The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
Open accessAbstract
Pre-trained models for programming languages have recently demonstrated great success on code intelligence.To support both code-related understanding and generation tasks, recent works attempt to pre-train unified encoder-decoder models.However, such encoder-decoder framework is sub-optimal for auto-regressive tasks, especially code completion that requires a decoder-only manner for efficient inference.In this paper, we present UniXcoder, a unified cross-modal pre-trained model for programming language.The model utilizes mask attention matrices with prefix adapters to control the behavior of the model and leverages cross-modal contents like AST and code comment to enhance code representation.To encode AST that is represented as a tree in parallel, we propose a one-to-one mapping method to transform AST in a sequence structure that retains all structural information from the tree.Furthermore, we propose to utilize multi-modal contents to learn representation of code fragment with contrastive learning, and then align representations among programming languages using a cross-modal generation task.We evaluate UniXcoder on five code-related tasks over nine datasets.To further evaluate the performance of code fragment representation, we also construct a dataset for a new task, called zero-shot code-to-code search.Results show that our model achieves state-of-the-art performance on most tasks and analysis reveals that comment and AST can both enhance UniXcoder.
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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019) · arXiv · doi:10.48550/arxiv.1910.13461 · Cited by 8,816
SimCSE: Simple Contrastive Learning of Sentence Embeddings
Gao, T., Yao, X., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.552 · Cited by 2,851
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.139 · Cited by 2,824
Self-citationWang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.685 · Cited by 1,495
Self-citation
Research works printed but not linked: 2
- Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, LIU Shujie, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al. 2020. Graphcode- bert: Pre-training code representations with data flow. In International Conference on Learning Rep- resentations.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Lan- guage models are unsupervised multitask learners. OpenAI blog, 1(8):9.
Cited by
Sorted by citations
Items 401–500 of 674
StackPlagger: A System for Identifying AI-Code Plagiarism on Stack Overflow
Swaraj, A., Goyal, H., Chadgal, S., & Kumar, S. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00386 · Cited by 1
GRAPHCODEBERT-GAT: Hierarchical Graph Attention Networks for Explainable Neural Code Retrieval
Babatunde, O. S., Chen, B., Chen, W., Ssemwogerere, R., Ukot, A. R., Stamber, R. T., & Marrah, S. A. (2025) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip68645.2025.11352717 · Cited by 1
AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion
Jiang, T., Wang, Y., Wang, Y., Guo, D., Shi, E., Ma, Y., Chen, T., & Zheng, Z. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00085 · Cited by 1
Self-citationLOSVER: Line-Level Modifiability Signal-Guided Vulnerability Detection and Classification
Nam, D., & Baik, J. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00092 · Cited by 1
On the Evaluation of Large Language Models in Unit Test Evolution (Experience Paper)
None, Zhang, J., Niu, Y., & Zhou, B. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832282 · Cited by 1
CosFormer: A Code Semantic-Aware Transformer for Vulnerability Detection
Wei, Q., Zhang, Z., Yi, Q., Ji, Z., & Liang, H. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3659630 · Cited by 2
From Function to Repository: Towards Repository-Level Evaluation of Software Vulnerability Detection
Wen, X.-C., Wang, X., Chen, Y., Hu, R., Lo, D., & Gao, C. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3662145 · Cited by 17
Retrieval-Augmented Code Generation of Low-Resource Programming Languages
Lin, J., Zhang, Y., Li, C., Zou, W., Ge, J., & Luo, B. (2025) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec66846.2025.00106 · Cited by 1
