividing quality grades. According to the method, the limitation of a traditional method in the aspects of fuzzy information description, static empowerment and linear compensation is overcome, and the accuracy, the adaptability and the decision reasonability of software quality measurement are improved.

The application provides a modeling and model conversion method for automatic driving demand analysis, comprising the following steps: step 1, defining a UML extension meta-model; step 2, obtaining an intermediate model; and step 3, generating a target extension model. The application combines the abstracted concepts with the UML analysis model by analyzing and extracting the automatic driving related terms and concepts and the behavior characteristics of the automatic driving vehicle, realizes the extension of the UML class diagram and the activity diagram in the automatic driving field, formulates the model conversion rules according to the automatic driving concepts of the automatic driving system limited use case modeling language, and realizes the model conversion method based on the model conversion engine written in the Java language, so that the defects that the general model conversion method cannot process the RUCM4ADS model are made up, the efficiency and accuracy of the model conversion in the automatic driving field are promoted, the subsequent analysis and testing of the system demand are facilitated, and the accuracy of the demand modeling in the automatic driving field is improved.

This application relates to a code generation method, apparatus, device, storage medium, and program product. The method includes: obtaining a requirement model; obtaining an initial class diagram of a design model based on the requirement model; optimizing the initial class diagram of the design model using a multi-objective genetic algorithm and a large model to obtain an optimized design model; and generating monolithic system code matching the requirement model based on the optimized design model. This application can improve code generation efficiency and code generation quality.

The invention discloses a code reconstruction method and device, equipment, a medium and a chip, and belongs to the field of artificial intelligence. Comprising the steps of generating a UML class diagram according to a call chain of a source code of a to-be-upgraded application; the UML class diagram is a code class relation diagram meeting the reconstruction requirement of the source code; performing function marking on a plurality of functions contained in the source code to obtain function marking information; generating at least one code reconstruction request according to the source code, the reconstruction demand of the source code and the function mark information; a target code is obtained through a pre-trained code reconstruction model according to the code reconstruction request, and the code reconstruction model is obtained through training based on a GPT large language model: a target code framework is obtained through the pre-trained code reconstruction model according to the UML class diagram and the function marking information; and splicing the target code with the target code framework to obtain a reconstructed code of the source code.

The invention provides an intelligent software quality measurement method and system based on deep semantic fusion and dynamic weight, and the method comprises the steps: constructing a composite fuzzy matter element matrix of a software class diagram, and carrying out the dimensionless processing of the composite fuzzy matter element matrix, and obtaining an optimal membership matrix; calculating an initial static weight of the complexity measurement index of the software class diagram; optimizing the initial static weight into an instance-level dynamic weight, and obtaining a self-attention value of each complexity measurement index; introducing the self-attention numerical value into the optimal membership matrix, and constructing an enhanced weighted fuzzy compound element matrix in combination with the instance-level dynamic weight; and on the basis of the enhanced weighted fuzzy complex element matrix, a TOPSIS method is adopted, and a final complexity metric value of the software class diagram is calculated in combination with bidirectional projection closeness. The method can adapt to quality evaluation requirements of different types of software systems, and a high-precision, high-adaptability and interpretable intelligent measurement method is provided for software engineering quality management.

This invention discloses an adaptive software quality measurement method based on information geometry and reinforcement learning, comprising: acquiring original quality feature data of software class graphs; extracting low-dimensional manifold coordinates through information geometry mapping and sparse representation; reconstructing index values ​​and constructing composite fuzzy matter elements based on manifold coordinates; dimensionlessly transforming the matter elements to obtain an optimal membership matrix; determining initial weights based on the entropy weight method; iteratively optimizing the weights through a reinforcement learning adaptive feedback mechanism; and calculating the comprehensive evaluation value and outputting the measurement result using the MARCOS method. This invention achieves a breakthrough in software quality measurement from static to dynamic adaptation, significantly improving the accuracy and practicality of the measurement, and effectively overcoming the shortcomings of traditional methods in terms of high-dimensional complexity, evaluation fuzziness, and limitations of static weights.

Embodiments of the present disclosure provide a class diagram generation method and device, the method comprising: obtaining code compilation information of engineering code; determining, based on the code compilation information, a first source code file of the engineering code, and a second source code file and / or a macro associated with the first source code file, wherein the first source code file belongs to the engineering code, and the second source code file is a third-party code file; and generating a unified modeling language class diagram of the engineering code based on the first source code file, and the second source code file and / or the macro.

The embodiment of the invention provides a UML class diagram code automatic generation method and related equipment, and belongs to the technical field of software engineering and artificial intelligence crossing. The method comprises the following steps: analyzing an input UML class diagram into structured data; retrieving related code snippets as project contexts in a target project code library based on the data; constructing a cue word fusing the structured data, the item context, a predefined code generation thinking chain rule and few sample examples; and inputting the cue word into a large language model to generate a source code conforming to a project specification. According to the method, RAG (Retrieval Enhanced Generation) is combined with a structured thinking chain rule, the item context is effectively utilized, the coding logic of a human developer is simulated, the problems that codes generated in the prior art are disjointed with items, semantics are inconsistent, and large model illusion is caused are solved, and automatic generation from UML class diagrams to high-quality and integratable codes is realized.