Search Results - (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

Authors: Haidari, Marvin Abdi Salah, Salahudin

Index Terms: 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

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

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

Authors: Santivanez, Diego Oliva, Kevin Wong, Lenis

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

Subject Terms: 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.

Authors: Sun, Zhensu Du, Xiaoning Song, Fu et al.

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

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

Resource-Efficient & Effective Code Summarization

Authors: Afrin, Saima Call, Joseph Nguyen, Khai-Nguyen et al.

Index Terms: 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.

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

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

Subject Terms: 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.

Authors: Sapel, Patrick Gannouni, Aymen Abdelrazeq, Anas et al.

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

Subject Terms: 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.

Authors: Sandouka, Rana Aljamaan, Hamoud

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

Subject Terms: 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.

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

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

Subject Terms: 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.

Authors: Zhang, Junsan Yan, Yudie Han, Junxiao et al.

Source: 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.

Authors: Mo, Ran Wang, Dongyu Zhan, Wenjing et al.

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

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

Company/Entity: GITHUB Inc.

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

Authors: Marshall, Daniel P. Farah, Elie S. Musselman, Eric D. et al.

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

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

Blindspots in Python and Java APIs Result in Vulnerable Code.

Authors: BRUN, YURIY TIAN LIN SOMERVILLE, JESSIE ELISE et al.

Source: 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

Authors: Diehl, Patrick Nader, Noujoud Brandt, Steve et al.

Index Terms: 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.

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

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

Subject Terms: 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.

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

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

Authors: Zhao, Zixiao Fard, Fatemeh

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

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

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

Authors: Yamato, Yoji

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

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

Company/Entity: MICROSOFT Corp.

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

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

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

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