Suchergebnisse - (java OR jan) and python (source OR resource) code~

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

Autoren: Yi Rong Yan Zhou

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

Schlagwörter: 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

Relation: https://doi.org/10.1007/s44443-025-00362-2; https://doaj.org/toc/1319-1578; https://doaj.org/toc/2213-1248; https://doaj.org/article/0e8d85656442482785bc981b326c25a4

Verfügbarkeit: https://doi.org/10.1007/s44443-025-00362-2
https://doaj.org/article/0e8d85656442482785bc981b326c25a4

Context‐based transfer learning for low resource code summarization.

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

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

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

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

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

Weitere Verfasser: Chaudhari, Nirmal Jain, Madhur Mosser, Sébastien et al.

Schlagwörter: source code, merge, tool, software engineering

Zugangs-URL: http://hdl.handle.net/11375/28838

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

Autoren: Ahmad, Azeem Sun, Xin

Quelle: IEEE Access. 13:205254-205277

Schlagwörter: 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

Dateibeschreibung: electronic

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

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

Autoren: Eric Jin Yu Sun

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

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

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

Quelle: IEEE Access. 13:54561-54584

Schlagwörter: 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

Dateibeschreibung: electronic

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

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

Autoren: Bhutani Vikram Toosi Farshad Ghassemi Buckley Jim

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

Schlagwörter: 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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/ac1de08d4c5c4733b3ca9004305cf826

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

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

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

Schlagwörter: *SUPPORT vector machines, *MACHINE learning, *LANGUAGE models, *SOURCE code, *FEATURE selection, *JAVA programming language, *PROGRAMMING languages, *SOFTWARE engineering

Software and pipelines for registration and analyses of rodent brain image data in reference atlas space.

Autoren: Puchades, Maja A. Yates, Sharon C. Csucs, Gergely et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-15, 15p

Schlagwörter: OPEN source software, DATA analysis, DATA visualization, COMPUTER software, SOFTWARE frameworks, BRAIN mapping, DATA mapping, RODENTS

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

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

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

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

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

Autoren: Muhammad Ilham Akbar Novita Kurnia Ningrum

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

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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/f40cbf64cc594c2e8ae97f26ed727c5a

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

Autoren: Smit, Imelda Naudé, Eduan Zulu, Busisiwe

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

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

Using ChatGPT as a Static Application Security Testing Tool.

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

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

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

Can open source large language models be used for tumor documentation in Germany?—An evaluation on urological doctors' notes.

Autoren: Lenz, Stefan Ustjanzew, Arsenij Jeray, Marco et al.

Quelle: BioData Mining; 7/24/2025, Vol. 18 Issue 1, p1-25, 25p

Schlagwörter: LANGUAGE models, MEDICAL records, DATA mining, UROLOGISTS, OPEN source software, MEDICAL language

Geografische Kategorien: GERMANY

NESTML: a generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules.

Autoren: Linssen, Charl Babu, Pooja N. Eppler, Jochen M. et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-21, 21p

Schlagwörter: ARTIFICIAL neural networks, MODELING languages (Computer science), SYNAPSES, C++, SYNTAX (Grammar)

Systems Neuroscience Computing in Python (SyNCoPy): a python package for large-scale analysis of electrophysiological data.

Autoren: Mönke, Gregor Schäfer, Tim Parto-Dezfouli, Mohsen et al.

Quelle: Frontiers in Neuroinformatics; 2024, p1-8, 8p

Schlagwörter: COMPUTER systems, SIGNAL processing, POWER spectra, COHERENCE (Physics), PARALLEL processing

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

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

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

FHIR in Focus: Enabling Biomedical Data Harmonization for Intelligent Healthcare Systems.

Autoren: Hornback, Andrew Marteau, Benoit Tan, Shaun Q. Y. et al.

Quelle: IEEE Reviews in Biomedical Engineering; 2026, Vol. 19, p305-336, 32p

A dataset for human-written and AI-generated code source classification(Mendeley Data).

Autoren: Ghizlane Boukili Said EL Garouani Jamal Riffi

Quelle: Data in Brief, Vol 65, Iss , Pp 112527- (2026)

Schlagwörter: ChatGPT, Programming languages, Machine learning, Detection, Prompt, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2352340926000806; https://doaj.org/toc/2352-3409

Zugangs-URL: https://doaj.org/article/e71dc34051dc4789bdc6d3608aeef850

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

Autoren: Bagheri, Amirreza Hegedűs, Péter

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

Schlagwörter: COMPUTER software, SOURCE code, SYSTEM failures, DEEP learning, MACHINE learning