TRACED: Execution-aware Pre-training for Source Code

Author:

Ding Yangruibo1ORCID,Steenhoek Benjamin2ORCID,Pei Kexin1ORCID,Kaiser Gail1ORCID,Le Wei3ORCID,Ray Baishakhi1ORCID

Affiliation:

1. Columbia University, New York, NY, United States

2. Iowa State University, Ames, IA, USA

3. Iowa State University, Ames, IA, United States

Funder

National Science Foundation

Publisher

ACM

Reference55 articles.

1. Unified Pre-training for Program Understanding and Generation

2. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton. 2021. Program Synthesis with Large Language Models. CoRR abs/2108.07732 (2021). arXiv:2108.07732 https://arxiv.org/abs/2108.07732

3. David Bieber Rishab Goel Dan Zheng Hugo Larochelle and Daniel Tarlow. 2022. Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions. https://openreview.net/forum?id=SIcz2sObJ-5

4. David Bieber, Charles Sutton, Hugo Larochelle, and Daniel Tarlow. 2020. Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. In Advances in Neural Information Processing Systems, Vol. 33. Curran Associates, Inc., 8626--8637. https://papers.nips.cc/paper/2020/hash/62326dc7c4f7b849d6f013ba46489d6c-Abstract.html

5. Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations

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