A Transformer-based Approach for Source Code Summarization
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
Abstract
Generating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwise relationship between code tokens to capture their long-range dependencies is crucial. To learn code representation for summarization, we explore the Transformer model that uses a self-attention mechanism and has shown to be effective in capturing long-range dependencies. In this work, we show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin. We perform extensive analysis and ablation studies that reveal several important findings, e.g., the absolute encoding of source code tokens’ position hinders, while relative encoding significantly improves the summarization performance. We have made our code publicly available to facilitate future research.- Anthology ID:
- 2020.acl-main.449
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4998–5007
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.449
- DOI:
- 10.18653/v1/2020.acl-main.449
- Cite (ACL):
- Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2020. A Transformer-based Approach for Source Code Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4998–5007, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Transformer-based Approach for Source Code Summarization (Ahmad et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.449.pdf
- Code
- wasiahmad/NeuralCodeSum + additional community code