| 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 | 201 | 2017 |
| 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 | 192 | 2016 |
| Deep learning type inference VJ Hellendoorn, C Bird, ET Barr, M Allamanis Proceedings of the 2018 26th acm joint meeting on european software …, 2018 | 91 | 2018 |
| 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 | 68 | 2015 |
| 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 | 52 | 2015 |
| Global Relational Models of Source Code VJ Hellendoorn, Maniatis, P, R Singh, C Sutton, D Bieber International Conference on Learning Representations, 2020 | 48 | 2020 |
| 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 | 25 | 2019 |
| 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 | 19 | 2017 |
| On the naturalness of proofs VJ Hellendoorn, PT Devanbu, MA Alipour Proceedings of the 2018 26th ACM Joint Meeting on European Software …, 2018 | 7 | 2018 |
| 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 | 5 | 2020 |
| Learning lenient parsing & typing via indirect supervision T Ahmed, P Devanbu, VJ Hellendoorn Empirical Software Engineering 26 (2), 1-31, 2021 | 3 | 2021 |
| Patching as Translation: the Data and the Metaphor Y Ding, B Ray, P Devanbu, VJ Hellendoorn 2020 35th IEEE/ACM International Conference on Automated Software …, 2020 | 2 | 2020 |
| Are my invariants valid? a learning approach VJ Hellendoorn, PT Devanbu, O Polozov, M Marron arXiv preprint arXiv:1903.06089, 2019 | 2 | 2019 |
| Understanding Neural Code Intelligence Through Program Simplification M Rafiqul Islam Rabin, VJ Hellendoorn, MA Alipour arXiv e-prints, arXiv: 2106.03353, 2021 | 1* | 2021 |
| Memorization and Generalization in Neural Code Intelligence Models M Rafiqul Islam Rabin, A Hussain, VJ Hellendoorn, MA Alipour arXiv e-prints, arXiv: 2106.08704, 2021 | | 2021 |
| Learning to Infer Run-Time Invariants from Source code VJ Hellendoorn, P Devanbu, A Polozov, M Marron | | 2020 |
| Machine Learning and the Science of Software Engineering VJ Hellendoorn University of California, Davis, 2020 | | 2020 |
| Empirical Software Linguistics: An Investigation of Code Reviews, Recommendations and Faults VJ Hellendoorn | | 2015 |
| Project Jackal Final Report R Van Heest, R Hartog, V Hellendoorn, AA Sawant | | 2013 |
| Artifact Evaluation Committee of ICSE 2019 S Abrahão, H Bagheri, D Benavides, K Blincoe, C Casalnuovo, A Filieri, ... | | |
Prem DevanbuDistinguished Professor (பேராசிரியர்), UC Davis.Verified email at ucdavis.edu
