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: print

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

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

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

Resource-Efficient & Effective Code Summarization

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

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

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

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

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

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

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

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

KeldyshQFT: A C++ codebase for real-frequency multiloop functional renormalization group and parquet computations of the single-impurity Anderson model.

Authors: Ritz, Nepomuk Ge, Anxiang Walter, Elias et al.

Source: Journal of Chemical Physics; 8/7/2024, Vol. 161 Issue 5, p1-21, 21p

Subject Terms: RENORMALIZATION group, ANDERSON model, STATISTICAL correlation, DATA structures, FUNCTIONAL groups

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

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

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

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

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

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 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

A Systematic Survey on Large Language Models for Code Generation.

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

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

Subject Terms: 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

Authors: Yi Rong Yan Zhou

Source: Journal of King Saud University: Computer and Information Sciences, Vol 37, Iss 10, Pp 1-20 (2025)

Subject Terms: 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

File Description: electronic resource

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

Access URL: https://doaj.org/article/0e8d85656442482785bc981b326c25a4

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

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

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

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

Wenwang: Toward Effectively Generating Code Beyond Standalone Functions via Generative Pre-trained Models.

Authors: Yu, Hao Shen, Bo Zhang, Jiaxin et al.

Source: ACM Transactions on Software Engineering & Methodology; Sep2025, Vol. 34 Issue 7, p1-27, 27p

Subject Terms: CODE generators, PROBABILISTIC generative models, MATHEMATICAL optimization

Comparison of two methods in determining hearing loss type and hearing loss degree: mobile application coded with artificial neural networks and conditional structures.

Authors: Disci, Ahmet Yasin Konukseven, Ozlem Tanisir Disci, Rukiye

Source: Egyptian Journal of Otolaryngology; 7/4/2025, Vol. 41 Issue 1, p1-16, 16p

Subject Terms: HEARING disorder diagnosis, MOBILE apps, AUDIOMETRY, DESCRIPTIVE statistics, MEDICAL coding, ARTIFICIAL neural networks, DEEP learning, COMPARATIVE studies, DATA analysis software

Geographic Terms: TURKEY