Vincent Hellendoorn
TitleCited byYear
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
1152016
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
962017
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
462015
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
292015
Deep learning type inference
VJ Hellendoorn, C Bird, ET Barr, M Allamanis
Proceedings of the 2018 26th acm joint meeting on european software …, 2018
212018
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
92017
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
32019
On the naturalness of proofs
VJ Hellendoorn, PT Devanbu, MA Alipour
Proceedings of the 2018 26th ACM Joint Meeting on European Software …, 2018
12018
Learning Lenient Parsing & Typing via Indirect Supervision
T Ahmed, V Hellendoorn, P Devanbu
arXiv preprint arXiv:1910.05879, 2019
2019
Are My Invariants Valid? A Learning Approach
VJ Hellendoorn, PT Devanbu, O Polozov, M Marron
arXiv preprint arXiv:1903.06089, 2019
2019
Empirical Software Linguistics: An Investigation of Code Reviews, Recommendations and Faults
VJ Hellendoorn
2015
Artifact Evaluation Committee of ICSE 2019
S Abrahão, H Bagheri, D Benavides, K Blincoe, C Casalnuovo, A Filieri, ...
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