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Software defects heavily affect software's functionalities and may cause huge losses.
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2016
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2019
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Y. Wan, W. Zhao, H. Zhang, Y. Sui, G. Xu, and H. Jin, “What do they capture? a structural analysis of pre-trained language models for source code,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 2377–2388
2022
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2022
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2022
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 . Curran Associates, Inc., 2019, pp. 8024–8035. [Online]. Available: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
2019
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2020
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Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang et al. , “Codebert: A pre-trained model for programming and natural languages,” in Findings of the Association for Computational Linguistics: EMNLP 2020 , 2020, pp. 1536–1547
2020
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M. Wardat, W. Le, and H. Rajan, “Deeplocalize: Fault localization for deep neural networks,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 251–262
2021
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Z. Li, D. Zou, S. Xu, Z. Chen, Y. Zhu, and H. Jin, “Vuldeelocator: a deep learning-based fine-grained vulnerability detector,” IEEE Transactions on Dependable and Secure Computing , 2021
2021
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Y. Li, S. Wang, and T. N. Nguyen, “Vulnerability detection with fine-grained interpretations,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 292–303
2021
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S. Chakraborty, R. Krishna, Y. Ding, and B. Ray, “Deep learning based vulnerability detection: Are we there yet,” IEEE Transactions on Software Engineering , 2021
2021
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Y. Lou, Q. Zhu, J. Dong, X. Li, Z. Sun, D. Hao, L. Zhang, and L. Zhang, “Boosting coverage-based fault localization via graph-based representation learning,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 664–676
2021
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2021
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2023
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2023
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C. Ni, X. Yin, K. Yang, D. Zhao, Z. Xing, and X. Xia, “Distinguishing look-alike innocent and vulnerable code by subtle semantic representation learning and explanation,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 1611–1622
2023
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D. AI, “Deepseek coder: Let the code write itself,” https://github.com/deepseek-ai/DeepSeek-Coder , 2023
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Z. Liu, K. Liu, X. Xia, and X. Yang, “Towards more realistic evaluation for neural test oracle generation,” in Proceedings of the 32th International Symposium on Software Testing and Analysis . ACM, 2023
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Y. Zhao, K. Damevski, and H. Chen, “A systematic survey of just-in-time software defect prediction,” ACM Computing Surveys , vol. 55, no. 10, pp. 1–35, 2023
2023
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