Search Results - (java OR jana) AND python (source OR resource) code

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

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

Git ADV: Improving Java & Python Source Code Merging using Abstract Data Structures

Authors: Chaudhari, Nirmal Jain, Madhur Mosser, Sébastien et al.

Contributors: Chaudhari, Nirmal Jain, Madhur Mosser, Sébastien et al.

Subject Terms: source code, merge, tool, software engineering

Access URL: http://hdl.handle.net/11375/28838

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: print

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

Understanding Flaky Tests Through Linguistic Diversity: A Cross-Language and Comparative Machine Learning Study

Authors: Ahmad, Azeem Sun, Xin Naeem, Muhammad Rashid et al.

Source: IEEE Access. 13:54561-54584

Subject Terms: Codes, Accuracy, Vocabulary, Java, Feature extraction, Python, Training, Reproducibility of results, Random forests, Programming, Flaky tests, non-deterministic tests, machine learning, artificial intelligence, software testing

File Description: electronic

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-213193
https://doi.org/10.1109/ACCESS.2025.3553626

An Algorithm-Adaptive Source Code Converter to Automate the Translation from Python to Java

Authors: Eric Jin Yu Sun

Source: Computer Science and Information Technology Trends. :215-229

Analysing Software Quality of AI-Translated Code: A Comparative Study of Large Language Models Using Static Analysis

Authors: Bhutani Vikram Toosi Farshad Ghassemi Buckley Jim

Source: Applied Computer Systems, Vol 30, Iss 1, Pp 105-121 (2025)

Subject Terms: artificial intelligence, chatgpt4.1, c++, deep learning, deepseek r1, java, machine learning, nmt, object oriented, programming languages translation, python, source code translation, smt, Computer software, QA76.75-76.765

File Description: electronic resource

Relation: https://doaj.org/toc/2255-8691

Access URL: https://doaj.org/article/ac1de08d4c5c4733b3ca9004305cf826

Implementation of Gated Graph Neural Network (GGNN) on Bug Prediction in Java, Python, and C++ Programming Languages.

Authors: Juwono, Elroy Novarino Phoa, Matthew Farrell Wijaya, Michael et al.

Source: Procedia Computer Science. 2025, Vol. 269, p1171-1180. 10p.

Subject Terms: Graph neural networks, Java programming language, Python programming language, C++, Data structures, Defect tracking (Computer software development), Software failures, Machine learning

Design Pattern Prediction From Source Code Using LLM–Based Feature Engineering and SVM Classification.

Authors: Komolov, Sirojiddin Mazzara, Manuel Bajwa, Imran Sarwar et al.

Source: IET Software (Wiley-Blackwell). 1/16/2026, Vol. 2026, p1-25. 25p.

Identification of Source Code Plagiarism Using a Natural Language Processing (NLP) Approach Based on Code Writing Style Analysis

Authors: Muhammad Ilham Akbar Novita Kurnia Ningrum

Source: Journal of Applied Informatics and Computing, Vol 9, Iss 6, Pp 3079-3086 (2025)

Subject Terms: code plagiarism, codebert, siamese network, deep learning, source code plagiarism, identification, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/11206; https://doaj.org/toc/2548-6861

Access URL: https://doaj.org/article/f40cbf64cc594c2e8ae97f26ed727c5a

A detection process to create awareness of source-code plagiarism among students using it to pass introductory programming.

Authors: Smit, Imelda Naudé, Eduan Zulu, Busisiwe

Source: Independent Journal of Teaching & Learning; 2024, Vol. 19 Issue 1, p79-92, 14p

Subject Terms: PLAGIARISM, STUDENTS, COVID-19 pandemic, PYTHON programming language, AWARENESS

Using ChatGPT as a Static Application Security Testing Tool.

Authors: Bakhshandeh, Atieh Keramatfar, Abdalsamad Norouzi, Amir et al.

Source: ISeCure; Nov2023, Vol. 15 Issue 3, p1-8, 8p

Subject Terms: CHATGPT, ARTIFICIAL intelligence, SOURCE code, PYTHON programming language, NATURAL languages

Context‐based transfer learning for low resource code summarization

Authors: Yi Guo Yu Chai Lehuan Zhang et al.

Source: Software: Practice and Experience. 54:465-482

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

GNPSum: A code summarization enhancement framework based on Graph Node Position.

Authors: Cheng, Haogang Xu, Ling Huangfu, Luwen et al.

Source: Information & Software Technology. Nov2025, Vol. 187, pN.PAG-N.PAG. 1p.

Subject Terms: JAVA programming language, PYTHON programming language, GRAPH theory, REPRESENTATIONS of graphs

Towards a Block-Level ML-Based Python Vulnerability Detection Tool.

Authors: Bagheri, Amirreza Hegedűs, Péter

Source: Acta Cybernetica; 2024, Vol. 26 Issue 3, p323-371, 49p

Subject Terms: COMPUTER software, SOURCE code, SYSTEM failures, DEEP learning, MACHINE learning

Enhancing Code Summarization with Graph Embedding and Pre-Trained Model.

Authors: Li, Lixuan Li, Jie Xu, Yihui et al.

Source: International Journal of Software Engineering & Knowledge Engineering. Nov/Dec2023, Vol. 33 Issue 11/12, p1765-1786. 22p.

Subject Terms: *PROGRAMMING languages, *INFORMATION resources, LANGUAGE models, SOURCE code, PYTHON programming language

SBOM generation based on code-level external component trees.

Authors: Kong X Zhuo H Miao X et al.

Source: Scientific reports [Sci Rep] 2025 Nov 24; Vol. 15 (1), pp. 45277. Date of Electronic Publication: 2025 Nov 24.

Publication Type: Journal Article

Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE

FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology.

Authors: Groza, Tudor Gration, Dylan Baynam, Gareth et al.

Source: Bioinformatics. Jul2024, Vol. 40 Issue 7, p1-8. 8p.

Subject Terms: *LANGUAGE models, *HUMAN phenotype, *SOURCE code, *NATURAL languages, *PHENOTYPES

PyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data.

Authors: Xu, Jia Vaeggemose, Michael Schulte, Rolf F. et al.

Source: Diagnostics (2075-4418); Dec2024, Vol. 14 Issue 23, p2668, 17p

Subject Terms: NUCLEAR magnetic resonance spectroscopy, PYTHON programming language, MONTE Carlo method, SIGNAL-to-noise ratio, MACHINE learning

Comprehending C codes with LLMs: Effective comment generation through retrieval and reasoning.

Authors: Majumdar, Srijoni Deshpande, Adwita Das, Partha Pratim et al.

Source: Pattern Recognition Letters. Jan2026, Vol. 199, p295-302. 8p.

Subject Terms: *C (Computer program language), *LANGUAGE models, *EVALUATION methodology, *SOFTWARE maintenance, *ANNOTATIONS, *NATURAL language processing, *MACHINE learning