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- W4288426705 abstract "With the outbreak of information technology, software development companies provide personalized and comprehensive software systems for users' needs, providing many conveniences to their lives. As the basis of software development, requirements run through the whole process of software or system development and determine the quality of software projects. Requirements traceability is applicable to a variety of development scenarios and optimizes the development process. Establishing a complete requirements traceability can effectively speed up the development process and reduce development problems, which has important practical significance. Therefore, in order to improve the efficiency and accuracy of requirements traceability and facilitate the display of traceability results, an entry traceability algorithm based on requirement knowledge graph is proposed. The model uses BERT+BiLSTM+CRF to extract entities to form triples, and uses the pointer generation network to supplement the entries lacking titles to form the knowledge graph. Combining the theory in complex networks, the importance of entities is sorted, and the similarity between entities in the document knowledge graph is calculated by restarting random walk algorithm. Extract subgraphs related to the target entry according to the characteristics of the nodes and a breadth-first algorithm. Finally, the graph kernel method in the graph matching algorithm is used to calculate the structural similarity and entity similarity between the related entry and the target entry, and determine the relationship. Through the experimental analysis of real requirements documents, the results show that the method can quickly and accurately trace the requirements of the set of input entries." @default.
- W4288426705 created "2022-07-29" @default.
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- W4288426705 date "2022-07-28" @default.
- W4288426705 modified "2023-09-29" @default.
- W4288426705 title "Entry traceability technology based on requirement knowledge graph" @default.
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- W4288426705 doi "https://doi.org/10.1117/12.2642585" @default.
- W4288426705 hasPublicationYear "2022" @default.
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