Vincent J. Hellendoorn

Orcid: 0000-0001-7516-0525

Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA, USA


According to our database1, Vincent J. Hellendoorn authored at least 34 papers between 2015 and 2024.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Large Language Models for Test-Free Fault Localization.
Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, 2024

2023
Memorization and generalization in neural code intelligence models.
Inf. Softw. Technol., 2023

Learning Defect Prediction from Unrealistic Data.
CoRR, 2023

In-IDE Generation-based Information Support with a Large Language Model.
CoRR, 2023

Stack Over-Flowing with Results: The Case for Domain-Specific Pre-Training Over One-Size-Fits-All Models.
CoRR, 2023

AI for Low-Code for AI.
CoRR, 2023

CAT-LM Training Language Models on Aligned Code And Tests.
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.
Proceedings of the 45th IEEE/ACM International Conference on Software Engineering, 2023

DiffusER: Diffusion via Edit-based Reconstruction.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Test smells 20 years later: detectability, validity, and reliability.
Empir. Softw. Eng., 2022

DiffusER: Discrete Diffusion via Edit-based Reconstruction.
CoRR, 2022

A Library for Representing Python Programs as Graphs for Machine Learning.
CoRR, 2022

The growing cost of deep learning for source code.
Commun. ACM, 2022

A systematic evaluation of large language models of code.
Proceedings of the MAPS@PLDI 2022: 6th ACM SIGPLAN International Symposium on Machine Programming, 2022

Comments on Comments: Where Code Review and Documentation Meet.
Proceedings of the 19th IEEE/ACM International Conference on Mining Software Repositories, 2022

On the Naturalness of Fuzzer-Generated Code.
Proceedings of the 19th IEEE/ACM International Conference on Mining Software Repositories, 2022

Capturing Structural Locality in Non-parametric Language Models.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Learning lenient parsing & typing via indirect supervision.
Empir. Softw. Eng., 2021

Understanding neural code intelligence through program simplification.
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.
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.
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.
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020

Revisiting Test Smells in Automatically Generated Tests: Limitations, Pitfalls, and Opportunities.
Proceedings of the IEEE International Conference on Software Maintenance and Evolution, 2020

Global Relational Models of Source Code.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Are My Invariants Valid? A Learning Approach.
CoRR, 2019

When code completion fails: a case study on real-world completions.
Proceedings of the 41st International Conference on Software Engineering, 2019

2018
On the naturalness of proofs.
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.
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?
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.
Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, 2017

2016
On the "naturalness" of buggy code.
Proceedings of the 38th International Conference on Software Engineering, 2016

2015
On the "Naturalness" of Buggy Code.
CoRR, 2015

Will They Like This? Evaluating Code Contributions with Language Models.
Proceedings of the 12th IEEE/ACM Working Conference on Mining Software Repositories, 2015

CACHECA: A Cache Language Model Based Code Suggestion Tool.
Proceedings of the 37th IEEE/ACM International Conference on Software Engineering, 2015


  Loading...