Search Results - (("Python programs") OR ("Python progress"))

Semantic Versioning for Python Programs

Authors: Carvalho, Luís NOVALincs RUN

Contributors: Carvalho, Luís NOVALincs RUN

Subject Terms: Software evolution, type theory, Artificial Intelligence, Human-Computer Interaction, Software

File Description: application/pdf

Relation: 979-8-4007-0384-3; PURE: 83888407

Availability: http://hdl.handle.net/10362/164728

Student Perceptions of Learning through Original and AI-Generated Python Programs from a Software Quality Perspective

Authors: Mark Frydenberg Anqi Xu Jennifer Xu

Source: Information Systems Education Journal. 2025 23(4):34-56.

Peer Reviewed: Y

Page Count: 23

Descriptors: Student Attitudes, Programming, Computer Software, Quality Assurance, Artificial Intelligence, Computer Science Education, Evaluation, Introductory Courses, Problem Based Learning, Coding, College Students

Towards understanding the security issues of Python programs

Authors: Hongcheng Fan Di Liu Jielun Wu et al.

Source: Proceedings of the 16th International Conference on Internetware. :1-12

A Scaffolded Approach for Tracing Control-Flow in Simplified Python Programs

Authors: Gnaneswar Kulindala Anushka Srikanth Venkatesh Choppella

Source: Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1. :51-57

Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices

Authors: Shehroz Khan Gaadha Sudheerbabu Bianca Elena Staicu et al.

Source: 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). :217-225

ESBMC-Python: A Bounded Model Checker for Python Programs: A Bounded Model Checker for Python Programs

Authors: Bruno Farias Rafael Menezes Eddie B. de Lima Filho et al.

Source: Farias, B, Menezes, R, Filho, E B D L, Sun, Y & Cordeiro, L 2024, ESBMC-Python : A Bounded Model Checker for Python Programs. in The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA).

Subject Terms: Software Engineering (cs.SE), FOS: Computer and information sciences, Bounded Model Checking, Computer Science - Software Engineering, Formal Verification, Python

File Description: application/pdf

Access URL: http://arxiv.org/abs/2407.03472
https://research.manchester.ac.uk/en/publications/08f79973-9b61-4fd6-8590-b42095741f43
https://pure.manchester.ac.uk/ws/files/337130786/issta2024_1.pdf
https://research.manchester.ac.uk/en/publications/08f79973-9b61-4fd6-8590-b42095741f43
https://pure.manchester.ac.uk/ws/files/337130784/2407.03472v1.pdf

Fault-Proneness of Python Programs Tested By Smelled Test Code

Authors: Yuki Fushihara Hirohisa Aman Sousuke Amasaki et al.

Source: 2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). :373-378

py_tps2tnt y py_tm2tnt: dos programas en Python para procesamiento de datos morfométricos en análisis cladísticos con TNT.

Alternate Title: py_tps2tnt and py_tm2tnt: Two Python programs for processing morphometric data for cladistic analyses with TNT.

Authors: Liria, Jonathan Soto-Vivas, Ana

Source: Revista Peruana de Biología. jul2025, Vol. 32 Issue 2, p1-6. 5p.

Subject Terms: *MORPHOMETRICS, *CLADISTIC analysis, *PHYLOGENY, *PYTHON programming language, *OPEN source software, *ELECTRONIC data processing

Using LLMs to Extract UML Class Diagrams from Java and Python Programs:An Empirical Study

Authors: Siala, Hanan Lano, Kevin

Source: Siala, H & Lano, K 2025, 'Using LLMs to Extract UML Class Diagrams from Java and Python Programs : An Empirical Study', CEUR Workshop Proceedings, vol. 4122.