GFTrans: an on-the-fly static analysis framework for code performance profiling
Li, J., Wen, Y., Liu, J., Zeng, B., & Mirjalili, S. (2026) · Frontiers in Big Data · doi:10.3389/fdata.2026.1779935 · Cited by 1
Chen, K., Gu, Y. J., & Zha, Z. (2025) · International Conference on Electronic Information Engineering and Computer Communication (EIECC) · doi:10.1109/eiecc67963.2025.11409511 · Cited by 1
R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability
Wang, Y., Keung, J., Ma, X., Mao, Z., Chen, K., & Li, Y. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00057 · Cited by 1
Self-citationClara: A Cross-Modal Learning Framework for Enhanced Vulnerability Detection
Peng, X., Wang, S., Lin, B., Qin, Y., Chen, L., & Mao, X. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3691741 · Cited by 1
UntrustVul: Automated Untrustworthy Alert Identification in Vulnerability Detection Models
Tung, L. N., Du, X., Neelofar, N., & Aleti, A. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3693636 · Cited by 1
Beyond Accuracy: Characterizing Code Comprehension Capabilities in (Large) Language Models
Mächtle, F., Serr, J.-N., Loose, N., & Eisenbarth, T. (2026) · IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest) · doi:10.1145/3786154.3788586 · Cited by 1
Yu, L., Zhang, J., Wang, X., Yang, L., Zhang, F., Wang, P., Xu, J. Q., & Ma, J. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3808102 · Cited by 1
An Automated Methodology for Generating Labeled Datasets of Semantic Errors in Code
Kassem, M., Ribeiro, F., & Nadi, S. (2026) · International Workshop on Large Language Models for Code (LLM4Code) · doi:10.1145/3786181.3788730 · Cited by 1
Alsobeh, A., Khamaiseh, S. Y., Middendorf, D., & Dziubinski, J. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00100 · Cited by 2
Wang, S., Huang, L., Wei, S., Gao, A., Hu, R., & Ng, V. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3787831 · Cited by 1
From Seed to Scope: Reasoning to Identify Change Impact Sets
Yadavally, A., & Nguyen, T. N. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3773265 · Cited by 1
Large Language Model-Aided Partial Program Dependence Analysis
Rong, X., Yadavally, A., & Nguyen, T. N. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3773119 · Cited by 1
Phan, H., & Jannesari, A. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614869 · Cited by 2
Mapping the Repository Landscape: Harnessing Similarity with RepoSim and RepoSnipy
Li, Z., & Filgueira, R. (2023) · IEEE International Conference on eScience · doi:10.1109/e-science58273.2023.10254873 · Cited by 1
Williams, C., & Filgueira, R. (2023) · IEEE International Conference on eScience · doi:10.1109/e-science58273.2023.10254843 · Cited by 1
Assessing and Improving Dataset and Evaluation Methodology in Deep Learning for Code Clone Detection
Li, H., Gao, Q. Z., & Zhang, S. (2023) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre59848.2023.00044 · Cited by 1
Liu, S., Guo, W., Yu, Y., & Cai, B. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394423 · Cited by 1
KFCC: A differentiation-aware and keyword-guided fine-grain code comment generation model
Zhang, R., Qiao, Z., Zhang, C., & Yu, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123946 · Cited by 1
SG-Coder: Code completion algorithm applied on large software development platform
Li, S., Liu, Z., Liu, X., Chen, G., Gao, Y., & Liu, Y. (2023) · International Academic Exchange Conference on Science and Technology Innovation (IAECST) · doi:10.1109/iaecst60924.2023.10503165 · Cited by 1
SPVR: syntax-to-prompt vulnerability repair based on large language models
Wang, R., Li, Z., Gao, C., Wang, C., Yang, X. H., & Wang, X. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00579-5 · Cited by 13
A practical tool for detecting cross-language code pairs with similar control structures
Dai, F., & Chiba, S. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3636134 · Cited by 1
IRaDT: LLVM IR as Target for Efficient Neural Decompilation
Li, Y., Xu, T., & Wang, C. (2024) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194024500463 · Cited by 1
