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:content {:text "Jureczko et al. [2] used hierarchical clustering and K-means to cluster projects and then used the Kohonen neural network to differentiate whether a cluster was for defect predictio"},
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:content {:text "ect. The challenge is to build the model with a consistent distribution of data across pr"},
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:content {:text "Zimmermann et al. [3] believed that selecting source projects arbitrarily or based on the same domain was not practic"},
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:content {:text "Experiment was conducted on Windows operating system using Python programming language. The graph neural network model was built using PyTorch and torch-geometric package. The comparison experiments were also implemented using Pyth"},
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:content {:text "TABLE I. PROJECT INTRODUCTION"},
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:content {:text ". The discriminator will distinguish whether the nodes come from the source project or the target project, so in order to balance the training speed, it is necessary to adjust the learning rat"},
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:content {:text ". The settings of other parameters are default settings"},
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:content {:text "e set to default values without any specific modification"},
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:content {:text "The dataset is sourced from the Promise dataset, and six projects were selected for experimenta"},
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:content {:text "cts. For example, when Ant is the target project for defect prediction, Camel, Lucene, Synapse, Velocity, and Ivy are used as the source projects for traini"},
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:content {:text "ts. For example, when Ant is the target project for defect prediction, all graph structures of Camel, Lucene, Synapse, Velocity, and Ivy are used as the training set."},
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:content {:text "ws: firstly, the software in the source project and the target project are mapped into graph structures, and the community division algorithm is used to divide the graph structure into subgra"},
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:content {:text " 3. Firstly, multiple subgraphs are obtained by mapping the software through data preprocessing."},
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