Abstract
Code summarization, aiming to generate succinct natural language description of source code, is extremely useful for code search and code comprehension. It has played an important role in software maintenance and evolution. Previous approaches generate summaries by retrieving summaries from similar code snippets. However, these approaches heavily rely on whether similar code snippets can be retrieved, how similar the snippets are, and fail to capture the API knowledge in the source code, which carries vital information about the functionality of the source code. In this paper, we propose a novel approach, named TL-CodeSum, which successfully uses API knowledge learned in a different but related task to code summarization. Experiments on large-scale real-world industry Java projects indicate that our approach is effective and outperforms the state-of-the-art in code summarization.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 |
| Editors | Jerome Lang |
| Place of Publication | Marina del Rey CA USA |
| Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
| Pages | 2269-2275 |
| Number of pages | 7 |
| ISBN (Electronic) | 9780999241127 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | International Joint Conference on Artificial Intelligence 2018 - Stockholm, Sweden Duration: 13 Jul 2018 → 19 Jul 2018 Conference number: 27th https://www.ijcai.org/proceedings/2018/ https://www.ijcai.org/proceedings/2018/ (Proceedings) |
Conference
| Conference | International Joint Conference on Artificial Intelligence 2018 |
|---|---|
| Abbreviated title | IJCAI 2018 |
| Country/Territory | Sweden |
| City | Stockholm |
| Period | 13/07/18 → 19/07/18 |
| Internet address |
Keywords
- Machine Learning
- Transfer
- Adaptation
- Multi-task Learning
- Deep Learning
- Applications of Supervised Learning
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