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Vincent Hellendoorn
Vincent Hellendoorn
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Are deep neural networks the best choice for modeling source code?
VJ Hellendoorn, P Devanbu
Proceedings of the 2017 11th Joint meeting on foundations of software …, 2017
3632017
On the "naturalness" of buggy code
B Ray, V Hellendoorn, S Godhane, Z Tu, A Bacchelli, P Devanbu
Software Engineering (ICSE), 2016 IEEE/ACM 38th International Conference on …, 2016
315*2016
A systematic evaluation of large language models of code
FF Xu, U Alon, G Neubig, VJ Hellendoorn
Proceedings of the 6th ACM SIGPLAN International Symposium on Machine …, 2022
3132022
Global Relational Models of Source Code
VJ Hellendoorn, Maniatis, P, R Singh, C Sutton, D Bieber
International Conference on Learning Representations, 2020
2412020
Deep learning type inference
VJ Hellendoorn, C Bird, ET Barr, M Allamanis
Proceedings of the 2018 26th acm joint meeting on european software …, 2018
2202018
Will they like this? evaluating code contributions with language models
VJ Hellendoorn, PT Devanbu, A Bacchelli
2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, 157-167, 2015
952015
Cacheca: A cache language model based code suggestion tool
C Franks, Z Tu, P Devanbu, V Hellendoorn
2015 IEEE/ACM 37th IEEE International Conference on Software Engineering 2 …, 2015
782015
When code completion fails: A case study on real-world completions
VJ Hellendoorn, S Proksch, HC Gall, A Bacchelli
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE …, 2019
76*2019
Understanding Neural Code Intelligence Through Program Simplification
M Rafiqul Islam Rabin, VJ Hellendoorn, MA Alipour
arXiv e-prints, arXiv: 2106.03353, 2021
47*2021
Patching as translation: the data and the metaphor
Y Ding, B Ray, P Devanbu, VJ Hellendoorn
Proceedings of the 35th IEEE/ACM International Conference on Automated …, 2020
462020
PLUR: A unifying, graph-based view of program learning, understanding, and repair
Z Chen, VJ Hellendoorn, P Lamblin, P Maniatis, PA Manzagol, D Tarlow, ...
Advances in Neural Information Processing Systems 34, 23089-23101, 2021
332021
Perceived language complexity in GitHub issue discussions and their effect on issue resolution
D Kavaler, S Sirovica, V Hellendoorn, R Aranovich, V Filkov
2017 32nd IEEE/ACM International Conference on Automated Software …, 2017
332017
Diffuser: Diffusion via edit-based reconstruction
M Reid, VJ Hellendoorn, G Neubig
The Eleventh International Conference on Learning Representations, 2022
31*2022
Revisiting test smells in automatically generated tests: limitations, pitfalls, and opportunities
A Panichella, S Panichella, G Fraser, AA Sawant, VJ Hellendoorn
2020 IEEE international conference on software maintenance and evolution …, 2020
262020
Memorization and Generalization in Neural Code Intelligence Models
M Rafiqul Islam Rabin, A Hussain, MA Alipour, VJ Hellendoorn
arXiv e-prints, arXiv: 2106.08704, 2021
25*2021
Patch generation with language models: Feasibility and scaling behavior
SD Kolak, R Martins, C Le Goues, VJ Hellendoorn
Deep Learning for Code Workshop, 2022
232022
Towards Automating Code Review at Scale
VJ Hellendoorn, J Tsay, M Mukherjee, M Hirzel
Proceedings of the 29th ACM Joint Meeting on European Software Engineering …, 2021
222021
Learning Lenient Parsing & Typing via Indirect Supervision
T Ahmed, P Devanbu, VJ Hellendoorn
Empirical Software Engineering 26 (2), 1-31, 2021
212021
The Growing Cost of Deep Learning for Source Code
VJ Hellendoorn, AA Sawant
Communications of the ACM 65 (1), 31-33, 2022
182022
Test smells 20 years later: detectability, validity, and reliability
A Panichella, S Panichella, G Fraser, AA Sawant, VJ Hellendoorn
Empirical Software Engineering 27 (7), 170, 2022
152022
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Artikelen 1–20