GraphCode2Vec

Author:

Ma Wei1,Zhao Mengjie2,Soremekun Ezekiel1,Hu Qiang1,Zhang Jie M.3,Papadakis Mike1,Cordy Maxime1,Xie Xiaofei4,Traon Yves Le1

Affiliation:

1. University of Luxembourg, Luxembourg

2. LMU Munich, Germany

3. University College London, United Kingdom

4. Singapore Management University, Singapore

Funder

Luxembourg National Research Funds (FNR)

Publisher

ACM

Reference78 articles.

1. Miltiadis Allamanis , Marc Brockschmidt , and Mahmoud Khademi . 2017. Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740 ( 2017 ). Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2017. Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740 (2017).

2. Uri Alon Shaked Brody Omer Levy and Eran Yahav. 2019. code2seq: Generating Sequences from Structured Representations of Code. arXiv:1808.01400 [cs.LG] Uri Alon Shaked Brody Omer Levy and Eran Yahav. 2019. code2seq: Generating Sequences from Structured Representations of Code. arXiv:1808.01400 [cs.LG]

3. code2vec: learning distributed representations of code

4. Richard E Bellman . 2015. Adaptive control processes . Princeton university press . Richard E Bellman. 2015. Adaptive control processes. Princeton university press.

5. Tal Ben-Nun , Alice Shoshana Jakobovits, and Torsten Hoefler . 2018 . Neural code comprehension: A learnable representation of code semantics. arXiv preprint arXiv:1806.07336 (2018). Tal Ben-Nun, Alice Shoshana Jakobovits, and Torsten Hoefler. 2018. Neural code comprehension: A learnable representation of code semantics. arXiv preprint arXiv:1806.07336 (2018).

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3. GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning;Journal of Systems and Software;2024-06

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