| What do pre-trained code models know about code? A Karmakar, R Robbes 2021 36th IEEE/ACM International Conference on Automated Software …, 2021 | 91 | 2021 |
| Codex hacks hackerrank: Memorization issues and a framework for code synthesis evaluation A Karmakar, JA Prenner, M D'Ambros, R Robbes arXiv preprint arXiv:2212.02684, 2022 | 17 | 2022 |
| Mining software repositories with a collaborative heuristic repository H Babii, JA Prenner, L Stricker, A Karmakar, A Janes, R Robbes 2021 IEEE/ACM 43rd International Conference on Software Engineering: New …, 2021 | 6 | 2021 |
| Inspect: Intrinsic and systematic probing evaluation for code transformers A Karmakar, R Robbes IEEE Transactions on Software Engineering, 2023 | 4 | 2023 |
| JEMMA: An extensible Java dataset for ML4Code applications A Karmakar, M Allamanis, R Robbes Empirical Software Engineering 28 (2), 54, 2023 | 4 | 2023 |
| Establishing Benchmarks for Learning Program Representations. A Karmakar SATToSE, 2019 | 4 | 2019 |
| On the Intrinsic and Extrinsic Evaluation of Source Code Models A Karmakar Free University of Bozen-Bolzano, 2023 | | 2023 |
| Java Extensible dataset for Many ML4Code Applications A Karmakar, M Allamanis, R Robbes | | |
| GLUECode: A Benchmark for Source Code Machine Learning Models A Karmakar, JA Prenner, M Allamanis, R Robbes | | |
Romain RobbesCNRS Senior Scientist, LaBRI, Université de BordeauxVerified email at u-bordeaux.fr
