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CCISolver: End-to-End Detection and Repair of Method-Level Code-Comment Inconsistency
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DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
DeepSeek-AI, Zhu, Q., Guo, D., Shao, Z., Yang, D., Wang, P., Xu, R., Wu, Y., Li, Y., Gao, H., et al. (2024) · arXiv · doi:10.48550/arxiv.2406.11931 · Cited by 51
Self-citationKTO: Model Alignment as Prospect Theoretic Optimization
Ethayarajh, K., Xu, W., Muennighoff, N., Jurafsky, D., & Kiela, D. (2024) · arXiv · doi:10.48550/arxiv.2402.01306 · Cited by 29
Research works printed but not linked: 65
- 10.1007/s10664-019-09694-w · DOI
- 10.1109/ICSE.2009.5070533 · DOI
- 10.1109/TSE.2009.70 · DOI · Paper page
- 10.1109/ICSE-Companion66252.2025.00056 · DOI
- 10.1109/TSE.2021.3138909 · DOI
- 10.1109/COMPSAC.2018.00028 · DOI
- 10.1609/aaai.v35i1.16119 · DOI · Paper page
- 10.1109/ICSE48619.2023.00074 · DOI
- 10.1109/ICSE55347.2025.00035 · DOI
- 10.1609/aaai.v34i05.6382 · DOI
- Boslaugh, 2012, Statistics in a Nutshell: A Desktop Quick Reference.
- 10.1093/ptj/85.3.257 · DOI · Paper page
- 10.18653/v1/2024.eacl-demo.20 · DOI
- 10.52202/075280-2020 · doi:10.52202/075280-2020 · Paper page
- 10.1109/ICSE55347.2025.00021 · DOI
- 10.1145/3773287 · DOI
- Arora, 2023, Ask me anything: A simple strategy for prompting language models, 1
- 10.1109/ASE56229.2023.00109 · DOI · Paper page
- 10.1109/ICSE.2019.00087 · DOI · Paper page
- difflib
- 10.18653/v1/2020.acl-main.168 · DOI
- 10.18653/v1/2022.acl-long.499 · DOI · Paper page
- Dong, 2019, Unified language model pre-training for natural language understanding and generation, 32, 1
- 10.1109/TSE.2024.3364675 · DOI
- 10.1145/3597503.3623306 · DOI
- 10.1109/TSE.2024.3358489 · DOI
- 10.18653/v1/D19-1670 · DOI · Paper page
- 10.1186/s40537-021-00492-0 · DOI · Paper page
- 10.1109/ICSE48619.2023.00081 · DOI
- 10.1109/TBDATA.2025.3536934 · DOI · Paper page
- 10.18653/v1/2024.findings-acl.388 · DOI
- 10.18653/v1/2023.acl-long.754 · DOI · Paper page
- Hu, 2022, LoRA: Low-rank adaptation of large language models, 1
- 10.52202/075280-2338 · DOI · Paper page
- 10.1145/3650212.3680395 · DOI
- 10.1109/TSE.2024.3475375 · DOI
- 10.1145/3597503.3608134 · DOI
- 10.18653/v1/2020.findings-emnlp.139 · DOI · Paper page
- 10.3115/1073083.1073135 · DOI · Paper page
- Banerjee, 2005, METEOR: An automatic metric for MT evaluation with improved correlation with human judgments, 65
- 10.1162/tacl_a_00107 · DOI · Paper page
- 10.3115/v1/p15-2097 · DOI
- 10.1561/1500000019 · DOI · Paper page
- 10.18653/v1/2021.findings-emnlp.354 · DOI · Paper page
- Wei, 2024, MagiCoder: Empowering code generation with OSS-instruct, 52632
- Jain, 2023, LLM-assisted code cleaning for training accurate code generators, 14981
- 10.1145/3731754 · DOI · Paper page
- 10.1109/ASE.2017.8115624 · DOI
- 10.1145/1294261.1294276 · DOI · Paper page
- 10.1109/ASE56229.2023.00142 · DOI
- 10.1145/3468264.3468553 · DOI
- 10.24818/ida-ql/2019.5 · doi:10.24818/ida-ql/2019.5
- Rabbi, 2020, An ensemble approach to detect code comment inconsistencies using topic modeling, 392
- Ouyang, 2021, Towards detecting and understanding code-document violations in rust, 189
- 10.1109/ASE.2015.67 · DOI
- 10.1016/j.jss.2011.09.019 · DOI
- 10.1007/s10664-023-10425-5 · DOI
- 10.1145/3546949 · DOI
- Zhai, 2020, CPC: Automatically classifying and propagating natural language comments via program analysis, 1359, 10.1145/3377811.3380427 · DOI
- 10.1109/ICPC.2019.00019 · DOI · Paper page
- 10.1145/3756681.3756988 · DOI
- 10.1145/3534117 · DOI
- 10.1109/TSE.2022.3185458 · DOI · Paper page
- 10.1145/3434280 · DOI
- 10.1145/3728963 · DOI · Paper page
Metadata
- Authors
- Zhong, R., Huo, Y., Gu, W., Kuang, J., Jiang, Z., Yu, G., Li, Y., Lo, D., & Lyu, M. R.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2026-09
- Volume
- 52
- Issue
- 9
- Pages
- 2546-2565
- ISSN
- 0098-5589
- 1939-3520
- 2326-3881
- Cited by
- 0
- References
- 2
- Licence
- Funders
- Research Grants Council of the Hong Kong Special Administrative Region, China (CUHK 14209124)
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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.
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- This page
- CoChat. (2026). Citation check: CCISolver: End-to-End Detection and Repair of Method-Level Code-Comment Inconsistency. https://app.cochat.ai/papers/10.1109/tse.2026.3689089
- The paper
- Zhong, R., Huo, Y., Gu, W., Kuang, J., Jiang, Z., Yu, G., Li, Y., Lo, D., & Lyu, M. R. (2026). CCISolver: End-to-End Detection and Repair of Method-Level Code-Comment Inconsistency. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2026.3689089
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.