Hellendoorn, Vincent

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

A systematic evaluation of large language models of code

Xu, F. F., Alon, U., Neubig, G., & Hellendoorn, V. J. (2022) · Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming

Software Engineering ResearchTopic Modeling

Cited by 540

Using an LLM to Help With Code Understanding

Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., & Myers, B. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 487

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Are deep neural networks the best choice for modeling source code?

Hellendoorn, V. J., & Devanbu, P. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

Software Engineering ResearchNatural Language Processing Techniques

Cited by 292

Large Language Models for Test-Free Fault Localization

Yang, A. Z. H., Le Goues, C., Martins, R., & Hellendoorn, V. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 144

When Code Completion Fails: A Case Study on Real-World Completions

Hellendoorn, V. J., Proksch, S., Gall, H. C., & Bacchelli, A. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 76

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

CACHECA: A Cache Language Model Based Code Suggestion Tool

Franks, C., Tu, Z., Devanbu, P., & Hellendoorn, V. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 30