Subject Terms: Unified Modeling Language (UML), UML Class Diagram, Model-driven Reverse Engineering (MDRE), Machine Learning, Large Language Models (LLMs), Java programs, Python programs

File Description: application/pdf

Availability: https://kclpure.kcl.ac.uk/portal/en/publications/73e37044-11bc-48ac-8cff-42d183c617b6
https://kclpure.kcl.ac.uk/ws/files/362383754/Using_LLMs_to_extract_UML_class_diagrams_from_Java_and_Version_of_Record.pdf
https://ceur-ws.org/Vol-4122/

Using LLMs to Extract OCL Specifications from Java and Python Programs:An Empirical Study

Authors: Siala, Hanan Lano, Kevin

Source: Siala, H & Lano, K 2025, 'Using LLMs to Extract OCL Specifications from Java and Python Programs : An Empirical Study', CEUR Workshop Proceedings, vol. 4122.

Subject Terms: Object Constraint Language (OCL), Machine Learning, Large Language Models (LLMs), Reverse engineering, Java programs, Python programs

File Description: application/pdf

Availability: https://kclpure.kcl.ac.uk/portal/en/publications/1badd32b-4bb5-4104-8a41-f0d7543d96bc
https://kclpure.kcl.ac.uk/ws/files/362382813/Using_LLMs_to_extract_OCL_specifications_from_Java_and_Python_programs_Version_of_Record.pdf
https://ceur-ws.org/Vol-4122/

Basic Structure and Elementary Components of Python Programs

Authors: Muhammad Azhar Iqbal

Source: Python for Agriculturists ISBN: 9783032014412

Using ChatGPT for Converting Sequential Python Programs into Parallel Code

Authors: Denny B. Czejdo Wiktor B. Daszczuk Waldemar Grabski

Source: Lecture Notes in Networks and Systems ISBN: 9783031927331

Using Python programs to determine the secondary eclipse depth of Kepler-12 b and its geometric albedo

Authors: Liyang Qi

Source: Theoretical and Natural Science. 12:136-140

Subject Terms: 13. Climate action

Type-aware LLM-based Regression Test Generation for Python Programs

Authors: Liu, Runlin Zhang, Zhe Hu, Yunge et al.

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

Access URL: http://arxiv.org/abs/2503.14000

Leveraging Llms for Abstracting Uml and Ocl Representations From Java and Python Programs

Authors: Hanan Abdulwahab Siala Kevin Lano

Discovering Parallelisms in Python Programs

Authors: Siwei Wei Guyang Song Senlin Zhu et al.

Source: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. :832-844

ANALYSIS OF MISCONCEPTIONS IN PYTHON PROGRAMS USING CHATGPT

Authors: Monika Tomcsányiová

Source: ICERI Proceedings. 1:281-288

Poster: Improving Spectrum Based Fault Localization For Python Programs Using Weighted Code Elements

Authors: Qusay Idrees Sarhan Árpád Beszédes

Source: 2023 IEEE Conference on Software Testing, Verification and Validation (ICST). :478-481

Орман өрттерін бақылауда ұшқышсыз ұшу аппараттарының деректерін өңдеу үшін Python бағдарламаларын пайдалану. (Kazakh)

Alternate Title: Using Python programs to process data from unmanned aerial vehicles for forest fire monitoring. (English)
Использование программ Python для обработки данных беспилотных летательных аппаратов для мониторинга лесных пожаров. (Russian)

Authors: Молдамурат, Х. Калманова, Д. Базарбек, А. et al.

Source: Bulletin of the L.N. Gumilyov Eurasian National University. Technical science & Technology series; 2025, Vol. 153 Issue 4, p139-151, 13p

Subject Terms: FOREST fire management, GEOGRAPHIC information systems, DRONE aircraft, DATA visualization, DATA mining, ENVIRONMENTAL security, PYTHON programming language, COMMERCIAL drones

Mapping Python Programs to Vectors Using Recursive Neural Encodings

Authors: Paassen, Benjamin McBroom, Jessica Jeffries, Bryn et al.

Source: Journal of Educational Data Mining. 2021 13(3):1-35.

Peer Reviewed: Y

Page Count: 35

Descriptors: Data Analysis, Programming Languages, Networks, Novices, Prediction, Decision Making, Computer Software, Learning Analytics, Computer Science Education, Higher Education