Advancements in Natural Language Processing for Code Summarization
Abstract
Code summarization plays a crucial role in software development by providing concise descriptions of code functionality. This paper explores recent advancements in natural language processing (NLP) techniques for automatically generating high-quality code summaries. We review state-of-the-art models, datasets, and evaluation metrics in the field of code summarization and identify key research challenges and opportunities for future work.
References
Alon, U., Brody, S., Levy, O., & Yahav, E. (2018). code2vec: Learning Distributed Representations of Code. arXiv preprint arXiv:1803.09473.
Ahmad, W. U., Chakraborty, S., Ray, B., & Chang, K. W. (2020). A Transformer-based Approach for Source Code Summarization. arXiv preprint arXiv:2005.00653.
Priyanka Koushik, S. M. (2024). Elevating Customer Experiences and Maximizing Profits with Predictable Stockout Prevention Modelling. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1171–1178. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5547
LeClair, A., McMillan, C., & Chatterjee, S. (2020). A Neural Model for Generating Natural Language Summaries of Program Subroutines. Empirical Software Engineering, 25(4), 3199-3227.
Hu, X., Li, G., Xia, X., Lo, D., & Jin, Z. (2020). Deep Code Comment Generation with Hybrid Lexical and Syntactical Information. Empirical Software Engineering, 25(3), 2179-2217.
Iyer, S., Konstas, I., Cheung, A., & Zettlemoyer, L. (2016). Summarizing Source Code using a Neural Attention Model. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2073-2083.
Sumit Mittal, "Framework for Optimized Sales and Inventory Control: A Comprehensive Approach for Intelligent Order Management Application," International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 61-65, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I3P109
Wang, W., Wang, Z., Cheung, S. C., & Yang, J. (2020). Learning Context-aware Code Representations for Predicting Program Properties. Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 583-594.
Yin, P., & Neubig, G. (2017). A Syntactic Neural Model for General-Purpose Code Generation. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 440-450.
Chen, Z., & Zhou, Y. (2018). Neural Network-based Automatic Code Generation for Sentence-to-API Translation. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 149-154.
Xu, C., Liu, D., White, R., Li, H., & Zhang, H. (2020). Pythonic Object-Oriented Code Summarization. Proceedings of the 42nd International Conference on Software Engineering, 1295-1307.
Kulbir Singh (2024) HEALTHCARE FRAUDULENCE: LEVERAGING ADVANCED ARTIFICIAL INTELLIGENCE TECHNIQUES FOR DETECTION International Research Journal of Modernization in Engineering Technology and Science, 6(2), 966-976 https://www.doi.org/10.56726/IRJMETS49394
Allamanis, M., Tarlow, D., Gordon, A., & Wei, Y. (2015). Bimodal Modelling of Source Code and Natural Language. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2123-2132.
Neha Dhaliwal. (2023). Towards AI-Driven Standardization in Disease Indication: Implementing Controlled Vocabulary for Clinical Reporting Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11), 1142–1151. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10673
