CACHECA: A Cache Language Model Based Code Suggestion Tool. - Christine Franks; Zhaopeng Tu; Premkumar T. Devanbu; Vincent J. Hellendoorn (ICSE (2), 2015)

Will They Like This? Evaluating Code Contributions with Language Models. - Vincent J. Hellendoorn; Premkumar T. Devanbu; Alberto Bacchelli (MSR, 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)

On the "naturalness" of buggy code. - Baishakhi Ray; Vincent J. Hellendoorn; Saheel Godhane; Zhaopeng Tu; Alberto Bacchelli; Premkumar T. Devanbu (ICSE, 2016)

Perceived language complexity in GitHub issue discussions and their effect on issue resolution. - David Kavaler; Sasha Sirovica; Vincent J. Hellendoorn; Raúl Aranovich; Vladimir Filkov (ASE, 2017)

Are deep neural networks the best choice for modeling source code? - Vincent J. Hellendoorn; Premkumar T. Devanbu (ESEC/SIGSOFT FSE, 2017)

Deep learning type inference. - Vincent J. Hellendoorn; Christian Bird; Earl T. Barr; Miltiadis Allamanis (ESEC/SIGSOFT FSE, 2018)

On the naturalness of proofs. - Vincent J. Hellendoorn; Premkumar T. Devanbu; Mohammad Amin Alipour (ESEC/SIGSOFT FSE, 2018)

When code completion fails: a case study on real-world completions. - Vincent J. Hellendoorn; Sebastian Proksch 001; Harald C. Gall; Alberto Bacchelli (ICSE, 2019)

Are My Invariants Valid? A Learning Approach. - Vincent J. Hellendoorn; Premkumar T. Devanbu; Oleksandr Polozov; Mark Marron (CoRR, 2019)

Global Relational Models of Source Code. - Vincent J. Hellendoorn; Charles Sutton; Rishabh Singh; Petros Maniatis; David Bieber (ICLR, 2020)

Revisiting Test Smells in Automatically Generated Tests: Limitations, Pitfalls, and Opportunities. - Annibale Panichella; Sebastiano Panichella; Gordon Fraser 001; Anand Ashok Sawant; Vincent J. Hellendoorn (ICSME, 2020)

Patching as Translation: the Data and the Metaphor. - Yangruibo Ding; Baishakhi Ray; Premkumar T. Devanbu; Vincent J. Hellendoorn (ASE, 2020)

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 (NeurIPS, 2021)

Towards automating code review at scale. - Vincent J. Hellendoorn; Jason Tsay; Manisha Mukherjee; Martin Hirzel (ESEC/SIGSOFT FSE, 2021)

Understanding neural code intelligence through program simplification. - Md. Rafiqul Islam Rabin; Vincent J. Hellendoorn; Mohammad Amin Alipour (ESEC/SIGSOFT FSE, 2021)

Learning lenient parsing & typing via indirect supervision. - Toufique Ahmed; Premkumar T. Devanbu; Vincent J. Hellendoorn (Empir. Softw. Eng., 2021)

Capturing Structural Locality in Non-parametric Language Models. - Frank F. Xu; Junxian He; Graham Neubig; Vincent Josua Hellendoorn (ICLR, 2022)

On the Naturalness of Fuzzer-Generated Code. - Rajeswari Hita Kambhamettu; John Billos; Tomi Oluwaseun-Apo; Benjamin Gafford; Rohan Padhye; Vincent J. Hellendoorn (MSR, 2022)

Comments on Comments: Where Code Review and Documentation Meet. - Nikitha Rao; Jason Tsay; Martin Hirzel; Vincent J. Hellendoorn (MSR, 2022)

A systematic evaluation of large language models of code. - Frank F. Xu; Uri Alon 002; Graham Neubig; Vincent Josua Hellendoorn (MAPS@PLDI, 2022)

The growing cost of deep learning for source code. - Vincent J. Hellendoorn; Anand Ashok Sawant (Commun. ACM, 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 001; Daniel Tarlow (CoRR, 2022)

DiffusER: Discrete Diffusion via Edit-based Reconstruction. - Machel Reid; Vincent J. Hellendoorn; Graham Neubig (CoRR, 2022)

Test smells 20 years later: detectability, validity, and reliability. - Annibale Panichella; Sebastiano Panichella; Gordon Fraser 001; Anand Ashok Sawant; Vincent J. Hellendoorn (Empir. Softw. Eng., 2022)

DiffusER: Diffusion via Edit-based Reconstruction. - Machel Reid; Vincent Josua Hellendoorn; Graham Neubig (ICLR, 2023)

Improving API Knowledge Discovery with ML: A Case Study of Comparable API Methods. - Daye Nam; Brad A. Myers; Bogdan Vasilescu; Vincent J. Hellendoorn (ICSE, 2023)

CAT-LM Training Language Models on Aligned Code And Tests. - Nikitha Rao; Kush Jain; Uri Alon 002; Claire Le Goues; Vincent J. Hellendoorn (ASE, 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)

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)

Memorization and generalization in neural code intelligence models. - Md. Rafiqul Islam Rabin; Aftab Hussain 001; Mohammad Amin Alipour; Vincent J. Hellendoorn (Inf. Softw. Technol., 2023)

Using an LLM to Help With Code Understanding. - Daye Nam; Andrew Macvean; Vincent J. Hellendoorn; Bogdan Vasilescu; Brad A. Myers (ICSE, 2024)

Large Language Models for Test-Free Fault Localization. - Aidan Z. H. Yang; Claire Le Goues; Ruben Martins; Vincent J. Hellendoorn (ICSE, 2024)

AI for Low-Code for AI. - Nikitha Rao; Jason Tsay; Kiran Kate; Vincent J. Hellendoorn; Martin Hirzel (IUI, 2024)

Learning Defect Prediction from Unrealistic Data. - Kamel Alrashedy; Vincent J. Hellendoorn; Alessandro Orso (SANER, 2024)

Skill over Scale: The Case for Medium, Domain-Specific Models for SE. - Manisha Mukherjee; Vincent J. Hellendoorn (Forge@ICSE, 2025)

An Exploratory Study of ML Sketches and Visual Code Assistants. - Luís F. Gomes; Vincent J. Hellendoorn; Jonathan Aldrich; Rui Abreu 001 (ICSE, 2025)

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 (ICSE, 2025)

SOSecure: Safer Code Generation with RAG and StackOverflow Discussions. - Manisha Mukherjee; Vincent J. Hellendoorn (CoRR, 2025)

Prompting LLMs for Code Editing: Struggles and Remedies. - Daye Nam; Ahmed Omran; Ambar Murillo; Saksham Thakur; Abner Araujo; Marcel Blistein; Alexander Frömmgen; Vincent J. Hellendoorn; Satish Chandra 001 (CoRR, 2025)