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Machine Learning for Software Dependability.

. University of Waterloo, Ontario, Canada, (2020)base-search.net (ftunivwaterloo:oai:uwspace.uwaterloo.ca:10012/16501).

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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)How Effective Are Neural Networks for Fixing Security Vulnerabilities., , , , , , , and . ISSTA, page 1282-1294. ACM, (2023)Machine Learning for Software Dependability.. University of Waterloo, Ontario, Canada, (2020)base-search.net (ftunivwaterloo:oai:uwspace.uwaterloo.ca:10012/16501).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)CRADLE: cross-backend validation to detect and localize bugs in deep learning libraries., , , and . ICSE, page 1027-1038. IEEE / ACM, (2019)Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training., , , , , , , , and . NeurIPS, page 30211-30227. (2021)Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance., , , , , , , and . ASE, page 771-783. IEEE, (2020)