Search Results - Java AND python resource code

Base, Instruct, and Fine-Tune: Evaluating LLMs for Cross-Language Flaky Test Detection

Authors: Ahmad, Azeem Sun, Xin

Source: IEEE Access. 13:205254-205277

Subject Terms: Codes, Java, Semantics, C plus plus languages, Python, Large language models, Vocabulary, Training, Adaptation models, Robustness, Flaky tests, non-deterministic tests, fine-tuning, software testing, large language models, base models

File Description: electronic

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-220548
https://doi.org/10.1109/ACCESS.2025.3639645

Context‐based transfer learning for low resource code summarization.

Authors: Guo, Yi Chai, Yu Zhang, Lehuan et al.

Source: Software: Practice & Experience; Mar2024, Vol. 54 Issue 3, p465-482, 18p

Subject Terms: SOFTWARE maintenance, TRANSFER of training, LANGUAGE models, JAVA programming language, SOURCE code, NEURAL circuitry

Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs

Authors: Federico Cassano John Gouwar Francesca Lucchetti et al.

Source: Proceedings of the ACM on Programming Languages. 8:677-708

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Programming Languages, Programming Languages (cs.PL), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2308.09895

Quantifying cross-language code reuse via function-level clone detection

Authors: Yi Rong Yan Zhou

Source: Journal of King Saud University: Computer and Information Sciences, Vol 37, Iss 10, Pp 1-20 (2025)

Subject Terms: Natural language processing, Code reuse detection, Function-level clone analysis, Java and python source code, Academic plagiarism detection, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://doaj.org/toc/1319-1578; https://doaj.org/toc/2213-1248

Access URL: https://doaj.org/article/0e8d85656442482785bc981b326c25a4

Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs

Authors: Cassano, Federico Gouwar, John Lucchetti, Francesca et al.

Index Terms: Programming Languages, Machine Learning, text

URL: http://arxiv.org/abs/2308.09895

Context-Enriched Logging for Intelligent Code Fix Suggestions Using Large Language Models.

Authors: Kurapati, Maheswara

Source: Journal of Computer Science & Technology Studies; 2025, Vol. 7 Issue 11, p15-22, 8p

Subject Terms: METADATA, MACHINE learning, COMPUTER software, FAILURE analysis, DATA analytics, DATA logging

LoRA-MME: Multi-Model Ensemble of LoRA-Tuned Encoders for Code Comment Classification

Authors: Haider, Md Akib Bulbul, Ahsan Shahid, Nafis Fuad et al.

Index Terms: Software Engineering, Machine Learning, text

URL: http://arxiv.org/abs/2603.03959

Making Python Code Idiomatic by Automatic Refactoring Non-Idiomatic Python Code with Pythonic Idioms

Authors: Zhang, Zejun Xing, Zhenchang Xia, Xin et al.

Index Terms: Software Engineering, text

URL: http://arxiv.org/abs/2207.05613

Program Code Generation with Generative AIs.

Authors: Idrisov, Baskhad Schlippe, Tim

Source: Algorithms; Feb2024, Vol. 17 Issue 2, p62, 19p

Subject Terms: GENERATIVE artificial intelligence, TIME complexity, CHATGPT, GENERATIVE pre-trained transformers, PROBLEM solving

Cross-lingual Performance of CodeGPT on the Code Completion Task

Authors: Kuo, Nadine (author)

Index Terms: Code completion, Large Language Models (LLMs), Transformers, CodeGPT, Self-attention, Autocompletion, bachelor thesis

URL: http://resolver.tudelft.nl/uuid:2b2386e8-f9a9-4d77-9a4a-a4e7a5208d38
https://github.com/AISE-TUDelft/CodeShop
https://github.com/AISE-TUDelft/CodeShop

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

Deriving Coding-Specific Sub-Models from LLMs using Resource-Efficient Pruning

Authors: Puccioni, Laura Farshin, Alireza Scazzariello, Mariano et al.

Source: Digital Futures Proceedings of IEEE/ACM International Workshop on Large Language Models for Code 2025, LLM4Code 2025.

Subject Terms: Large Language Models, LLMs, pruning, code

File Description: electronic

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-374905
https://kth.diva-portal.org/smash/get/diva2:2025739/FULLTEXT01.pdf

Evaluating Large Language Models for Code Translation: Effects of Prompt Language and Prompt Design

Authors: Aljagthami, Aamer Banabila, Mohammed Alshehri, Musab et al.

Index Terms: Software Engineering, text

URL: http://arxiv.org/abs/2509.12973

MultiAIGCD: A Comprehensive dataset for AI Generated Code Detection Covering Multiple Languages, Models,Prompts, and Scenarios

Authors: Demirok, Basak Kutlu, Mucahid Mergen, Selin

Index Terms: Software Engineering, Artificial Intelligence, text

URL: http://arxiv.org/abs/2507.21693

AVATAR: A Parallel Corpus for Java-Python Program Translation

Authors: Ahmad, Wasi Uddin Tushar, Md Golam Rahman Chakraborty, Saikat et al.

Index Terms: Software Engineering, Computation and Language, text

URL: http://arxiv.org/abs/2108.11590

When Retriever Meets Generator : A Joint Model for Code Comment Generation

Authors: Le, T. P. T. Bui, A. M. T. Pham, H. N. D. et al.

Index Terms: Training, Codes, Shape, Source coding, Retrieval augmented generation, Noise, Nearest neighbor methods, Software measurement, Software engineering, Python, Code comment generation, Pre-trained language model, Computer Sciences, Datavetenskap (datalogi), Conference paper, info:eu-repo/semantics/conferenceObject, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-75816
2025 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), p. 435-441

Insights for Curriculum-Oriented Instruction of Programming Paradigms for Non-Computer Science Majors: Survey and Public Q&A Evidence.

Authors: Oh, Ji-Hye Park, Hyun-Seok

Source: Applied Sciences (2076-3417); Feb2026, Vol. 16 Issue 3, p1191, 29p

Subject Terms: LEARNING problems, COMPUTER science education, INSTRUCTIONAL systems, QUESTIONNAIRES, CURRICULUM planning, CONTEXTUAL learning

CNN-BASED CODE READABILITY CLASSIFICATION AND BUG LOCALIZATION IN PROGRAMMING FRAMEWORK.

Authors: BARRAMEDA, ROLANDO B. BALLERA, MELVIN A.

Source: TPM: Testing, Psychometrics, Methodology in Applied Psychology; 2025 Supplement, Vol. 32, p201-206, 6p

Context-Enriched Logging for Intelligent Code Fix Suggestions Using Large Language Models

Authors: Maheswara Kurapati

Source: Journal of Computer Science and Technology Studies; Vol. 7 No. 11; 15-22; 2709-104X

Index Terms: Context-Rich Logging, Code Refactoring Automation, Language Model Diagnostics, Programmatic Fix Synthesis, Software System Observability, info:eu-repo/semantics/article, info:eu-repo/semantics/publishedVersion

URL: https://al-kindipublisher.com/index.php/jcsts/article/view/11269
https://al-kindipublisher.com/index.php/jcsts/article/view/11269/10015
https://al-kindipublisher.com/index.php/jcsts/article/view/11269/10015

Neuron-Guided Interpretation of Code LLMs: Where, Why, and How?

Authors: Yin, Zhe Gu, Xiaodong Shen, Beijun

Index Terms: Software Engineering, Artificial Intelligence, text

URL: http://arxiv.org/abs/2512.19980