| 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 | 371 | 2017 |
| 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 | 368 | 2022 |
| 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 | 323* | 2016 |
| Global Relational Models of Source Code VJ Hellendoorn, Maniatis, P, R Singh, C Sutton, D Bieber International Conference on Learning Representations, 2020 | 244 | 2020 |
| Deep learning type inference VJ Hellendoorn, C Bird, ET Barr, M Allamanis Proceedings of the 2018 26th acm joint meeting on european software …, 2018 | 224 | 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 | 94 | 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 | 82 | 2019 |
| 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 | 79 | 2015 |
| Understanding Neural Code Intelligence Through Program Simplification M Rafiqul Islam Rabin, VJ Hellendoorn, MA Alipour arXiv e-prints, arXiv: 2106.03353, 2021 | 48* | 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 | 44 | 2020 |
| Diffuser: Diffusion via edit-based reconstruction M Reid, VJ Hellendoorn, G Neubig The Eleventh International Conference on Learning Representations, 2022 | 32* | 2022 |
| 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 | 32 | 2017 |
| 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 | 31 | 2021 |
| 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 | 30* | 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 | 25 | 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 | 25 | 2020 |
| Learning Lenient Parsing & Typing via Indirect Supervision T Ahmed, P Devanbu, VJ Hellendoorn Empirical Software Engineering 26 (2), 1-31, 2021 | 22 | 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 | 21 | 2021 |
| The Growing Cost of Deep Learning for Source Code VJ Hellendoorn, AA Sawant Communications of the ACM 65 (1), 31-33, 2022 | 19 | 2022 |
| 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 | 18 | 2022 |
Prem DevanbuDistinguished Research Professor (பேராசிரியர்), ACM FellowVerified email at ucdavis.edu
