Allamanis, Miltiadis

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Husain, H., Wu, H.-H., Gazit, T., Allamanis, M., & Brockschmidt, M. (2019) · arXiv

Software Engineering ResearchInformation Retrieval and Search Behavior

Cited by 483

The adverse effects of code duplication in machine learning models of code

Allamanis, M. (2019) · Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 284

A Survey of Machine Learning for Big Code and Naturalness

Allamanis, M., Barr, E. T., Devanbu, P., & Sutton, C. (2019) · ACM Computing Surveys

Software Engineering ResearchLogic, programming, and type systems

Cited by 150

Fast and Memory-Efficient Neural Code Completion

Svyatkovskiy, A., Lee, S., Hadjitofi, A., Riechert, M., Franco, J. V., & Allamanis, M. (2021) · 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 95

SmartPaste: Learning to Adapt Source Code

Allamanis, M., & Brockschmidt, M. (2017) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 13