Syntax-guided program reduction for understanding neural code intelligence models-Reference-Cited by-同舟云学术

Syntax-guided program reduction for understanding neural code intelligence models

Published:2022-06-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming
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Author:

Rabin Md Rafiqul Islam¹,Hussain Aftab¹,Alipour Mohammad Amin¹

Affiliation:

1. University of Houston, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3520312.3534869

Reference28 articles.

1. The adverse effects of code duplication in machine learning models of code

2. Learning natural coding conventions

3. Suggesting accurate method and class names

4. A Survey of Machine Learning for Big Code and Naturalness

5. Miltiadis Allamanis , Marc Brockschmidt , and Mahmoud Khademi . 2018 . Learning to Represent Programs with Graphs . In International Conference on Learning Representations (ICLR 2018). OpenReview.net, Open Access. https://openreview.net/forum?id=BJOFETxR- Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018. Learning to Represent Programs with Graphs. In International Conference on Learning Representations (ICLR 2018). OpenReview.net, Open Access. https://openreview.net/forum?id=BJOFETxR-

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