Suchergebnisse - (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

Autoren: Haidari, Marvin Abdi Salah, Salahudin

Index Begriffe: 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

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: print

Zugangs-URL: 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

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

Index Begriffe: Computer Science - Software Engineering, text

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

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

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

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

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

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

Cross-Language Code Smell Detection via Transfer Learning.

Autoren: Sandouka, Rana Aljamaan, Hamoud

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

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

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

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

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

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

Firma/Körperschaft: GITHUB Inc.

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

Autoren: Zhao, Zixiao Fard, Fatemeh

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

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

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

Autoren: Siala, Hanan Lano, Kevin

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

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

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

Index Begriffe: Computer Science - Software Engineering, Computer Science - Artificial Intelligence, text

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

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

Autoren: Li, Lixuan Li, Jie Xu, Yihui et al.

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

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

A Systematic Survey on Large Language Models for Code Generation.

Autoren: Jabrw, Sardar K. Sarhan, Qusay I.

Quelle: ARO: The Scientific Journal of Koya University; 2025, Vol. 13 Issue 2, p83-99, 17p

Schlagwörter: LANGUAGE models, CODE generators, PROGRAMMING languages, EVALUATION research, COMPUTER software development, COMPUTER security vulnerabilities, NATURAL language processing

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

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1319-1578; https://doaj.org/toc/2213-1248

Zugangs-URL: https://doaj.org/article/0e8d85656442482785bc981b326c25a4

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

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

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

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

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

Autoren: Yu, Hao Shen, Bo Ran, Dezhi et al.

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

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

Code Smells and Development Efficiency in Native and Cross-Platform Mobile Applications: A Study of Java, Kotlin, and Flutter.

Autoren: Ibrokhimov Sardorbek Rustam Ugli Junseok Cheon Gyun Woo

Quelle: Journal of Information Processing Systems; Aug2025, Vol. 21 Issue 4, p401-412, 12p

Code Plagiarism Checking Function and Its Application for Code Writing Problem in Java Programming Learning Assistant System †.

Autoren: Htet, Ei Ei Wai, Khaing Hsu Aung, Soe Thandar et al.

Quelle: Analytics (2813-2203); Mar2024, Vol. 3 Issue 1, p46-62, 17p

Schlagwörter: PLAGIARISM, JAVA programming language, PYTHON programming language, SUPPORT vector machines

Geografische Kategorien: JAPAN

Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code.

Autoren: Pan, Rangeet Ibrahimzada, Ali Reza Krishna, Rahul et al.

Quelle: ICSE: International Conference on Software Engineering; 2024, p1-13, 13p

Schlagwörter: LANGUAGE models, PYTHON programming language, C++, EMPIRICAL research, SOURCE code

CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution

Autoren: Jana, Prithwish Jha, Piyush Ju, Haoyang et al.

Index Begriffe: Computer Science - Programming Languages, Computer Science - Artificial Intelligence, Computer Science - Software Engineering, I.2.7, I.2.5, D.2, text

URL: http://arxiv.org/abs/2306.06755

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

Autoren: Karabiyik, Muhammed Abdulhamid

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

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