Výsledky vyhľadávania - (java OR jana) 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

Resource-Efficient & Effective Code Summarization

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

Zdroj: 2025 IEEE/ACM Second International Conference on AI Foundation Models and Software Engineering (Forge). :224-235

Predmety: Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models.

Autori: Yu, Hao Shen, Bo Ran, Dezhi a ďalší

Zdroj: ICSE: International Conference on Software Engineering; 2024, p1-12, 12p

Predmety: CODE generators, PRAGMATICS, BENCHMARKING (Management), MANUSCRIPTS, PYTHON programming language

Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs

Autori: Federico Cassano John Gouwar Francesca Lucchetti a ďalší

Zdroj: Proceedings of the ACM on Programming Languages. 8:677-708

Predmety: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Programming Languages, Programming Languages (cs.PL), Machine Learning (cs.LG)

Prístupová URL adresa: http://arxiv.org/abs/2308.09895

DDASR: Deep Diverse API Sequence Recommendation.

Autori: Nan, Siyu Wang, Jian Zhang, Neng a ďalší

Zdroj: ACM Transactions on Software Engineering & Methodology; Jul2025, Vol. 34 Issue 6, p1-39, 39p

Predmety: COMPUTER software development, LANGUAGE models, INFORMATION retrieval, HETEROGENEITY, JAVA programming language, APPLICATION program interfaces, PYTHON programming language

A comparison of large language models and model-driven reverse engineering for reverse engineering.

Autori: Siala, Hanan Lano, Kevin

Zdroj: Frontiers in Computer Science; 2025, p1-14, 14p

RefactorGPT: a ChatGPT-based multi-agent framework for automated code refactoring.

Autori: Karabiyik, Muhammed Abdulhamid

Zdroj: PeerJ Computer Science; Oct2025, p1-28, 28p

Predmety: SOFTWARE refactoring, CHATGPT, MULTIAGENT systems, COMPUTER software quality control

ODEL: An Experience-Augmented Self-Evolving Framework for Efficient Python-to-C++ Code Translation.

Autori: Feng, Kaiyuan Peng, Furong Wu, Jiayue

Zdroj: Applied Sciences (2076-3417); Feb2026, Vol. 16 Issue 3, p1506, 16p

Predmety: MACHINE learning, COMPUTER software reusability