Identification of refactoring opportunities for source code based on class association relationships

In order to deal with the complex association relationships between classes in an object-oriented software system,a novel approach for identifying refactoring opportunities is proposed.The approach can be used to detect complex and duplicated many-to-many association relationships in source code,and to provide guidance for further refactoring.In the approach,source code is first transformed to an abstract syntax tree from which all data members of each class are extracted,then each class is characterized in connection with a set of association classes saving its data members.Next,classes in common associations are obtained by comparing different association classes sets in integrated analysis.Finally,on condition of pre-defined thresholds,all class sets in candidate for refactoring and their common association classes are saved and exported.This approach is tested on 4 projects.The results show that the precision is over 96%when the threshold is 3,and 100%when the threshold is 4.Meanwhile,this approach has good execution efficiency as the execution time taken for a project with more than 500 classes is less than 4 s,which also indicates that it can be applied to projects of different scales to identify their refactoring opportunities effectively.

Effects of Selenium on Fusarium Growth and Associated Fermentation Products and the Relationship with Chondrocyte Viability

This study determined the effects of selenium on the growth of Fusorium strains and the effects of products extracted from the fungal cultures on relevant indicators of chondrocytes injury.

Design parameter optimization method for a prestressed steel structure driven by multi-factor coupling

To achieve efficient structural design,it is crucial to reduce the cost of materials while ensuring structural safety.This study proposes an optimization method for design parameters(DPs)in a prestressed steel structure driven by multi-factor coupling.To accomplish this,a numerical proxy model of prestressed steel structures is established with integration of DPs and mechanical parameters(MPs).A data association-parameter analysis-optimization selection system is established.A correlation between DPs and MPs is established using a back propagation(BP)neural network.This correlation provides samples for parameter analysis and optimization selection.MPs are used to characterize the safety of the structure.Based on the safety grade analysis,the key DPs that affect the mechanical properties of the structure are obtained.A mapping function is created to match the MPs and the key DPs.The optimal structural DPs are obtained by setting structural materials as the optimization objective and safety energy as the constraint condition.The theoretical model is applied to an 80-m-span gymnasium and verified with a scale test physical model.The MPs obtained using the proposed method are in good agreement with the experimental results.Compared with the traditional design method,the design cycle can be shortened by more than 90%.Driven by the optimal selection method,a saving of more than 20% can be achieved through reduction of structural material quantities.Moreover,the cross-sectional dimensions of radial cables have a substantial influence on vertical displacement.The initial tension and cross-sectional size of the upper radial cable exhibit the most pronounced impact on the stress distribution in that cable.The initial tension and cross-sectional size of the lower radial cable hold the greatest sway over the stress distribution in that cable.