Xie, H., Lei, Y., Li, M., Yan, M. L., & Zhang, S. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695542 · Cited by 2
Data race detection via few-shot parameter-efficient fine-tuning
Shen, Y., Peng, M., Zhang, F., & Wu, Q. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112289 · Cited by 1
Simple and efficient Hash sketching for tree-structured data
Wu, W., Jiang, M., Luo, C. W., & Li, F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125973 · Cited by 1
MGVul: a Multi-Granularity Detection Framework for Software Vulnerability
Zhao, X., Li, Y., Zha, Z., & Ling, Z.-H. (2024) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc63619.2025.10849117 · Cited by 1
Data-Efficient Performance Modeling via Pre-training
Liu, C., & Baghdadi, R. (2025) · ACM SIGPLAN International Conference on Compiler Construction (CC) · doi:10.1145/3708493.3712683 · Cited by 1
SEOCD: Detecting obsolete code comments by fusing semantic features and expert features
Cui, Z., Liu, S., Li, L., & Zheng, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127470 · Cited by 1
CoSTV: Accelerating Code Search with Two-Stage Paradigm and Vector Retrieval
Zheng, D., Wang, Y., Chen, W., Chen, J., & Zheng, Z. (2024) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec65559.2024.00049 · Cited by 1
Self-citationWang, J.-Y., Chen, X., Pei, W., & Yang, S.-Y. (2025) · International Conference on Automated Software Engineering · doi:10.1007/s10515-025-00525-5 · Cited by 2
Learning code better through structural information of data flow
Zhang, Z., Chen, Y., Jiao, T., Bai, L., Guo, C., & Song, J. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07348-x · Cited by 1
Zhao, Y., Huang, Z., Zhang, K., Gao, W., Liu, Q., Liu, X., Yao, F., & Chen, E. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3581792 · Cited by 3
He, R., Zhang, L., Lyu, L., & Xue, C. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00545-1 · Cited by 4
SYNC: SYnergistic aNnotation Collaboration between Humans and LLMs for Enhanced Model Training
Le, T., Taylor, W., Maharjan, S., Xia, M., & Song, M. (2025) · IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA) · doi:10.1109/sera65747.2025.11154541 · Cited by 1
Prompt Learning for Source Code Summarization
Xu, T., Fang, C., Qian, H., Feng, X., & Sun, W. (2025) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs65678.2025.00028 · Cited by 2
Wu, Y., Xiao, L., & Du, X. (2025) · Network · doi:10.3390/network5040045 · Cited by 2
Stoica, A. Ş., Băbiceanu, D., Rebedea, T., & Mihăescu, M. C. (2025) · Mathematics · doi:10.3390/math13223589 · Cited by 1
Can Mamba Be Better? An Experimental Evaluation of Mamba in Code Intelligence
Liu, S., Keung, J., Yang, Z., Mao, Z., & Sun, Y. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00155
Weyssow, M., Zhou, X., Kim, K., Lo, D., & Sahraoui, H. A. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.04106 · Cited by 1
Pre-Training Representations of Binary Code Using Contrastive LearningPreprint
Zhang, Y., Huang, C., Zhang, Y., Cao, K., Anderson, S., Shao, H., Leach, K., & Huang, Y. G. (2023) · doi:10.36227/techrxiv.24061689.v1 · Cited by 1
A Code Completion Model based RWKV with Bimodal PretrainingPreprint
Zhou, L., & Li, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3387525/v1 · Cited by 1
Improving Programming Q&A with Neural Generative Augmentation
Chaidaroon, S., Zhang, X., Subramaniyam, S., Svajlenko, J., Shourya, T., Keivanloo, I., & Joy, R. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591860
Pre-Training Representations of Binary Code Using Contrastive LearningPreprint
Zhang, Y., Huang, C., Zhang, Y., Cao, K., Anderson, S., Shao, H., Leach, K., & Huang, Y. G. (2023) · doi:10.36227/techrxiv.24061689
An Empirical Study of Adversarial Training in Code Comment Generation
Shen, Y., Ju, X., Chen, X., & Yang, G. (2023) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2023-108
VDCNet: A Vulnerability Detection and Classification System in Cross-Project Scenarios
Zhang, D., Xian, H., Chen, J., & Xu, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44207-0_26
An Interpretable Vulnerability Detection Framework Based on Multi-task Learning
Liu, M., Han, X., Zuo, W., Luo, X., & Guo, L. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8178-6_18
