Beyond Accuracy and Robustness Metrics for Large Language Models for Code

Author:

Rodriguez-Cardenas Daniel1ORCID

Affiliation:

1. Semeru Lab, William & Mary, Williamsburg, VA, United States of America

Publisher

ACM

Reference37 articles.

1. Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski et al. 2021. Program Synthesis with Large Language Models. arXiv:cs.PL/2108.07732

2. Federico Cassano John Gouwar Daniel Nguyen Sydney Nguyen Luna Phipps-Costin et al. 2022. MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation. http://arxiv.org/abs/2208.08227 arXiv:2208.08227 [cs].

3. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, et al. 2021. Evaluating Large Language Models Trained on Code. http://arxiv.org/abs/2107.03374 arXiv:2107.03374 [cs].

4. SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

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