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Xinli Yang
Researcher at Zhejiang University
Publications - 8
Citations - 632
Xinli Yang is an academic researcher from Zhejiang University. The author has contributed to research in topics: Software bug & Deep learning. The author has an hindex of 6, co-authored 7 publications receiving 455 citations.
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Proceedings ArticleDOI
Deep Learning for Just-in-Time Defect Prediction
TL;DR: An approach Deeper is proposed which leverages deep learning techniques to predict defect-prone changes by leveraging a deep belief network algorithm and a machine learning classifier is built on the selected features.
Journal ArticleDOI
TLEL: A two-layer ensemble learning approach for just-in-time defect prediction
TL;DR: A two-layer ensemble learning approach TLEL which leverages decision tree and ensemble learning to improve the performance of just-in-time defect prediction and can achieve a substantial and statistically significant improvement over the state-of-the-art methods.
Proceedings ArticleDOI
Combining Word Embedding with Information Retrieval to Recommend Similar Bug Reports
TL;DR: The approach combines a traditional information retrieval technique and a word embedding technique, and takes bug titles and descriptions as well as bug product and component information into consideration, and improves the performance of NextBug statistically significantly and substantially for both projects.
Journal ArticleDOI
Characterizing malicious Android apps by mining topic-specific data flow signatures
TL;DR: A topic-specific approach to malware comprehension based on app descriptions and data-flow information, which is efficient in highlighting the malicious behavior, and thus can help in characterizing malware.
Proceedings ArticleDOI
Automated Identification of High Impact Bug Reports Leveraging Imbalanced Learning Strategies
TL;DR: The effectiveness of various imbalanced learning strategies built upon a number of well-known classification algorithms are investigated and under-sampling is the best im balanced learning strategy with naive Bayes multinominal for high impact bug identification.