Intervention-Based Alignment of Code Search with Execution Feedback
Han, H., Kim, M., Hwang, S.-W., Duan, N., & Lu, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.148
Self-citationTowards a Universal Python: Translating the Natural Modality of Python into Other Human Languages
Otten, J., Anastasopoulos, A., & Moran, K. (2023) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme58846.2023.00044
Mucha: Multi-channel based Code Change Representation Learning for Commit Message Generation
Wang, C., Wu, Y., & Zhang, X. (2023) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs60937.2023.00064
Multi-View Feature Fusion Model for Software Bug Repair Pattern Prediction
Yong, X. U., & CHENG, M. (2023) · Wuhan University Journal of Natural Sciences · doi:10.1051/wujns/2023286493
A Code Completion Approach Combining Pointer Network and Transformer-XL Network
Zhang, X., Liu, J., Long, T., & Hu, H. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-54521-4_17
Song, Q., Liu, J., & Hu, H. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-54521-4_19
SYNC: A Structurally Guided Hard Negative Curricula for Generalizable Neural Code Search
Naik, A., Das, S., Vedurada, J., & Aditya, S. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.7
A Survey on Code Pre-Trained Models for Neural Program RepairPreprint
Zhan, S., Wang, X., Wei, D., & Cao, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4831589
Zhang, S., & Liu, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66459-5_1
VulCausal: Robust Vulnerability Detection Using Neural Network Models from a Causal Perspective
Kuang, H., Zhang, J., Yang, F., Zhang, L., Huang, Z., & Yang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5498-4_4
Typhon: Automatic Recommendation of Relevant Code Cells in Jupyter Notebooks
Ragkhitwetsagul, C., Prasertpol, V., Ritta, N., Sae-Wong, P., Noraset, T., & Choetkiertikul, M. (2024) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse61278.2024.10613645
Rethinking the Role of Structural Information: How It Enhances Code Representation Learning?
Sun, Q., Chen, N., Wang, J., & Li, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651132
A study of code search technology
Zhao, L. (2024) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat62382.2024.10695201
Le, D. A., Bui, A. M. T., Nguyen, P. T., & Ruscio, D. D. (2024) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1145/3674805.3686682 · Cited by 1
Analyzing the Potency of Pretrained Transformer Models for Automated Program Repair
Leiwig, M., Swierzy, B., Bungartz, C., & Meier, M. (2024) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa64295.2024.00020
Towards generalizable neural program understanding and synthesis
Li, Z. (2024) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/181413
LLM-based method for generating vulnerable code equivalents
Chen, J., Liu, J., Wang, J., & Li, D. (2025) · doi:10.1117/12.3058052 · Cited by 1
Dynamic feedback-based vulnerability mining method for highly closed terminal protocols
Wang, Y., Wang, W., & Li, D. (2025) · doi:10.1117/12.3058050
Self-citationDetecting question relatedness in programming Q&A communities via bimodal feature fusion
Bu, Q., Guo, X., Sun, X., Jiang, J., Zhao, X., Zou, W., Wang, X., & Yan, J. (2025) · Automated Software Engineering · doi:10.1007/s10515-024-00482-5
Laminar 2.0: Serverless Stream Processing with Enhanced Code Search and Recommendations
Rotchford, D., Evans, S., & Filgueira, R. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/scw63240.2024.00261
A code completion approach combining pointer network and Transformer-XL network
Zhang, X., Liu, J., Long, T., & Hu, H. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06315-6
Enhancing Large Language Models for Improved Code Clone Detection: A Fine-Tuning Approach
Amin, R., Nashaat, M., Eid, A., & Abdel-Kader, R. F. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-9327-3_30
Rath, A., Li, Y., Davis, T., Bronaugh, B., Poddar, D., Li, S., & Wang, X. (2024) · Annual Computer Security Applications Conference Workshops (ACSAC Workshops) · doi:10.1109/acsacw65225.2024.00017
Rozi, M. F., & Takahashi, T. (2024) · Annual Computer Security Applications Conference Workshops (ACSAC Workshops) · doi:10.1109/acsacw65225.2024.00014 · Cited by 1
