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At present, deep learning has emerged as a powerful technique for automated feature generation, since deep learning architecture can effectively capture highly complicated nonlinear features.
Promise Repository Datasets For Defect Prediction Software Defect PredictionShow citation An Approach to Semantic and Structural Features Learning for Software Defect Prediction Shi Meilong, 1 Peng He, 1, 2 Haitao Xiao, 1 Huixin Li, 1 and Cheng Zeng 1 1 School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China 2 Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan 430062, China Show more Academic Editor: Chunlai Chai Received 20 Dec 2019 Revised 01 Feb 2020 Accepted 24 Feb 2020 Published 06 Apr 2020 Abstract Research on software defect prediction has achieved great success at modeling predictors.To build more accurate predictors, a number of hand-crafted features are proposed, such as static code features, process features, and social network features.
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