Software Maintainability Prediction with UML Class Diagram |
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Authors: | LIU Li ZHU Xiao-dong HAO Xue-liang |
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Institution: | 1. Department of Teaching and Research, Mechanized Infantry Academy, Shijiazhuang 050003, China 2. Maintenance Engineering Institute, Mechanical Engineering College, Shijiazhuang 050003, China |
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Abstract: | Software system can be classified into many function modules from the perspective of user. Unified modeling language( UML) class diagram of each function module was extracted,and design characteristic metrics which influenced software maintainability were selected based on UML class diagram.Choosing metrics of UML class diagram as predictors,and mean maintenance time of function module was regarded as software maintainability parameter. Software maintainability models were built by using back propagation( BP) neural network and radial basis function( RBF) neural network, respectively and were simulated by MATLAB. In order to evaluate the performance of models,the training results were analyzed and compared with leaveone-out cross-validation and model performance evaluation criterion. The result indicated that RBF arithmetic was superior to BP arithmetic in predicting software maintainability. |
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Keywords: | unified modeling language (UML) class diagram software maintainability back propagation (BP) neural network radial basis function (RBF) neural network |
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