Tab: template-aware bug report title generation via two-phase fine-tuned models
Liu, X., Xu, Y., Sun, W., Huang, N., Sun, S., Li, Q., Yang, D., & Yan, M. L. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00505-9 · Cited by 1
R+R: Security Vulnerability Dataset Quality Is CriticalPreprint
Yadav, A. S., & Wilson, J. N. (2025) · Qeios · doi:10.32388/bt8ocz
MGS3: A Multi-Granularity Self-Supervised Code Search Framework
Li, R., Kang, J., Liu, Q., He, L., Zhang, Z., Sha, Y., Zhu, L., & Huang, Z. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709263
Exploration Study About LLM with Proper Prompt in Automated Program Repair
Liang, H., Gao, C., & Jia, Y. F. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4509-1_28
A Binary Code Similarity Detection Method Based on Multi-source Contrastive Learning
Yang, H., Wang, Y., Hu, Z., & Cheng, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-4731-6_14
Self-citationA Two-stage Approach for Structurally-similar Cross-language Code-pair Detection
Dai, F., & Chiba, S. (2025) · ACM Symposium on Applied Computing · doi:10.1145/3672608.3707807
Liu, Z., Li, Y., Wang, J., & Cai, Y. (2025) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner64311.2025.00031
Structure-Enhanced Prompt Learning for Graph-Based Code Vulnerability Detection
Chang, W., Ye, C., & Zhou, H. (2025) · Applied Sciences · doi:10.3390/app15116128
Learning the fine-grained code representation for log-level prediction
Zhao, Z., Fan, G., Jing, L., Zhu, M., Zhang, H., & Su, H. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00064-9
STEP-LINK: STEP-by-Step Tutorial Editing with Programmable LINKages
Li, T., Ke, J., Wen, Z., Wu, Y., lu, J., Zhu, B., Zhu, M., & Chen, W. (2025) · Visual Informatics · doi:10.1016/j.visinf.2025.100244
Dyff-VD: Binary Code Vulnerability Detection Based on Dynamic Feature Fusion
Li, Q., Liu, T., Liu, C., & Dong, W. (2025) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit65432.2025.11035071
Chen, B., López, J. A. H., Mussbacher, G., & Varró, D. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00088
Hedgecode: A Multi-Task Hedging Contrastive Learning Framework for Code Search
Chen, G., Xie, X., Tang, X., Qi, X., & Liu, W. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00008 · Cited by 3
HyRACC: A Hybrid Retrieval-Augmented Framework for More Efficient Code Completion
Li, C., Shang, J., Feng, Y., & Luo, B. (2025) · IEEE/ACM International Conference on AI Foundation Models and Software Engineering (FORGE) · doi:10.1109/forge66646.2025.00013
Red Light for Security: Uncovering Auto Feature Check and Access Control Gaps in AAOS
Jumana, Vyas, P., & Aafer, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97623-0_9
Hierarchical Mixture-of-Experts model for unified code search
Lin, Z., Zhang, X., Hu, X., Li, P., Zhou, D., & Lai, D. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103489
Infusing Syntax and Semantics into LLMs
Labate, A. B., & Cozman, F. G. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01341-9 · Cited by 2
DPEfficR: a data and parameter efficient approach for training neural API recommendation model
Yu, H., han, X., Chen, S. H., Feng, X., Sun, G., & Yang, W. P. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00530-8 · Cited by 1
Metadata
- Authors
- Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., & Yin, J.
- Affiliations
- Sun Yat-sen University (CN)
- Microsoft Research Asia (China) (CN)
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2022
- Pages
- 7212-7225
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4221166942
- Cited by
- 673
- References
- 28
- Citations by year
- 2026: 153 · 2025: 239 · 2024: 148 · 2023: 122 · 2022: 11
- FWCI
- 116.24
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (U1811262, U1811261, U2001211, U1811264, U1911203)
- Basic and Applied Basic Research Foundation of Guangdong Province (2019B15 15130001)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- UniXcoder, code representation, programming language models, code comments, contrastive learning, cross-modal generation, code completion, code intelligence
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
