Material demand forecasting has a profound impact on the supply chain and is an important prerequisite for manufacturing enterprises to produce. In order to accurately predict the material demand of... Show More

Material demand forecasting has a profound impact on the supply chain and is an important prerequisite for manufacturing enterprises to produce. In order to accurately predict the material demand of enterprises, this paper proposes a material demand forecasting algorithm based on multi-dimensional feature fusion (DFMF). Secondly, in order to obtain the spatial features, the vector representation of the relevant materials of a material is obtained through the attention mechanism. Then, the authors aggregate the relevant material representation and material vector representation of materials to obtain the final material vector representation through aggregation function. Then the final material vector representation under different time scales is used as input, and the prediction value of material demand is obtained by using BP neural network. Finally, experiments show that the model can effectively obtain multi-dimensional features of materials for prediction, and the prediction results have high accuracy.

Sequence diagrams define functional requirements through use cases. However, their visual form limits their usability in the later stages of the development life cycle. This work proposes a method... Show More

Sequence diagrams define functional requirements through use cases. However, their visual form limits their usability in the later stages of the development life cycle. This work proposes a method to transform sequence diagrams into graph-based event sequence graphs, allowing the application of graph analysis methods and defining graph-based coverage criteria. This work explores these newfound abilities in two directions. The first is to use coverage criteria along with existing tests to measure their coverage levels, providing a metric of how well they address the scenarios defined in sequence diagrams. The second is to use coverage criteria to automatically generate effective and efficient acceptance test cases based on the scenarios defined in sequence diagrams. The transformation method is validated with over eighty non-trivial projects. The complete method is validated through a non-trivial example. The results show that the test cases generated with the proposed method are more effective at exposing faults and more efficient in test input size than user-generated test cases.