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Cross-project software defects prediction using fuzzy embedding and deep learning
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DOI：10.1016/j.infsof.2025.107968.png)
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Abstract
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Cross-project defect prediction (CPDP) aims to predict software defects in a target project using data from related source projects, especially when defect data for the target project is limited or unavailable. A key challenge in CPDP is data heterogeneity and distributional differences across projects, which often lead to poor performance and unreliable predictions.
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