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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)CoCoNuT: combining context-aware neural translation models using ensemble for program repair., , , , , and . ISSTA, page 101-114. ACM, (2020)CURE: Code-Aware Neural Machine Translation for Automatic Program Repair., , and . ICSE, page 1161-1173. IEEE, (2021)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)Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance., , , , , , , and . ASE, page 771-783. IEEE, (2020)EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries., , , , and . ICSE, page 798-810. ACM, (2022)KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair., , , , , and . ICSE, page 1251-1263. IEEE, (2023)Impact of Code Language Models on Automated Program Repair., , , and . ICSE, page 1430-1442. IEEE, (2023)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)