Výsledky vyhľadávania - (java OR jan) AND python resource code

Neural Network in Java using DeepLearning4J framework vs. Python using TensorFlow framework: Trade-offs Between Execution Speed, Resource Consumption, and Developer Efficiency : A Comparative Benchmark and User Evaluation Study

Additional Titles: Neuronnät i Java (med DeepLearning4J) vs. Python (med TensorFlow): Avvägningar mellan exekveringshastighet, resursförbrukning och utvecklareffektivitet : En Jämförande Prestandamätning och Användarutvärdering

Autori: Haidari, Marvin Abdi Salah, Salahudin

Témy: Artificial intelligence, Machine learning, Neural network, Convolutional neural network, Performance benchmarking, Execution speed, Model accuracy, Python, Java, Deeplearning4j, TensorFlow, Modified National Institute of Standards and Technology database (MNIST), Image recognition., Artificiell intelligens, Maskininlärning, Artificiellt neuronnät, Konvolutionellt neuralt nätverk, Prestandamätning, Exekveringshastighet, Modellnoggrannhet, DeepLearning4J, Bildigenkänning, Computer and Information Sciences, Data- och informationsvetenskap, Student thesis, info:eu-repo/semantics/bachelorThesis, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-368035
TRITA-EECS-EX ; 2025:467

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

Autori: Ahmad, Azeem Sun, Xin

Zdroj: IEEE Access. 13:205254-205277

Predmety: 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

Popis súboru: electronic

Prístupová URL adresa: 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.

Autori: Guo, Yi Chai, Yu Zhang, Lehuan a ďalší

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

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

Utilizing Adaptive Learning with GPT-4 for Introduction to Python: Effects on Accuracy and Time-to-Solution.

Autori: Santivanez, Diego Oliva, Kevin Wong, Lenis

Zdroj: Engineering, Technology & Applied Science Research; Feb2026, Vol. 16 Issue 1, p31954-31962, 9p

Predmety: GENERATIVE pre-trained transformers, PYTHON programming language, INSTRUCTIONAL systems, WEB-based user interfaces, STUDENT engagement, FORMATIVE evaluation, GAMIFICATION, COMPUTER programming education

Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems.

Autori: Sun, Zhensu Du, Xiaoning Song, Fu a ďalší

Zdroj: ACM Transactions on Software Engineering & Methodology; Jan2025, Vol. 34 Issue 1, p1-22, 22p

Predmety: LANGUAGE models, NEURAL codes, POWER resources, DEEP learning, SCIENTIFIC community

Resource-Efficient & Effective Code Summarization

Autori: Afrin, Saima Call, Joseph Nguyen, Khai-Nguyen a ďalší

Témy: Computer Science - Software Engineering, text

URL: http://arxiv.org/abs/2502.03617

Formal Abstraction of Program Slices for Specification and Requirement Extraction Using Object-Z.

Autori: Aparna, K. S. Kulkarni, R. N.

Zdroj: Engineering, Technology & Applied Science Research; Feb2026, Vol. 16 Issue 1, p30843-30851, 9p

Predmety: SOFTWARE requirements specifications, FORMAL methods (Computer science), SOFTWARE engineering, FORMAL languages, SOFTWARE maintenance, JAVA programming language

Purposeful decision-support with Digital Shadows: a model catalog architecture and its implementation for production scheduling in the injection molding domain.

Autori: Sapel, Patrick Gannouni, Aymen Abdelrazeq, Anas a ďalší

Zdroj: International Journal of Production Research; Mar2026, Vol. 64 Issue 6, p2133-2157, 25p

Predmety: PRODUCTION scheduling, INJECTION molding, MODEL-driven software architecture, DECISION support systems, DIGITAL twin, ENTERPRISE resource planning, INDUSTRY 4.0

Cross-Language Code Smell Detection via Transfer Learning.

