Suchergebnisse - (( (statne OR state) python code analysis ) OR ( aastateni 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

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

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

Autoren: He, Xincheng Liu, Xiaojin Xu, Lei

Quelle: International Journal of Software Engineering & Knowledge Engineering. Oct2023, Vol. 33 Issue 10, p1567-1597. 31p.

Schlagwörter: *COMPUTER software development, *APPLICATION program interfaces, PYTHONS, PYTHON programming language, CLASSIFICATION

Modelling the positive and negative interaction between mood and thermal sensation in the built environment using a combined Markov Chain Monte Carlo algorithm and Morris method.

Autoren: Özbey, Mehmet Furkan Turhan, Cihan

Quelle: Building Services Engineering Research & Technology. Nov2025, Vol. 46 Issue 6, p737-752. 16p.

Schlagwörter: *MARKOV chain Monte Carlo, MOOD (Psychology), THERMAL comfort, SENSITIVITY analysis, PSYCHOLOGICAL factors, ENVIRONMENTAL psychology

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

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

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

Index Begriffe: Computer Science - Programming Languages, Computer Science - Artificial Intelligence, text

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

PLangRec: Deep-learning model to predict the programming language from a single line of code.

Autoren: Rodriguez-Prieto, Oscar Pato, Alejandro Ortin, Francisco

Quelle: Future Generation Computer Systems. May2025, Vol. 166, pN.PAG-N.PAG. 1p.

Schlagwörter: *NATURAL language processing, *PROGRAMMING languages, DEEP learning, WEB-based user interfaces, SOURCE code

PyNanospacing: TEM image processing tool for strain analysis and visualization

Autoren: Sarsil, Mehmet Ali Mansoor, Mubashir Saracoglu, Mert et al.

Index Begriffe: Condensed Matter - Materials Science, Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2311.15751

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

Treefix: Enabling Execution with a Tree of Prefixes

Autoren: Souza, Beatriz Pradel, Michael

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

URL: http://arxiv.org/abs/2501.12339

Utilizing Deep Learning and Object-Based Image Analysis to Search for Low-Head Dams in Indiana, USA.

Autoren: Crookston, Brian M. Arnold, Caitlin R.

Quelle: Water (20734441); Mar2025, Vol. 17 Issue 6, p876, 24p

Schlagwörter: IMAGE analysis, WATER supply, DAMS, MODEL validation, LEARNING ability

A Firm's Operational Risk: Data Set and Empirical Evidence.

Autoren: Astvansh, Vivek Simpson, Joseph J.

Quelle: Manufacturing & Service Operations Management (M&SOM). Jan/Feb2026, Vol. 28 Issue 1, p326-341. 16p.

Schlagwörter: *OPERATIONAL risk, *TRANSFORMER models, *STAKEHOLDER theory, *LEGAL liability, *FINANCIAL disclosure, *EMPIRICAL research, NUMERICAL analysis

Geografische Kategorien: UNITED States

Automation of Software Code Analysis Using Machine Learning Methods.

Autoren: Moshkin, V. S. Dyrnochkin, A. A. Yarushkina, N. G.

Quelle: Pattern Recognition & Image Analysis; Sep2023, Vol. 33 Issue 3, p417-424, 8p

Rapid Development of Interferometric Software Using MIRIAD and Python

Autoren: Williams, Peter K. G. Law, Casey J. Bower, Geoffrey C.

Quelle: Publications of the Astronomical Society of the Pacific, 2012 . 124(916), 624-636.

Zugangs-URL: https://www.jstor.org/stable/10.1086/666604

ChatDBG: An AI-Powered Debugging Assistant

Autoren: Levin, Kyla van Kempen, Nicolas Berger, Emery D. et al.

Index Begriffe: Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Programming Languages, text

URL: http://arxiv.org/abs/2403.16354

HGAN4VD: Leveraging Heterogeneous Graph Attention Networks for enhanced Vulnerability Detection.

Autoren: Zhang, Yucheng Ju, Xiaolin Chen, Xiang et al.

Quelle: Computers & Security. Oct2025, Vol. 157, pN.PAG-N.PAG. 1p.

Schlagwörter: DEEP learning, GRAPH neural networks, FEATURE extraction, PENETRATION testing (Computer security)

Python code analysis and figures - intensity zones

Autoren: Fredrik Mentzoni

Schlagwörter: Sports science and exercise not elsewhere classified, sports scienes

Verfügbarkeit: https://doi.org/10.6084/m9.figshare.28562558.v1
https://figshare.com/articles/software/Python_code_analysis_and_figures_-_intensity_zones/28562558

LMs: Understanding Code Syntax and Semantics for Code Analysis

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

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

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