Towards Efficient Fine-Tuning of Pre-trained Code Models: An Experimental Study and Beyond

Author:

Shi Ensheng1,Wang Yanlin2,Zhang Hongyu3,Du Lun4,Han Shi4,Zhang Dongmei4,Sun Hongbin1

Affiliation:

1. Xi’an Jiaotong University, China

2. Sun Yat-sen University, China

3. Chongqing University, China

4. Microsoft, China

Publisher

ACM

Reference64 articles.

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2. Wasi Uddin Ahmad , Saikat Chakraborty , Baishakhi Ray , and Kai-Wei Chang . 2021. Unified Pre-training for Program Understanding and Generation . In NAACL-HLT. Association for Computational Linguistics , 2655–2668. Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021. Unified Pre-training for Program Understanding and Generation. In NAACL-HLT. Association for Computational Linguistics, 2655–2668.

3. Alfred V Aho Monica S Lam Ravi Sethi and Jeffrey D Ullman. 2007. Compilers: principles techniques & tools. Pearson Education India. Alfred V Aho Monica S Lam Ravi Sethi and Jeffrey D Ullman. 2007. Compilers: principles techniques & tools. Pearson Education India.

4. Haldun Akoglu . 2018. User’s guide to correlation coefficients. Turkish journal of emergency medicine, 18, 3 ( 2018 ), 91–93. Haldun Akoglu. 2018. User’s guide to correlation coefficients. Turkish journal of emergency medicine, 18, 3 (2018), 91–93.

5. Mining source code repositories at massive scale using language modeling

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3. Rethinking the Role of Structural Information: How It Enhances Code Representation Learning?;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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5. Exploiting the Adversarial Example Vulnerability of Transfer Learning of Source Code;IEEE Transactions on Information Forensics and Security;2024

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