Search Results - (( (statement OR stat) python code analysis ) OR ( (statement OR statni) python code analysis ))

Python Data Analyst's Toolkit - Learn Python and Python-Based Libraries with Applications in Data Analysis and Statistics by Rajagopalan, Gayathri

“… The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts - programming with Python, data analysis and visualization, and statistics…”
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Assessing code readability in Python programming courses using eye‐tracking by Segedinac, Milan, Savić, Goran, Zeljković, Ivana, Slivka, Jelena, Konjović, Zora

“… This paper assesses the readability of Python code statements commonly used in undergraduate programming courses…”
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Mitigating the Uncertainty and Imprecision of Log-Based Code Coverage Without Requiring Additional Logging Statements by Xu, Xiaoyan, Cogo, Filipe R., McIntosh, Shane

“…Understanding code coverage is an important precursor to software maintenance activities (e.g., better testing…”
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Detecting Memory Errors in Python Native Code by Tracking Object Lifecycle with Reference Count by Ma, Xutong, Yan, Jiwei, Zhang, Hao, Yan, Jun, Zhang, Jian

“…Third-party Python modules are usually implemented as binary extensions by using native code (C/C++…”
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GraphPyRec: A novel graph-based approach for fine-grained Python code recommendation by Zong, Xing, Zheng, Shang, Zou, Haitao, Yu, Hualong, Gao, Shang

“… Significant progress has been made in code recommendation for static languages in recent years, but it remains challenging for dynamic languages like Python as accurately determining data flows…”
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Evaluating Large Language Models in Class-Level Code Generation by Du, Xueying, Liu, Mingwei, Wang, Kaixin, Wang, Hanlin, Liu, Junwei, Chen, Yixuan, Feng, Jiayi, Sha, Chaofeng, Peng, Xin, Lou, Yiling

“…, function-level or statement-level code generation), which mainly asks LLMs to generate one single code unit (e.g…”
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Stress-Constrained Topology Optimization for Commercial Software: A Python Implementation for ABAQUS by Fernandes, Pedro, Ferrer, Àlex, Gonçalves, Paulo, Parente, Marco, Pinto, Ricardo, Correia, Nuno

“…, using validated FEM commercial software. The methodology was validated by comparing the sensitivity analysis with the results obtained through finite differences and solving two benchmark problems with the following optimizers…”
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Quantifying the interpretation overhead of Python by Zhang, Qiang, Xu, Lei, Zhang, Xiangyu, Xu, Baowen

“…•Quantitative overhead analysis for the Python interpreter based on sampling.•Overhead decomposition from both opcode and project composition perspectives…”
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Python for Everyone: Learn and polish your coding skills in Python (English Edition) by Saurabh Chandrakar, Dr. Nilesh Bhaskarrao Bahadure

“…A hands-on guide that will help you to write clean and efficient code in Python Key Features…”
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Data Science Fundamentals with R, Python, and Open Data by Cremonini, Marco

“…Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projectsOrganized with a strong focus on open data, This book discusses concepts…”
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Robustness Analysis of Watermarking Techniques for LLM-Generated Code by Cao, Di, Liu, Shigang, Zhang, Jun, Xiang, Yang

“…The widespread adoption of large language models (LLMs) for code generation raises critical concerns regarding authorship attribution and accountability, motivating the development of watermarking techniques to trace model outputs…”
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Visualization of Aqueous Geochemical Data Using Python and WQChartPy by Yang, Jing, Liu, Honghua, Tang, Zhonghua, Peeters, Luk, Ye, Ming

“… This method note presents WQChartPy, an open‐source Python package developed to plot a total of 12 diagrams for analysis of aqueous geochemical data…”
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Artificial intelligence programming with Python: from zero to hero by Xiao, Perry

“…A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python…”
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ProPy: Prolog-based Fault Localization Tool for Python by Morin, Janneke, Ghosh, Krishnendu

“… By examining which statements of the program were executed in successful versus unsuccessful test cases, then extract insight into the "suspiciousness" of areas of the program…”
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The Python workshop: a practical, no-nonsense introduction to Python development by Andrew Bird, Bird, Dr Lau Cher Han, Han, Mario Corchero Jimenez, Jimenez, Graham Lee, Lee, Corey Wade, Wade

“…Cut through the noise and get real results with a step-by-step approach to learning Python 3…”
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NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs by Di Domenico, Daniel, Lima, João V. F., Cavalheiro, Gerson G. H.

“… As a counterpoint to that trend, this paper presents a performance and programming effort analysis with Python, an interpreted and high-level language, which was applied to develop the kernels…”
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Semi-Supervised Code Translation Overcoming the Scarcity of Parallel Code Data by Zhu, Ming, Karim, Mohimenul, Lourentzou, Ismini, Yao, Danfeng Daphne

“… MIRACLE leverages static analysis and compilation to generate synthetic parallel code datasets with enhanced quality and alignment to address the challenge of data scarcity…”
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Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation by Oda, Yusuke, Fudaba, Hiroyuki, Neubig, Graham, Hata, Hideaki, Sakti, Sakriani, Toda, Tomoki, Nakamura, Satoshi

“…Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages…”
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Beyond Functional Correctness: An Empirical Evaluation of Large Language Models for Text-to-Code Generation by Nogueira, Rodrigo Pato, Vieira, Marco, Campos, Joao R.

“…Large Language Models (LLMs) have become increasingly popular for text-to-code generation, a task that involves converting natural language descriptions into code…”
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Understanding the Robustness of Transformer-Based Code Intelligence via Code Transformation: Challenges and Opportunities by Li, Yaoxian, Qi, Shiyi, Gao, Cuiyun, Peng, Yun, Lo, David, Lyu, Michael R., Xu, Zenglin

“… Specifically, 27 and 24 code transformation strategies are implemented for two popular programming languages, Java and Python, respectively…”
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