Autori: Sandouka, Rana Aljamaan, Hamoud

Zdroj: Applied Sciences (2076-3417); Sep2025, Vol. 15 Issue 17, p9293, 23p

Predmety: JAVA programming language, PYTHON programming language, TRANSFER of training, PROGRAMMING languages, ENSEMBLE learning, COMPUTER software quality control

3D visualization of code smells: A scalable multi-level metaphoric approach for software developers.

Autori: Hasantha, Chathuranga Hettiarachchi, Thenuri Sandara Wijesiriwardana, Chaman

Zdroj: Advances in Science & Technology Research Journal; 2026, Vol. 20 Issue 3, p318-335, 18p

Predmety: DESIGN failures, THREE-dimensional imaging, MAINTAINABILITY (Engineering), SOFTWARE architecture, SOURCE code, COMPUTER software quality control, SOFTWARE development tools

Cloud-assisted LLM-enhanced datasets for AST hierarchy-aware code summarization model.

Autori: Zhang, Junsan Yan, Yudie Han, Junxiao a ďalší

Zdroj: Journal of Cloud Computing (2192-113X); 2/14/2026, Vol. 15 Issue 1, p1-18, 18p

Assessing and Analyzing the Correctness of GitHub Copilot's Code Suggestions.

Autori: Mo, Ran Wang, Dongyu Zhan, Wenjing a ďalší

Zdroj: ACM Transactions on Software Engineering & Methodology; Sep2025, Vol. 34 Issue 7, p1-32, 32p

Predmety: PROGRAMMING languages, CODE generators, STATISTICAL accuracy, FAILURE analysis, EMPIRICAL research, COMPUTER software development

Korporácia: GITHUB Inc.

PyFibers: An open-source NEURON-Python package to simulate responses of model nerve fibers to electrical stimulation.

Autori: Marshall, Daniel P. Farah, Elie S. Musselman, Eric D. a ďalší

Zdroj: PLoS Computational Biology; 12/12/2025, Vol. 21 Issue 12, p1-30, 30p

Predmety: NERVE fibers, ELECTRIC stimulation, OPEN source software, PYTHON programming language, COMPUTER simulation, NEURONS, NEUROMODULATION

Blindspots in Python and Java APIs Result in Vulnerable Code.

Autori: BRUN, YURIY TIAN LIN SOMERVILLE, JESSIE ELISE a ďalší

Zdroj: ACM Transactions on Software Engineering & Methodology; May2023, Vol. 32 Issue 3, p1-31, 31p

Evaluating AI-generated code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and Rust

Autori: Diehl, Patrick Nader, Noujoud Brandt, Steve a ďalší

Témy: Computer Science - Software Engineering, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2405.13101

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

Autori: Komolov, Sirojiddin Mazzara, Manuel Bajwa, Imran Sarwar a ďalší

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

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

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

Autori: Li, Lixuan Li, Jie Xu, Yihui a ďalší

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

Predmety: LANGUAGE models, PROGRAMMING languages, SOURCE code, INFORMATION resources, PYTHON programming language

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

Autori: Zhao, Zixiao Fard, Fatemeh

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

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

Study and evaluation of quantum computer automatic offloading of specific calculation processing for environmental adaptation.

Autori: Yamato, Yoji

Zdroj: International Journal of Parallel, Emergent & Distributed Systems; Sep2025, Vol. 40 Issue 5, p570-582, 13p

Predmety: QUANTUM computing, HIGH performance computing, RESOURCE allocation, SOFTWARE architecture, HETEROGENEOUS computing, ALGORITHMS

Korporácia: MICROSOFT Corp.

Design of an efficient Transformer-XL model for enhanced pseudo code to Python code conversion.

Autori: Kuche, Snehal H. Gaikwad, Amit K. Deshmukh, Meghna

Zdroj: International Journal of Informatics & Communication Technology (IJ-ICT); Aug2024, Vol. 13 Issue 2, p223-230, 8p

Predmety: PYTHON programming language, TECHNOLOGICAL innovations, VIRTUAL reality, DIGITAL technology, ARTIFICIAL intelligence