The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Summarizing Source Code with Transferred API Knowledge
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Are deep neural networks the best choice for modeling source code?
Hellendoorn, V. J., & Devanbu, P. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106290 · Cited by 292
Cited by
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., et al. (2021) · arXiv · doi:10.48550/arxiv.2102.04664 · Cited by 536
Self-citationRetrieval-based neural source code summarization
Zhang, J., Wang, X., Zhang, H., Sun, H., & Liu, X. (2020) · Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering · doi:10.1145/3377811.3380383 · Cited by 275
Retrieval Augmented Code Generation and Summarization
Parvez, M. R., Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.232 · Cited by 141
What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs?
Gao, S., Wen, X.-C., Gao, C., Wang, W., Zhang, H., & Lyu, M. R. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00109 · Cited by 106
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
Liu, S., Gao, C., Chen, S., Nie, L. Y., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3038681 · Cited by 93
Metadata
- Authors
- Hu, X., Li, G., Xia, X., Lo, D., Lu, S., & Jin, Z.
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 2269-2275
- OpenAlex
- W2807964941
- Cited by
- 263
- References
- 3
- FWCI
- 18.93
- Type
- conference paper
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Summarizing Source Code with Transferred API Knowledge. https://app.cochat.ai/papers/10.24963/ijcai.2018/314
- The paper
- Hu, X., Li, G., Xia, X., Lo, D., Lu, S., & Jin, Z. (2018). Summarizing Source Code with Transferred API Knowledge. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/314
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
