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
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
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
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
Notebook for navigating code using machine learning and flow analysis
BP Kroth, JJ Henkel
US Patent 11,816,456, 2023
NL2SQL is a solved problem... Not!
A Floratou, F Psallidas, F Zhao, S Deep, G Hagleither, W Tan, J Cahoon, ...
CIDR, 2024
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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