Vincent J. Hellendoorn

According to our database, Vincent J. Hellendoorn authored at least 36 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models.

Aidan Z. H. Yang, Sophia Kolak, Vincent J. Hellendoorn, Ruben Martins, Claire Le Goues

CoRR, 2024

Learning Defect Prediction from Unrealistic Data.

Kamel Alrashedy, Vincent J. Hellendoorn, Alessandro Orso

Proceedings of the IEEE International Conference on Software Analysis, 2024

AI for Low-Code for AI.

Nikitha Rao, Jason Tsay, Kiran Kate, Vincent J. Hellendoorn, Martin Hirzel

Proceedings of the 29th International Conference on Intelligent User Interfaces, 2024

Large Language Models for Test-Free Fault Localization.

Aidan Z. H. Yang, Claire Le Goues, Ruben Martins, Vincent J. Hellendoorn

Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, 2024

Using an LLM to Help With Code Understanding.

Daye Nam, Andrew Macvean, Vincent J. Hellendoorn, Bogdan Vasilescu, Brad A. Myers

Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, 2024

2023

Memorization and generalization in neural code intelligence models.

Md. Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn

Inf. Softw. Technol., 2023

In-IDE Generation-based Information Support with a Large Language Model.

Daye Nam, Andrew Macvean, Vincent J. Hellendoorn, Bogdan Vasilescu, Brad A. Myers

CoRR, 2023

Stack Over-Flowing with Results: The Case for Domain-Specific Pre-Training Over One-Size-Fits-All Models.

Manisha Mukherjee, Vincent J. Hellendoorn

CoRR, 2023

CAT-LM Training Language Models on Aligned Code And Tests.

Nikitha Rao, Kush Jain, Uri Alon, Claire Le Goues, Vincent J. Hellendoorn

Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, 2023

Improving API Knowledge Discovery with ML: A Case Study of Comparable API Methods.

Daye Nam, Brad A. Myers, Bogdan Vasilescu, Vincent J. Hellendoorn

Proceedings of the 45th IEEE/ACM International Conference on Software Engineering, 2023

DiffusER: Diffusion via Edit-based Reconstruction.

Machel Reid, Vincent Josua Hellendoorn, Graham Neubig

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Test smells 20 years later: detectability, validity, and reliability.

Annibale Panichella, Sebastiano Panichella, Gordon Fraser, Anand Ashok Sawant, Vincent J. Hellendoorn

Empir. Softw. Eng., 2022

DiffusER: Discrete Diffusion via Edit-based Reconstruction.

Machel Reid, Vincent J. Hellendoorn, Graham Neubig

CoRR, 2022

A Library for Representing Python Programs as Graphs for Machine Learning.

David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent J. Hellendoorn, Daniel D. Johnson, Daniel Tarlow

CoRR, 2022

The growing cost of deep learning for source code.

Vincent J. Hellendoorn, Anand Ashok Sawant

Commun. ACM, 2022

A systematic evaluation of large language models of code.

Frank F. Xu, Uri Alon, Graham Neubig, Vincent Josua Hellendoorn

Proceedings of the MAPS@PLDI 2022: 6th ACM SIGPLAN International Symposium on Machine Programming, 2022

Comments on Comments: Where Code Review and Documentation Meet.

Nikitha Rao, Jason Tsay, Martin Hirzel, Vincent J. Hellendoorn

Proceedings of the 19th IEEE/ACM International Conference on Mining Software Repositories, 2022

On the Naturalness of Fuzzer-Generated Code.

Rajeswari Hita Kambhamettu, John Billos, Tomi Oluwaseun-Apo, Benjamin Gafford, Rohan Padhye, Vincent J. Hellendoorn

Proceedings of the 19th IEEE/ACM International Conference on Mining Software Repositories, 2022

Capturing Structural Locality in Non-parametric Language Models.

Frank F. Xu, Junxian He, Graham Neubig, Vincent Josua Hellendoorn

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Learning lenient parsing & typing via indirect supervision.

Toufique Ahmed, Premkumar T. Devanbu, Vincent J. Hellendoorn

Empir. Softw. Eng., 2021

Understanding neural code intelligence through program simplification.

Md. Rafiqul Islam Rabin, Vincent J. Hellendoorn, Mohammad Amin Alipour

Proceedings of the ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021

Towards automating code review at scale.

Vincent J. Hellendoorn, Jason Tsay, Manisha Mukherjee, Martin Hirzel

Proceedings of the ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021

PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair.

Zimin Chen, Vincent J. Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Patching as Translation: the Data and the Metaphor.

Yangruibo Ding, Baishakhi Ray, Premkumar T. Devanbu, Vincent J. Hellendoorn

Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020

Revisiting Test Smells in Automatically Generated Tests: Limitations, Pitfalls, and Opportunities.

Annibale Panichella, Sebastiano Panichella, Gordon Fraser, Anand Ashok Sawant, Vincent J. Hellendoorn

Proceedings of the IEEE International Conference on Software Maintenance and Evolution, 2020

Global Relational Models of Source Code.

Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Are My Invariants Valid? A Learning Approach.

Vincent J. Hellendoorn, Premkumar T. Devanbu, Oleksandr Polozov, Mark Marron

CoRR, 2019

When code completion fails: a case study on real-world completions.

Vincent J. Hellendoorn, Sebastian Proksch, Harald C. Gall, Alberto Bacchelli

Proceedings of the 41st International Conference on Software Engineering, 2019

2018

On the naturalness of proofs.

Vincent J. Hellendoorn, Premkumar T. Devanbu, Mohammad Amin Alipour

Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2018

Deep learning type inference.

Vincent J. Hellendoorn, Christian Bird, Earl T. Barr, Miltiadis Allamanis

Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2018

2017

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

Vincent J. Hellendoorn, Premkumar T. Devanbu

Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, 2017

Perceived language complexity in GitHub issue discussions and their effect on issue resolution.

David Kavaler, Sasha Sirovica, Vincent J. Hellendoorn, Raúl Aranovich, Vladimir Filkov

Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, 2017

2016

On the "naturalness" of buggy code.

Baishakhi Ray, Vincent J. Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, Premkumar T. Devanbu

Proceedings of the 38th International Conference on Software Engineering, 2016

2015

On the "Naturalness" of Buggy Code.

Baishakhi Ray, Vincent J. Hellendoorn, Zhaopeng Tu, Connie Nguyen, Saheel Godhane, Alberto Bacchelli, Premkumar T. Devanbu

CoRR, 2015

Will They Like This? Evaluating Code Contributions with Language Models.

Vincent J. Hellendoorn, Premkumar T. Devanbu, Alberto Bacchelli

Proceedings of the 12th IEEE/ACM Working Conference on Mining Software Repositories, 2015

CACHECA: A Cache Language Model Based Code Suggestion Tool.

Christine Franks, Zhaopeng Tu, Premkumar T. Devanbu, Vincent J. Hellendoorn

Proceedings of the 37th IEEE/ACM International Conference on Software Engineering, 2015