Jordan Henkel
Jordan Henkel
Senior Scientist, Microsoft
Verified email at - Homepage
Cited by
Cited by
Code vectors: understanding programs through embedded abstracted symbolic traces
J Henkel, SK Lahiri, B Liblit, T Reps
Proceedings of the 2018 26th ACM Joint Meeting on European Software …, 2018
Semantic Robustness of Models of Source Code
J Henkel, G Ramakrishnan, Z Wang, A Albarghouthi, S Jha, T Reps
IEEE International Conference on Software Analysis, Evolution and Reengineering, 2022
Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML. NET Pipelines
F Psallidas, Y Zhu, B Karlas, J Henkel, M Interlandi, S Krishnan, B Kroth, ...
ACM SIGMOD Record 51 (2), 30-37, 2022
Learning from, Understanding, and Supporting DevOps Artifacts for Docker
J Henkel, C Bird, SK Lahiri, T Reps
Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020
Shipwright: A Human-in-the-Loop System for Dockerfile Repair
J Henkel, D Silva, L Teixeira, M d’Amorim, T Reps
2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE …, 2021
A Dataset of Dockerfiles
J Henkel, C Bird, SK Lahiri, T Reps
Proceedings of the 17th International Conference on Mining Software …, 2020
NL2SQL is a solved problem... Not!
A Floratou, F Psallidas, F Zhao, S Deep, G Hagleither, W Tan, J Cahoon, ...
CIDR, 2024
Notebook for navigating code using machine learning and flow analysis
BP Kroth, JJ Henkel
US Patent 11,816,456, 2023
From Words to Code: Harnessing Data for Program Synthesis from Natural Language
A Khatry, J Cahoon, J Henkel, S Deep, V Emani, A Floratou, S Gulwani, ...
arXiv preprint arXiv:2305.01598, 2023
ReAcTable: Enhancing ReAct for Table Question Answering
Y Zhang, J Henkel, A Floratou, J Cahoon, S Deep, JM Patel
arXiv preprint arXiv:2310.00815, 2023
The Need for Tabular Representation Learning: An Industry Perspective
J Cahoon, A Savelieva, AC Mueller, A Floratou, C Curino, H Patel, ...
NeurIPS 2022 First Table Representation Workshop, 2022
Enabling Open-World Specification Mining via Unsupervised Learning
J Henkel, SK Lahiri, B Liblit, T Reps
arXiv preprint arXiv:1904.12098, 2019
Learning from Code and Non-code Artifacts
J Henkel
The University of Wisconsin-Madison, 2022
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