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Feature envy is one of the well-recognized code smells that should be removed by software refactoring. A major challenge in feature envy detection is that traditional approaches are less accurate whereas deep learning-based approaches are suffering from the lack of high-quality large-scale training data. Although existing refactoring detection tools could be employed to discover real-world feature envy examples, the noise (i.e., false positives) within the resulting data could significantly influence the quality of the training data as well as the performance of the models trained on the data. To this end, in this paper, we propose a sequence of heuristic rules and a decision tree-based classifier to filter out false positives reported by state-of-the-art refactoring detection tools. The data after filtering serve as the positive items in the requested training data. From the same subject projects, we randomly select methods that …