Search Results - "Python Code"

SemetonBug: A Machine Learning Model for Automatic Bug Detection in Python Code Based on Syntactic Analysis

Authors: Bahtiar Imran Selamet Riadi Emi Suryadi et al.

Source: Jurnal Informatika. 12:75-80

Automated Function Level Python Code Summarization Using a Transformer Based Model

Authors: Yaswanth Kharidu Yaswanth Kharidu Kharidu

Source: INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. :1-9

Enhancing Python Code Smell Detection with Heterogeneous Ensembles

Authors: Rana Sandouka Hamoud Aljamaan

Source: International Journal of Software Engineering and Knowledge Engineering. 35:963-986

New Cryptosystem Using Sumudu Transform, Combination of Three Functions with Simulation and Python Code

Authors: Raut P.P. Hiwarekar A.P.

Source: Punjab University Journal of Mathematics. :770-780

Automatic Generation of Data Assimilation Python Code: ——Application of Large Language Models to Programming——

Authors: Nobuki KAME Kazuro HIRAHARA Masayuki KANO et al.

Source: Zisin (Journal of the Seismological Society of Japan. 2nd ser.). 78:13-30

Security Analysis of Python Code Using Bandit: A Streamlit-Based Git Repository Scanner

Authors: Deeksha D R Prajna M R SAVITHA C K et al.

Source: International Journal of Computer Science and Mobile Computing. 14:12-14

FlowFusion: Pseudocode to Python Code with Flowchart Precision

Authors: Keerti Kahartmol

Source: International Journal for Research in Applied Science and Engineering Technology. 13:4469-4476

Multi-scale Swarm of Large Language Models for Python Code Generation

Authors: Yajing Tan Qiqi Duan Lijun Sun et al.

Source: Lecture Notes in Computer Science ISBN: 9789819509812

Revealing the Next Frontier in Python Code Review: Harnessing Automated Machine Learning with Random Forest for Unmatched Code Quality Evaluation

Authors: Md Arshad Rejuwan Shamim Trapty Agarwal et al.

Source: Computational Intelligence Solutions for Real-Life Problems ISBN: 9781998511013

LLMs Based Data Augmentation Techniques for Python Code Refactoring

Authors: Vasilica Moldovan Rares Patcas Simona Motogna

Source: Lecture Notes in Computer Science ISBN: 9783032041890

Naïve Chunking Strategies for Scalable SAS-Python Code Translation in the U.S. Census Bureau

Authors: Milne, Cameron Mamantov, Ellie Lynagh, John

Subject Terms: Code Translation, SAS, Large Language Models (LLMs), Python

Accelerating Python Code with Parallel I/O

Authors: Robin Varghese Hashirul Quadir Ladjel Bellatreche et al.

Source: Lecture Notes in Computer Science ISBN: 9783032020871

A Case Study of Python Code Calling the DeepSeek API

Authors: Lin Wang

Source: 2025 2nd International Conference on Artificial Intelligence and Digital Technology (ICAIDT). :461-464

Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMS

Authors: Midolo, Alessandro Di Penta, Massimiliano

Source: 2025 IEEE/ACM 33rd International Conference on Program Comprehension (ICPC). :01-11

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

Access URL: http://arxiv.org/abs/2501.17024

Measuring and Improving the Efficiency of Python Code Generated by LLMs Using CoT Prompting and Fine-Tuning

Authors: Ramya Jonnala Jeong Yang Young Lee et al.

Source: IEEE Access, Vol 13, Pp 119657-119681 (2025)

Subject Terms: LLMs, code generation, Electrical engineering. Electronics. Nuclear engineering, CoT, fine-tuning, vertex AI, Python, TK1-9971

Access URL: https://doaj.org/article/f8d90c3451a24f0dbb548f6c7b4fa3b2

Evaluating the Effects on Comprehension in Python Code from Idiomatic Development

Authors: Joshua A.C. Behler

Source: Proceedings of the 2025 Symposium on Eye Tracking Research and Applications. :1-3

Evaluation of Generative AI Models in Python Code Generation: A Comparative Study

Authors: Dominik Palla Antonin Slaby

Source: IEEE Access, Vol 13, Pp 65334-65347 (2025)

Subject Terms: LLM, python, generative AI, software development, Electrical engineering. Electronics. Nuclear engineering, Automatization, TK1-9971

Access URL: https://doaj.org/article/6a0bb64651314af19b0a2e5a3cb89fe4

OpCodeBERT: A Method for Python Code Representation Learning by BERT with Opcode

Authors: Canyu Qiu Jianxun Liu Xiaocong Xiao et al.

Source: IEEE Transactions on Software Engineering. :1-14

Hybrid solar chp microgrid optimization: python code framework using real equipment data

Authors: Amr Abbass

Source: Advanced Engineering Letters. 4:164-174

Optimizing Python Code Metrics Feature Reduction Through Meta-Analysis and Swarm Intelligence

Authors: Marina Ivanova Zamira Kholmatova Nikolay Pavlenko

Source: Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering. :338-345