| 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 | 258 | 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 | 220* | 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 | 132 | 2018 |
| Global Relational Models of Source Code VJ Hellendoorn, Maniatis, P, R Singh, C Sutton, D Bieber International Conference on Learning Representations, 2020 | 99 | 2020 |
| 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 | 72 | 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 | 58 | 2015 |
| 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 | 39* | 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 | 25 | 2017 |
| On the naturalness of proofs VJ Hellendoorn, PT Devanbu, MA Alipour Proceedings of the 2018 26th ACM Joint Meeting on European Software …, 2018 | 11 | 2018 |
| Learning Lenient Parsing & Typing via Indirect Supervision T Ahmed, P Devanbu, VJ Hellendoorn Empirical Software Engineering 26 (2), 1-31, 2021 | 9 | 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 | 9 | 2020 |
| 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 | 9 | 2020 |
| Understanding Neural Code Intelligence Through Program Simplification M Rafiqul Islam Rabin, VJ Hellendoorn, MA Alipour arXiv e-prints, arXiv: 2106.03353, 2021 | 8* | 2021 |
| 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 | 5 | 2021 |
| A systematic evaluation of large language models of code FF Xu, U Alon, G Neubig, VJ Hellendoorn arXiv preprint arXiv:2202.13169, 2022 | 3 | 2022 |
| 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 | 2 | 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 | 2* | 2021 |
| Are my invariants valid? a learning approach VJ Hellendoorn, PT Devanbu, O Polozov, M Marron arXiv preprint arXiv:1903.06089, 2019 | 2 | 2019 |
| The Growing Cost of Deep Learning for Source Code VJ Hellendoorn, AA Sawant Communications of the ACM 65 (1), 31-33, 2022 | 1 | 2022 |
| Capturing Structural Locality in Non-parametric Language Models FF Xu, J He, G Neubig, VJ Hellendoorn arXiv preprint arXiv:2110.02870, 2021 | 1 | 2021 |
Prem DevanbuDistinguished Professor (பேராசிரியர்), ACM FellowVerified email at ucdavis.edu
