Suchergebnisse - (( (stavebna OR state) python code analysis ) OR ( stateat python code analysis ))*

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

GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries.

Autoren: Fachada, Nuno Fernandes, Daniel Fernandes, Carlos M. et al.

Quelle: Future Internet. Sep2025, Vol. 17 Issue 9, p412. 28p.

Schlagwörter: *Experimental design, *Quantitative research, Language models, Python programming language, Generative pre-trained transformers, Benchmark problems (Computer science), Code generators

Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning

Autoren: Zhao, Wenting Abdelaziz, Ibrahim Dolby, Julian et al.

Schlagwörter: FOS: Computer and information sciences, Computer Science - Programming Languages, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Programming Languages (cs.PL)

Zugangs-URL: http://arxiv.org/abs/2301.05108

Numerical Analyses of a SAW Gas Sensor in a Non-Steady State

Autoren: T. Hejczyk J. Wrotniak P. Powroźnik et al.

Quelle: International Journal of Electronics and Telecommunications, Vol vol. 71, Iss No 4 (2025)

Schlagwörter: gas sensors, numerical modeling, ingebrigtsen’s formula, dmmp, (rr)-p3ht, acoustoelectric analysis (naa), Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Telecommunication, TK5101-6720

Dateibeschreibung: electronic resource

Relation: https://journals.pan.pl/Content/136909/6-5168-Hejczyk_sk.pdf; https://doaj.org/toc/2081-8491; https://doaj.org/toc/2300-1933

Zugangs-URL: https://doaj.org/article/c9d170a1144c4d9f86026066ca8dbfea

Measurement and Development for Automated Secure Coding Solutions

Autoren: Frantz, Miles Eugene

Thesis Advisors: Computer Science and#38; Applications, Yao, Danfeng, Rajagopalan, Raj, Meng, Na, Chung, Taejoong Tijay, Brown, Dwayne Christian

Schlagwörter: Large Language Models, Machine Learning, Static Code Analysis, Python, Java, Benchmark

Dateibeschreibung: ETD; application/pdf

Verfügbarkeit: https://hdl.handle.net/10919/121100

Python API Misuse Mining and Classification Based on Hybrid Analysis and Attention Mechanism

Autoren: Xincheng He Xiaojin Liu Lei Xu

Quelle: International Journal of Software Engineering and Knowledge Engineering. 33:1567-1597

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

A Study of Large Language Models in Detecting Python Code Violations

Autoren: Hekar A. Mohammed Salih Qusay I. Sarhan

Quelle: ARO-The Scientific Journal of Koya University, Vol 13, Iss 2 (2025)

Schlagwörter: Large language models, Software metrics, Software quality, Static code analysis, Technology, Science

Dateibeschreibung: electronic resource

Relation: https://aro.koyauniversity.org/index.php/aro/article/view/2395; https://doaj.org/toc/2410-9355; https://doaj.org/toc/2307-549X

Zugangs-URL: https://doaj.org/article/a66cf05353b54e19a03b46c0b089d1fc

Smart Compiler Assistant: An AST based Python Code Analysis

Autoren: Aneesh, Ch Saumik, Gembali Varun, Kvv et al.

Quelle: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) ; page 1-6

Verfügbarkeit: https://doi.org/10.1109/icccnt61001.2024.10724220
http://xplorestaging.ieee.org/ielx8/10723818/10723316/10724220.pdf?arnumber=10724220

Programming models to support data science workflows

Autoren: Ramón-Cortés Vilarrodona, Cristián

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors

Thesis Advisors: Badia Sala, Rosa M. (Rosa Maria), Ejarque Artigas, Jorge

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Distributed computing, High-performance computing, Data science pipelines, Task-ba workflows, Dataflows, Containers (Computer sicience), COMPSs, PyCOMPSs, AutoParallel, Docker, Àrees temàtiques de la UPC::Informàtica

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/669728
https://dx.doi.org/10.5821/dissertation-2117-330142

Time Series Analysis with Python Cookbook : Practical Recipes for the Complete Time Series Workflow, From Modern Data Engineering to Advanced Forecasting and Anomaly Detection

Autoren: Tarek A. Atwan

Resource Type: eBook.

Schlagworte: Python (Computer program language), Machine learning

Categories: COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / General, COMPUTERS / Data Science / Data Analytics

MoveApps: a serverless no-code analysis platform for animal tracking data.

Autoren: Kölzsch, Andrea Davidson, Sarah C. Gauggel, Dominik et al.

Quelle: Movement Ecology. 7/18/2022, Vol. 10 Issue 1, p1-12. 12p.

Analyzing LLM-Generated Code According to Four ISO/IEC 5055:2021 Categories

Autoren: Rasmus Krebs Somnath Mazumdar

Quelle: IEEE Access, Vol 13, Pp 202482-202499 (2025)

Schlagwörter: Analysis, ISO, LLMs, maintainability, performance, Python, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/11269792/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/3faae1da4e0b47beb1e1ac9da1ee6b78

Empirical Auto-Evaluation of Python Code for Performance Analysis of Transformer Network Using T5 Architecture

Autoren: Shivam Biswas Jaya Sil Isha Ganguli et al.

Quelle: 2021 8th International Conference on Smart Computing and Communications (ICSCC). :75-79

Zugangs-URL: https://ieeexplore.ieee.org/document/9528123/footnotes

DeVAIC: A tool for security assessment of AI-generated code.

Autoren: Cotroneo, Domenico De Luca, Roberta Liguori, Pietro

Quelle: Information & Software Technology. Jan2025, Vol. 177, pN.PAG-N.PAG. 1p.

Schlagwörter: *COMPUTER software development, *ARTIFICIAL intelligence, *SECURITY management, CODE generators, SOURCE code

LMs: Understanding Code Syntax and Semantics for Code Analysis

Autoren: Ma, Wei Liu, Shangqing Lin, Zhihao et al.

Schlagwörter: Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence

Zugangs-URL: http://arxiv.org/abs/2305.12138

Data analysis and visualization of electric vehicle engineering

Autoren: null Aditya Gaurav Gupta null Aryan Gaurav Gupta null Michelle Tashi Wongmu Bhutia

Quelle: World Journal of Advanced Research and Reviews. 25:1796-1804

Hands-on 3: Introduction to developing Python computational pipelines for predictive machine learning modelling of livestock data.

Autoren: Tulpan, Dan¹

Quelle: Journal of Animal Science. 2024 Supplement, Vol. 102, p68-68. 1/4p.

Schlagworte: Machine learning, Personal computers, Animal science, Data modeling, Python programming language

The state of the late Quaternary megafauna extinction debate: a systematic review and analysis.

Autoren: Stewart, Mathew Peters, Carli Ziegler, Michael J. et al.

Quelle: Frontiers in Mammal Science. 2025, p1-27. 27p.

Integrating multi-modal data fusion approaches for analysis of dairy cattle vocalizations.

Autoren: Jobarteh, Bubacarr Mincu-Iorga, Madalina Gavojdian, Dinu et al.

Quelle: Frontiers in Veterinary Science. 2025, p1-13. 13p.

Application of Artificial Intelligence Technologies in Livestock Management.

Alternate Title: Hayvancılıkta Yapay Zekâ Teknolojilerinin Uygulanması.

Autoren: DİLAVER, Hatice DİLAVER, Kamil Fatih

Quelle: Journal of Animal Science & Economics. Jul2025, Vol. 4 Issue 2, p64-74. 11p.

Schlagworte: Natural language processing, Artificial neural networks, Supervised learning, Artificial intelligence, Agriculture