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The best of both worlds: integrating semantic features with expert features for defect prediction and localization.

, , , , , and . ESEC/SIGSOFT FSE, page 672-683. ACM, (2022)

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Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach., , , , , , , and . NeurIPS, (2022)Class-Disentanglement and Applications in Adversarial Detection and Defense., , , , and . NeurIPS, page 16051-16063. (2021)Unifying Defect Prediction, Categorization, and Repair by Multi-Task Deep Learning., , , , and . ASE, page 1980-1992. IEEE, (2023)Automatic Identification of Crash-inducing Smart Contracts., , , , , and . SANER, page 108-119. IEEE, (2023)The best of both worlds: integrating semantic features with expert features for defect prediction and localization., , , , , and . ESEC/SIGSOFT FSE, page 672-683. ACM, (2022)Multi-task Learning with Consistent Prediction for Efficient Breast Ultrasound Tumor Detection., , , , , and . BIBM, page 3201-3208. IEEE, (2022)Boosting Just-in-Time Defect Prediction with Specific Features of C/C++ Programming Languages in Code Changes., , , and . MSR, page 472-484. IEEE, (2023)Identity-Disentangled Adversarial Augmentation for Self-supervised Learning., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 25364-25381. PMLR, (2022)Domain-Class Correlation Decomposition for Generalizable Person Re-Identification., and . CoRR, (2021)Distinguishing Look-Alike Innocent and Vulnerable Code by Subtle Semantic Representation Learning and Explanation., , , , , and . ESEC/SIGSOFT FSE, page 1611-1622. ACM, (2023)