Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2155 | 2023 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 676 | 2024 |
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 | 623 | 2022 |
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 | 408 | 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 | 361* | 2016 |
Global Relational Models of Source Code VJ Hellendoorn, Maniatis, P, R Singh, C Sutton, D Bieber International Conference on Learning Representations, 2020 | 268 | 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 | 246 | 2018 |
Using an llm to help with code understanding D Nam, A Macvean, V Hellendoorn, B Vasilescu, B Myers Proceedings of the IEEE/ACM 46th International Conference on Software …, 2024 | 107 | 2024 |
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 | 98 | 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 | 93 | 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 | 82 | 2015 |
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 | 67 | 2020 |
Understanding Neural Code Intelligence Through Program Simplification M Rafiqul Islam Rabin, VJ Hellendoorn, MA Alipour arXiv e-prints, arXiv: 2106.03353, 2021 | 62* | 2021 |
Large language models for test-free fault localization AZH Yang, C Le Goues, R Martins, V Hellendoorn Proceedings of the 46th IEEE/ACM International Conference on Software …, 2024 | 55 | 2024 |
Diffuser: Diffusion via edit-based reconstruction M Reid, VJ Hellendoorn, G Neubig The Eleventh International Conference on Learning Representations, 2023 | 54* | 2023 |
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 | 42* | 2021 |
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 | 37 | 2017 |
Patch generation with language models: Feasibility and scaling behavior SD Kolak, R Martins, C Le Goues, VJ Hellendoorn Deep Learning for Code Workshop, 2022 | 35 | 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 | 35 | 2020 |
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 | 34 | 2021 |