Vincent J. Hellendoorn

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 38

Top Topics

Systematic Evaluation

Automatically Generate

Top Venues

ESEC/SIGSOFT FSE

Publications

Kamel Alrashedy, Vincent J. Hellendoorn, Alessandro Orso
Learning Defect Prediction from Unrealistic Data. SANER (2024)
Daye Nam, Andrew Macvean, Vincent J. Hellendoorn, Bogdan Vasilescu, Brad A. Myers
Using an LLM to Help With Code Understanding. ICSE (2024)
Aidan Z. H. Yang, Claire Le Goues, Ruben Martins, Vincent J. Hellendoorn
Large Language Models for Test-Free Fault Localization. ICSE (2024)
Nikitha Rao, Jason Tsay, Kiran Kate, Vincent J. Hellendoorn, Martin Hirzel
AI for Low-Code for AI. IUI (2024)
Aidan Z. H. Yang, Sophia Kolak, Vincent J. Hellendoorn, Ruben Martins, Claire Le Goues
Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models. CoRR (2024)
Md. Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, Vincent J. Hellendoorn
Memorization and generalization in neural code intelligence models. Inf. Softw. Technol. 153 (2023)
Nikitha Rao, Kush Jain, Uri Alon, Claire Le Goues, Vincent J. Hellendoorn
CAT-LM Training Language Models on Aligned Code And Tests. ASE (2023)
Manisha Mukherjee, Vincent J. Hellendoorn
Stack Over-Flowing with Results: The Case for Domain-Specific Pre-Training Over One-Size-Fits-All Models. CoRR (2023)
Nikitha Rao, Jason Tsay, Kiran Kate, Vincent J. Hellendoorn, Martin Hirzel
AI for Low-Code for AI. CoRR (2023)
Nikitha Rao, Kush Jain, Uri Alon, Claire Le Goues, Vincent J. Hellendoorn
CAT-LM: Training Language Models on Aligned Code And Tests. CoRR (2023)
Daye Nam, Andrew Macvean, Vincent J. Hellendoorn, Bogdan Vasilescu, Brad A. Myers
In-IDE Generation-based Information Support with a Large Language Model. CoRR (2023)
Aidan Z. H. Yang, Ruben Martins, Claire Le Goues, Vincent J. Hellendoorn
Large Language Models for Test-Free Fault Localization. CoRR (2023)
Kamel Alrashedy, Vincent J. Hellendoorn, Alessandro Orso
Learning Defect Prediction from Unrealistic Data. CoRR (2023)
Daye Nam, Brad A. Myers, Bogdan Vasilescu, Vincent J. Hellendoorn
Improving API Knowledge Discovery with ML: A Case Study of Comparable API Methods. ICSE (2023)
Machel Reid, Vincent J. Hellendoorn, Graham Neubig
DiffusER: Discrete Diffusion via Edit-based Reconstruction. CoRR (2022)
Nikitha Rao, Jason Tsay, Martin Hirzel, Vincent J. Hellendoorn
Comments on Comments: Where Code Review and Documentation Meet. MSR (2022)
Vincent J. Hellendoorn, Anand Ashok Sawant
The growing cost of deep learning for source code. Commun. ACM 65 (1) (2022)
Frank F. Xu, Uri Alon, Graham Neubig, Vincent J. Hellendoorn
A Systematic Evaluation of Large Language Models of Code. CoRR (2022)
Rajeswari Hita Kambhamettu, John Billos, Tomi Oluwaseun-Apo, Benjamin Gafford, Rohan Padhye, Vincent J. Hellendoorn
On the Naturalness of Fuzzer-Generated Code. MSR (2022)
David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent J. Hellendoorn, Daniel D. Johnson, Daniel Tarlow
A Library for Representing Python Programs as Graphs for Machine Learning. CoRR (2022)
Annibale Panichella, Sebastiano Panichella, Gordon Fraser, Anand Ashok Sawant, Vincent J. Hellendoorn
Test smells 20 years later: detectability, validity, and reliability. Empir. Softw. Eng. 27 (7) (2022)
Nikitha Rao, Jason Tsay, Martin Hirzel, Vincent J. Hellendoorn
Comments on Comments: Where Code Review and Documentation Meet. CoRR (2022)
Zimin Chen, Vincent J. Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, Subhodeep Moitra
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair. NeurIPS (2021)
Md. Rafiqul Islam Rabin, Vincent J. Hellendoorn, Mohammad Amin Alipour
Understanding Neural Code Intelligence Through Program Simplification. CoRR (2021)
Md. Rafiqul Islam Rabin, Vincent J. Hellendoorn, Mohammad Amin Alipour
Understanding neural code intelligence through program simplification. ESEC/SIGSOFT FSE (2021)
Md. Rafiqul Islam Rabin, Aftab Hussain, Vincent J. Hellendoorn, Mohammad Amin Alipour
Memorization and Generalization in Neural Code Intelligence Models. CoRR (2021)
Vincent J. Hellendoorn, Jason Tsay, Manisha Mukherjee, Martin Hirzel
Towards automating code review at scale. ESEC/SIGSOFT FSE (2021)
Toufique Ahmed, Premkumar T. Devanbu, Vincent J. Hellendoorn
Learning lenient parsing & typing via indirect supervision. Empir. Softw. Eng. 26 (2) (2021)
Frank F. Xu, Junxian He, Graham Neubig, Vincent J. Hellendoorn
Capturing Structural Locality in Non-parametric Language Models. CoRR (2021)
Yangruibo Ding, Baishakhi Ray, Premkumar T. Devanbu, Vincent J. Hellendoorn
Patching as Translation: the Data and the Metaphor. ASE (2020)
Yangruibo Ding, Baishakhi Ray, Premkumar T. Devanbu, Vincent J. Hellendoorn
Patching as Translation: the Data and the Metaphor. CoRR (2020)
Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber
Global Relational Models of Source Code. ICLR (2020)
Annibale Panichella, Sebastiano Panichella, Gordon Fraser, Anand Ashok Sawant, Vincent J. Hellendoorn
Revisiting Test Smells in Automatically Generated Tests: Limitations, Pitfalls, and Opportunities. ICSME (2020)
Vincent J. Hellendoorn, Premkumar T. Devanbu, Oleksandr Polozov, Mark Marron
Are My Invariants Valid? A Learning Approach. CoRR (2019)
Vincent J. Hellendoorn, Sebastian Proksch, Harald C. Gall, Alberto Bacchelli
When code completion fails: a case study on real-world completions. ICSE (2019)
Vincent J. Hellendoorn, Christian Bird, Earl T. Barr, Miltiadis Allamanis
Deep learning type inference. ESEC/SIGSOFT FSE (2018)
Vincent J. Hellendoorn, Premkumar T. Devanbu, Mohammad Amin Alipour
On the naturalness of proofs. ESEC/SIGSOFT FSE (2018)
Vincent J. Hellendoorn, Premkumar T. Devanbu
Are deep neural networks the best choice for modeling source code? ESEC/SIGSOFT FSE (2017)