Enhancing source code classification effectiveness via prompt learning incorporating knowledge features

Author:

Ma Yong,Luo Senlin,Shang Yu-Ming,Zhang Yifei,Li Zhengjun

Publisher

Springer Science and Business Media LLC

Reference67 articles.

1. Khasnabish, J. N., Sodhi, M., Deshmukh, J. & Srinivasaraghavan, G. Detecting programming language from source code using Bayesian learning techniques. In Machine Learning and Data Mining in Pattern Recognition. Lecture Notes in Computer Science (ed. Perner, P.) 513–522 (Springer, Cham, 2014).

2. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

3. Alrashedy, K., Dharmaretnam, D., German, D. M., Srinivasan, V. & Aaron Gulliver, T. SCC++: Predicting the programming language of questions and snippets of stack overflow. J. Syst. Softw. 162, 110505 (2020).

4. Gilda, S. Source code classification using neural networks. In 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), 1–6 (2017).

5. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186 (Association for Computational Linguistics, Minneapolis, Minnesota, 2019).

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