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CoCoNuT: combining context-aware neural translation models using ensemble for program repair.

, , , , , and . ISSTA, page 101-114. ACM, (2020)

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Effective detection of vulnerable and malicious browser extensions., , , and . Comput. Secur., (2014)CURE: Code-Aware Neural Machine Translation for Automatic Program Repair., , and . ICSE, page 1161-1173. IEEE, (2021)CoCoNuT: combining context-aware neural translation models using ensemble for program repair., , , , , and . ISSTA, page 101-114. ACM, (2020)Machine Learning for Software Dependability.. University of Waterloo, Ontario, Canada, (2020)base-search.net (ftunivwaterloo:oai:uwspace.uwaterloo.ca:10012/16501).How Effective Are Neural Networks for Fixing Security Vulnerabilities., , , , , , , and . ISSTA, page 1282-1294. ACM, (2023)ENCORE: Ensemble Learning using Convolution Neural Machine Translation for Automatic Program Repair., , , , and . CoRR, (2019)On the correctness of electronic documents: studying, finding, and localizing inconsistency bugs in PDF readers and files., , , , and . Empir. Softw. Eng., 23 (6): 3187-3220 (2018)Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training., , , , , , , , and . NeurIPS, page 30211-30227. (2021)CRADLE: cross-backend validation to detect and localize bugs in deep learning libraries., , , and . ICSE, page 1027-1038. IEEE / ACM, (2019)Impact of Code Language Models on Automated Program Repair., , , and . ICSE, page 1430-1442. IEEE, (2023)