Výsledky vyhledávání - (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

Autoři: Haidari, Marvin Abdi Salah, Salahudin

Témata: 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

Autoři: Ahmad, Azeem Sun, Xin

Zdroj: IEEE Access. 13:205254-205277

Témata: 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 souboru: electronic

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

Resource-Efficient & Effective Code Summarization

Autoři: Afrin, Saima Call, Joseph Nguyen, Khai-Nguyen a další

Témata: Computer Science - Software Engineering, text

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

Context‐based transfer learning for low resource code summarization.

Autoři: Guo, Yi Chai, Yu Zhang, Lehuan a další

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

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

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

Autoři: Sun, Zhensu Du, Xiaoning Song, Fu a další

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

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

Cross-Language Code Smell Detection via Transfer Learning.

Autoři: Sandouka, Rana Aljamaan, Hamoud

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

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

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

Autoři: Komolov, Sirojiddin Mazzara, Manuel Bajwa, Imran Sarwar a další

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

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

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

Autoři: Mo, Ran Wang, Dongyu Zhan, Wenjing a další

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

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

Korporace: GITHUB Inc.

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

Autoři: Aparna, K. S. Kulkarni, R. N.

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

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

Autoři: Diehl, Patrick Nader, Noujoud Brandt, Steve a další

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

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

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

Autoři: Marshall, Daniel P. Farah, Elie S. Musselman, Eric D. a další

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

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

Blindspots in Python and Java APIs Result in Vulnerable Code.

Autoři: BRUN, YURIY TIAN LIN SOMERVILLE, JESSIE ELISE a další

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

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

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

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

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

An Empirical Evaluation of Ensemble Models for Python Code Smell Detection.

Autoři: Rao, Rajwant Singh Dewangan, Seema Mishra, Alok

Zdroj: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 13, p7472, 27p

Témata: ENSEMBLE learning, FEATURE selection, BOOSTING algorithms, PYTHON programming language, COMPUTER software quality control, DESIGN failures, DATA augmentation

Creating a Python platform for students to learn full-stack development of financial applications.

Autoři: Tang, Song

Zdroj: Financial Innovation; 1/21/2026, Vol. 12 Issue 1, p1-29, 29p

Témata: PYTHON programming language, COMPUTER software development, FINANCE education, EMPLOYABILITY, FINANCE software

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

Autoři: Li, Lixuan Li, Jie Xu, Yihui a další

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

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

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

Autoři: Zhao, Zixiao Fard, Fatemeh

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

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

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

Autoři: Yamato, Yoji

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

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

Korporace: MICROSOFT Corp.

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

Autoři: 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

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

A Systematic Review of Studies on the Use of Generative Artificial Intelligence Tools in Programming Education.

Alternate Title: Programlama Eğitiminde Üretken Yapay Zeka Araçlarının Kullanımına Yönelik Yapılan Çalışmalara Sistematik Bir Bakış. (Turkish)

Autoři: ÖZGÜL, Emre

Zdroj: Kastamonu Education Journal / Kastamonu Eğitim Dergisi; 2026, Vol. 34 Issue 1, p172-185, 14p

Témata: GENERATIVE artificial intelligence, COMPUTER programming education, CHATGPT, PYTHON programming language, ACADEMIC achievement, COMPUTATIONAL thinking, EDUCATIONAL support