Suchergebnisse - (( (statu OR stat) python code analysis ) OR ( (static OR staten) 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

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

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

Research on Binary Decompilation Optimization Based on Fine-Tuned Large Language Models for Vulnerability Detection.

Autoren: Wang, Yidan Mao, Deming Han, Ye et al.

Quelle: Electronics (2079-9292); Jan2026, Vol. 15 Issue 1, p8, 21p

Schlagwörter: LANGUAGE models, COMPUTER software security, MATHEMATICAL optimization, PENETRATION testing (Computer security), REVERSE engineering, PROGRAM transformation

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

JDroid: Android malware detection using hybrid opcode feature vector.

Autoren: Arslan, Recep Sinan

Quelle: PeerJ Computer Science; Jul2025, p1-30, 30p

Schlagwörter: MALWARE, MACHINE learning, DATA analytics, INFORMATION technology security, ENSEMBLE learning

Reviews & Products: ANDROID (Operating system)

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

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

A 1985–2023 time series dataset of absolute reservoir storage in Mainland Southeast Asia (MSEA-Res).

Autoren: Mahto, Shanti Shwarup Fatichi, Simone Galelli, Stefano

Quelle: Earth System Science Data; 2025, Vol. 17 Issue 6, p2693-2712, 20p

Schlagwörter: WATER demand management, WATER levels, TIME series analysis, WATER supply, WATERSHEDS

Keyword-labeled self-admitted technical debt and static code analysis have significant relationship but limited overlap.

Autoren: Rantala, Leevi Mäntylä, Mika Lenarduzzi, Valentina

Quelle: Software Quality Journal; Jun2024, Vol. 32 Issue 2, p391-429, 39p

Schlagwörter: DEBT, COMPUTER software development, MAINTAINABILITY (Engineering)

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

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

An extensive study of the effects of different deep learning models on code vulnerability detection in Python code.

Autoren: Wang, Rongcun Xu, Senlei Ji, Xingyu et al.

Quelle: Automated Software Engineering; Jun2024, Vol. 31 Issue 1, p1-35, 35p

Towards an understanding of memory leak patterns: an empirical study in Python.

Autoren: Chen, Jie Yu, Dongjin Hu, Haiyang

Quelle: Software Quality Journal; Dec2023, Vol. 31 Issue 4, p1303-1330, 28p

Schlagwörter: COLLECTIVE memory, MEMORY, SHORT-term memory, EMPIRICAL research, LEAK detection

State of the interactomes: an evaluation of molecular networks for generating biological insights.

Autoren: Wright, Sarah N Colton, Scott Schaffer, Leah V et al.

Quelle: Molecular Systems Biology; Jan2025, Vol. 21 Issue 1, p1-29, 29p

Schlagwörter: SYSTEMS biology, LIFE sciences, REGULATOR genes, BIOLOGICAL systems, BIOLOGICAL networks

ProgEdu4Web: An automated assessment tool for motivating the learning of web programming course.

Autoren: Duong, Hoang‐Thanh Chen, Hsi‐Min

Quelle: Computer Applications in Engineering Education; Sep2024, Vol. 32 Issue 5, p1-17, 17p

Schlagwörter: COMPUTER software industry, COMPUTER software development, SOURCE code, SYSTEMS design, QUALITY assurance

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

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python.

Autoren: Reis, João

Quelle: Logistics (2305-6290); Dec2023, Vol. 7 Issue 4, p91, 19p

Schlagwörter: MATERIAL requirements planning, PYTHON programming language, ENTERPRISE resource planning, PYTHONS, SUPPLY chain management

DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 Generate Correct Code for LoRaWAN-Related Engineering Tasks.

Autoren: Fernandes, Daniel Matos-Carvalho, João P. Fernandes, Carlos M. et al.

Quelle: Electronics (2079-9292); Apr2025, Vol. 14 Issue 7, p1428, 24p

Schlagwörter: LANGUAGE models, GENERATIVE pre-trained transformers, GENERATING functions, RESEARCH questions, NATURAL languages

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