Search Results - "unixcoder"

Evaluating uniXcoder Embeddings for Automated Grading: A Study Across Varied Code Perspectives

Authors: Nakka Narmada Peeta Basa Pati

Source: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). :1-5

A User Evaluation of UniXcoder Using Statement Completion in a Real-World Setting

Authors: de Weerdt, Jorit (author)

Index Terms: Code Completion, Integrated Development Environment, User Study, bachelor thesis

URL: http://resolver.tudelft.nl/uuid:4c54e567-86a7-4448-9c1b-d85856607b9f
https://github.com/code4me-me/code4me
https://github.com/code4me-me/code4me

An Empirical Analysis on the Performance of UniXcoder

Authors: van Dam, Tim (author)

Index Terms: code completion, type annotations, bachelor thesis

URL: http://resolver.tudelft.nl/uuid:ccf8b865-931c-480b-a5dd-4e4120a20126
https://github.com/timvandam/rp
https://github.com/timvandam/rp

UniXcoder: Unified Cross-Modal Pre-training for Code Representation

Authors: Guo, Daya Lu, Shuai Duan, Nan et al.

Index Terms: Computer Science - Computation and Language, Computer Science - Programming Languages, Computer Science - Software Engineering, text

URL: http://arxiv.org/abs/2203.03850

UniXcoder fine-tuned model

Authors: Benitez, Julian

Multi-Label Classification of Pure Code.

Authors: Gao, Bin Qin, Hongwu Ma, Xiuqin

Source: International Journal of Software Engineering & Knowledge Engineering; Oct2024, Vol. 34 Issue 10, p1641-1659, 19p

Subject Terms: PROGRAMMING languages, SOURCE code, CLASSIFICATION, INSTITUTIONAL repositories, ENCODING

Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study

Subject Terms: InCoder, CodeGPT, UniXcoder, code completion

Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study

Subject Terms: InCoder, CodeGPT, UniXcoder, code completion

An Empirical Study on the Performance of Large Pre-trained Models for Automatic Line Completion

Subject Terms: InCoder, UniXcoder, code completion

SYNCode: Synergistic Human–LLM Collaboration for Enhanced Data Annotation in Stack Overflow †.

Authors: Xia, Meng Maharjan, Shradha Le, Tammy et al.

Source: Information; May2025, Vol. 16 Issue 5, p392, 19p

Subject Terms: LANGUAGE models, SOFTWARE engineering, DEEP learning, TRANSFORMER models, ANNOTATIONS

Mašīnmācīšanās pielietojums programmatūras produktu automatizētās regresijas testēšanas paātrināšanai

Authors: Cipiševa, Svetlana Arnicāns, Guntis Latvijas Universitāte. Datorikas fakultāte

Contributors: Cipiševa, Svetlana Arnicāns, Guntis Latvijas Universitāte. Datorikas fakultāte

Subject Terms: Rindošanas mašīnmācīšanās, Datorzinātne, Gradient Boosting, Testpiemēra prioritāšu noteikšana, UniXCoder, LambdaMART

Access URL: https://dspace.lu.lv/dspace/handle/7/66043

An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities.

Authors: Yang, Zezhou Chen, Sirong Gao, Cuiyun et al.

Source: ACM Transactions on Software Engineering & Methodology; Sep2025, Vol. 34 Issue 7, p1-28, 28p

Subject Terms: CODE generators, DEEP learning, INFORMATION architecture, SEARCH algorithms, MULTISENSOR data fusion, LANGUAGE models, MACHINE learning

One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization.

Authors: Wang, Deze Chen, Boxing Li, Shanshan et al.

Source: ICSE: International Conference on Software Engineering; 2023, p5-16, 12p

Subject Terms: PROGRAMMING languages, SOURCE code, MULTILINGUALISM, DATA analysis, DATA corruption

Mašīnmācīšanās pielietojums programmatūras produktu automatizētās regresijas testēšanas paātrināšanai ; Application of machine learning to accelerate automated regression testing of software products

Authors: Cipiševa, Svetlana Arnicāns, Guntis Latvijas Universitāte. Datorikas fakultāte

Contributors: Cipiševa, Svetlana Arnicāns, Guntis Latvijas Universitāte. Datorikas fakultāte

Subject Terms: Datorzinātne, Testpiemēra prioritāšu noteikšana, Rindošanas mašīnmācīšanās, Gradient Boosting, UniXCoder, LambdaMART

Relation: 101361; https://dspace.lu.lv/dspace/handle/7/66043

Availability: https://dspace.lu.lv/dspace/handle/7/66043

Distinguishing LLM-Generated from Human-Written Code by Contrastive Learning.

Authors: Xu, Xiaodan Ni, Chao Guo, Xinrong et al.

Source: ACM Transactions on Software Engineering & Methodology; May2025, Vol. 34 Issue 4, p1-31, 31p

Subject Terms: CHATGPT, LANGUAGE models, CODE generators, MACHINE learning

Do Current Language Models Support Code Intelligence for R Programming Language?

Authors: Zhao, Zixiao Fard, Fatemeh

Source: ACM Transactions on Software Engineering & Methodology; Nov2025, Vol. 34 Issue 8, p1-39, 39p

Subject Terms: LANGUAGE models, SOFTWARE engineering, ABSTRACTION (Computer science), PROGRAMMING languages

Multi-View Feature Fusion Model for Software Bug Repair Pattern Prediction.

Authors: XU Yong CHENG Ming

Source: Wuhan University Journal of Natural Sciences; Dec2023, Vol. 28 Issue 6, p493-507, 15p

CoCoSoDa: Effective Contrastive Learning for Code Search.

Authors: Shi, Ensheng Wang, Yanlin Gu, Wenchao et al.

Source: ICSE: International Conference on Software Engineering; 2023, p2198-2210, 13p

Subject Terms: QUERY languages (Computer science), PROGRAMMING languages, DATA augmentation, SOFTWARE engineering

Reviews & Products: QUANTITATIVE Analysis (Book)

Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities.

Authors: Ma, Wei Liu, Shangqing Zhao, Mengjie et al.

Source: ACM Transactions on Software Engineering & Methodology; Sep2024, Vol. 33 Issue 7, p1-29, 29p

Subject Terms: LANGUAGE models, PROGRAMMING languages, FLOWGRAPHS, COMPUTER programming education, SYNTAX (Grammar), DEEP learning

Effective Hard Negative Mining for Contrastive Learning-Based Code Search.

Authors: Fan, Ye Li, Chuanyi Ge, Jidong et al.

Source: ACM Transactions on Software Engineering & Methodology; Mar2025, Vol. 34 Issue 3, p1-35, 35p

Subject Terms: SEMANTIC memory, PROGRAMMING languages, COMPUTER programming education, SEARCH engines, NATURAL languages