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Automatic detection of software bugs is a critical task in software security.
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Y. Zhou and A. Sharma, “Automated identification of security issues from commit messages and bug reports,” in Proceedings of the 2017 11th joint meeting on foundations of software engineering , 2017, pp. 914–919
2017
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2017
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A. Habib and M. Pradel, “How many of all bugs do we find? a study of static bug detectors,” in 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2018, pp. 317–328
2018
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2018
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R. Patgiri, “A taxonomy on big data: Survey,” arXiv preprint arXiv:1808.08474 , 2018
2018
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2018
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2018
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2021
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L. Jia, H. Zhong, X. Wang, L. Huang, and X. Lu, “The symptoms, causes, and repairs of bugs inside a deep learning library,” Journal of Systems and Software , vol. 177, p. 110935, 2021
2021
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Q. Shen, H. Ma, J. Chen, Y. Tian, S.-C. Cheung, and X. Chen, “A comprehensive study of deep learning compiler bugs,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 968–980
2021
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Z. Li, D. Zou, S. Xu, H. Jin, Y. Zhu, and Z. Chen, “Sysevr: A framework for using deep learning to detect software vulnerabilities,” IEEE Transactions on Dependable and Secure Computing , 2021
2021
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2022
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S. Lipp, S. Banescu, and A. Pretschner, “An empirical study on the effectiveness of static c code analyzers for vulnerability detection,” in Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis , 2022, pp. 544–555
2022
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Y. Lv, B. Liu, J. Zhang, Y. Dai, A. Li, and T. Zhang, “Semi-supervised active salient object detection,” Pattern Recognition , vol. 123, p. 108364, 2022
2022
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M. Gu, H. Feng, H. Sun, P. Liu, Q. Yue, J. Hu, C. Cao, and Y. Zhang, “Hierarchical attention network for interpretable and fine-grained vulnerability detection,” in IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . IEEE, 2022, pp. 1–6
2022
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D. Zou, Y. Hu, W. Li, Y. Wu, H. Zhao, and H. Jin, “mvulpreter: A multi-granularity vulnerability detection system with interpretations,” IEEE Transactions on Dependable and Secure Computing , 2022
2022
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S. Pujar, Y. Zheng, L. Buratti, B. Lewis, A. Morari, J. Laredo, K. Postlethwait, and C. Görn, “Varangian: a git bot for augmented static analysis,” in Proceedings of the 19th International Conference on Mining Software Repositories , 2022, pp. 766–767
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K. Umann and Z. Porkoláb, “Detecting uninitialized variables in c++ with the clang static analyzer,” Acta Cybernetica , vol. 25, no. 4, pp. 923–940, 2022
2022
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P. G. Szécsi, G. Horváth, and Z. Porkoláb, “Improved loop execution modeling in the clang static analyzer,” Acta Cybernetica , vol. 25, no. 4, pp. 909–921, 2022
2022
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H. Aslanyan, Z. Gevorgyan, R. Mkoyan, H. Movsisyan, V. Sahakyan, and S. Sargsyan, “Static analysis methods for memory leak detection: A survey,” in 2022 Ivannikov Memorial Workshop (IVMEM) . IEEE, 2022, pp. 1–6
2022
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2022
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J. Chen, C. Zhang, S. Cai, L. Zhang, and L. Ma, “A memory-related vulnerability detection approach based on vulnerability model with petri net,” Journal of Logical and Algebraic Methods in Programming , vol. 132, p. 100859, 2023
2023
Closest in time.
S. Mehrpour and T. D. LaToza, “Can static analysis tools find more defects? a qualitative study of design rule violations found by code review,” Empirical Software Engineering , vol. 28, no. 1, p. 5, 2023
2023
Closest in time.