Suchergebnisse - (( (statu OR stat) python code analysis ) OR ( (statement OR stateen) python code analysis ))*

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: LANGUAGE models, EXPERIMENTAL design, PYTHON programming language, GENERATIVE pre-trained transformers, QUANTITATIVE research, BENCHMARK problems (Computer science), CODE generators

A statistical approach to automated analysis of the low‐contrast object detectability test for the large ACR MRI phantom.

Autoren: Golestani, Ali M. Gee, Julia M.

Quelle: Journal of Applied Clinical Medical Physics; Jul2025, Vol. 26 Issue 7, p1-9, 9p

Schlagwörter: LINEAR statistical models, IMAGING phantoms, QUALITY control, PYTHON programming language, MAGNETIC resonance imaging

Insights and Analysis of Open-source License Violation Risks in LLMs Generated Code.

Autoren: Yibo Wang Ying Wang Yue Yu et al.

Quelle: International Journal of Software & Informatics; 2025, Vol. 15 Issue 2, p145-173, 29p

Schlagwörter: OPEN source software, COPYRIGHT infringement, LANGUAGE models, DATA analytics, CODE generators, INTELLECTUAL property infringement, DETECTION algorithms

CLM App: Interlamellar Distance of Pearlite via CLM Revisited and Automated.

Autoren: Zouhar, Martin Mikmeková, Šárka Hovjacký, Jan et al.

Quelle: Materials (1996-1944); Feb2026, Vol. 19 Issue 3, p635, 17p

Schlagwörter: AUTOMATION, IMAGE analysis, QUALITY control, MICROSTRUCTURE

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

CQLLM: A Framework for Generating CodeQL Security Vulnerability Detection Code Based on Large Language Model.

Autoren: Wang, Le Chen, Chan Zhu, Junyi et al.

Quelle: Applied Sciences (2076-3417); Jan2026, Vol. 16 Issue 1, p517, 17p

Schlagwörter: COMPUTER security vulnerabilities, LANGUAGE models, COMPUTER software correctness, SOFTWARE frameworks, PENETRATION testing (Computer security), CODE generators

Regularized Maximum Likelihood Estimation for the Random Coefficients Model in Python.

Autoren: Dunker, Fabian Mendoza, Emil Reale, Marco

Quelle: Mathematics (2227-7390); Dec2025, Vol. 13 Issue 23, p3764, 29p

Schlagwörter: PYTHON programming language, MAXIMUM likelihood statistics, CONSUMPTION (Economics), MATHEMATICAL programming, MULTILEVEL models, SCIENTIFIC computing

Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant.

Autoren: Demidova, Liliya A. Andrianova, Elena G. Sovietov, Peter N. et al.

Quelle: Data (2306-5729); Jun2023, Vol. 8 Issue 6, p109, 16p

Schlagwörter: TEACHERS' assistants, PYTHON programming language, CLASSIFICATION algorithms, HIERARCHICAL clustering (Cluster analysis), MARKOV processes, SOURCE code, ALGORITHMS

RisingTides : An Analytic Modeling Code of Tidal Effects in Binary Neutron Star Mergers.

Autoren: O'Dell, Alexander Hamilton, Maria C. Babiuc

Quelle: Astronomy (2674-0346); Sep2024, Vol. 3 Issue 3, p167-188, 22p

Schlagwörter: NEUTRONS, GRAVITATIONAL waves, PYTHONS, WAVE analysis, EQUATIONS

Distinguishing LLM-Generated from Human-Written Code by Contrastive Learning.

Autoren: Xu, Xiaodan Ni, Chao Guo, Xinrong et al.

Quelle: ACM Transactions on Software Engineering & Methodology; May2025, Vol. 34 Issue 4, p1-31, 31p

Schlagwörter: CHATGPT, LANGUAGE models, CODE generators, MACHINE learning

Programmed differently? Testing for gender differences in Python programming style and quality on GitHub.

Autoren: Brooke, Siân

Quelle: Journal of Computer-Mediated Communication; Jan2024, Vol. 29 Issue 1, p1-13, 13p

Schlagwörter: PYTHON programming language, PYTHONS, RANDOM forest algorithms, GENDER

Reviewing seas of data: Integrating image‐based bio‐logging and artificial intelligence to enhance marine conservation.

Autoren: Chimienti, Marianna Kato, Akiko Seydi, Vahid et al.

Quelle: Methods in Ecology & Evolution; Feb2026, Vol. 17 Issue 2, p272-290, 19p

Schlagwörter: ARTIFICIAL intelligence, COMPUTER vision, ANIMAL tagging, ENVIRONMENTAL sciences, COOPERATION, IMAGE processing, MARINE resources conservation, DATA integration

Computing optimum solar tilt angles for photovoltaic applications through python code simulation.

Autoren: Sumair, Muhammad Pasha, Riffat Asim Aized, Tauseef et al.

Quelle: Environmental Progress & Sustainable Energy; Sep2023, Vol. 42 Issue 5, p1-15, 15p

Schlagwörter: PYTHON programming language, SPRING, AUTUMN, ANGLES

Geografische Kategorien: KARACHI (Pakistan), PAKISTAN

Automating fuzz driver generation for deep learning libraries with large language models.

Autoren: Zheng, Tianming Meng, Fanchao Yi, Ping et al.

Quelle: Cybersecurity (2523-3246); 1/4/2026, Vol. 9 Issue 1, p1-21, 21p

Schlagwörter: LANGUAGE models, COMPUTER software testing, TEST systems, STATISTICAL reliability, MECHANIZATION

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

Autoren: Astvansh, Vivek Simpson, Joseph J.

Quelle: Manufacturing & Service Operations Management (M&SOM) (INFORMS); 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

Factors Influencing Continued Usage of Intracavernosal Injections for Erectile Dysfunction: A Retrospective Analysis.

Autoren: Ledesma, Braian Rene Codrington, Jason Velasquez, David et al.

Quelle: World Journal of Men's Health; Apr2025, Vol. 43 Issue 2, p407-414, 8p

Schlagwörter: MEDICAL personnel, PROPORTIONAL hazards models, INDEPENDENT variables, IMPOTENCE, ACADEMIC medical centers

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions.

Autoren: Stoica, Alexandru Stefan Babiceanu, Daniel Mihaescu, Marian Cristian et al.

Quelle: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 22, p3589, 19p

Schlagwörter: MACHINE learning, CLUSTERING algorithms, COMPUTER science, ALGORITHMS, SOURCE code

SparseCoder: Advancing source code analysis with sparse attention and learned token pruning.

Autoren: Yang, Xueqi Jakubowski, Mariusz Kang, Li et al.

Quelle: Empirical Software Engineering; Feb2025, Vol. 30 Issue 1, p1-30, 30p

Mathematical Model of the Software Development Process with Hybrid Management Elements.

Autoren: Semenov, Serhii Tsukur, Volodymyr Molokanova, Valentina et al.

Quelle: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 21, p11667, 44p

Schlagwörter: COMPUTER software development, AGILE software development, TELEMETRY, PREDICTION models, INTERDISCIPLINARY research, EPISTEMIC uncertainty, RISK assessment

Analysis of Program Representations Based on Abstract Syntax Trees and Higher-Order Markov Chains for Source Code Classification Task.

Autoren: Gorchakov, Artyom V. Demidova, Liliya A. Sovietov, Peter N.

Quelle: Future Internet; Sep2023, Vol. 15 Issue 9, p314, 28p

Schlagwörter: MARKOV processes, K-nearest neighbor classification, SUPPORT vector machines, SOURCE code, SYNTAX (Grammar), CLASSIFICATION algorithms