This repository contains the source code and analysis materials for the research paper "Analysis ChatGPT Potential Transforming Software" published in IEEE.
Paper Title: Analysis ChatGPT Potential Transforming Software
DOI: 10.1109/ICICCS59265.2023.10327087
Conference: IEEE International Conference on Intelligent Computing and Control Systems (ICICCS) 2023
This research analyzes ChatGPT's potential in transforming software development through various programming tasks. The study evaluates ChatGPT's capabilities across different domains including:
- Code Refactoring - Improving code structure and maintainability
- Bug Fixing - Identifying and resolving programming errors
- Machine Learning - Implementing ML algorithms and data analysis
- Simple Application Development - Creating basic software applications
chat-gpt-analysis/
├── 00. Latex Source/ # LaTeX source files for the paper
├── 01. Refactor Code/ # Code refactoring examples
├── 02. Fix Bug/ # Bug fixing demonstrations
├── 03. Machine Learning/ # ML algorithm implementations
├── 04. Simple Application/ # Simple application development
├── 05. Result/ # Final research results
├── 06. Documentation/ # Additional documentation
└── Result.pdf # Complete research paper
The analysis was conducted through systematic evaluation of ChatGPT's performance in:
- Language: C#
- Task: Refactoring poorly structured code into clean, maintainable code
- Evaluation: Code quality improvements and structural enhancements
- Languages: Python, C++
- Task: Identifying and fixing syntax errors, logical bugs, and runtime issues
- Evaluation: Correctness of bug fixes and code functionality
- Algorithm: Naive Bayes Classification
- Dataset: Diabetes prediction dataset
- Task: Implementing ML pipeline with data preprocessing, model training, and evaluation
- Evaluation: Model accuracy, confusion matrix analysis, and performance metrics
- Technology: JavaScript (HTML/CSS/JS)
- Task: Creating a Tic-Tac-Toe game with AI opponent
- Evaluation: Code completeness, functionality, and game logic implementation
The research demonstrates ChatGPT's significant potential in:
- Code Quality Improvement: Effective refactoring capabilities
- Error Resolution: Accurate bug identification and fixing
- Algorithm Implementation: Successful ML algorithm development
- Application Development: Complete functional application creation
Detailed analysis results are available in:
Result.pdf- Complete research paper05. Result/- Detailed analysis and findings- Individual folders contain specific code examples and evaluations
- Python 3.x (for ML examples)
- C++ compiler (for C++ examples)
- Web browser (for JavaScript applications)
- LaTeX (for paper compilation)
- Python: Machine learning implementations, bug fixes
- C++: Bug fixing demonstrations
- C#: Code refactoring examples
- JavaScript: Interactive web applications
This research contributes to understanding AI's role in software development by providing empirical evidence of ChatGPT's capabilities across multiple programming domains. The findings have implications for:
- Software development education
- AI-assisted programming tools
- Code quality improvement methodologies
- Automated software testing and debugging
If you use this work in your research, please cite:
@inproceedings{chatgpt_analysis_2023,
title={Analysis ChatGPT Potential Transforming Software},
author={[Author Names]},
booktitle={2023 IEEE International Conference on Intelligent Computing and Control Systems (ICICCS)},
pages={[Page Numbers]},
year={2023},
doi={10.1109/ICICCS59265.2023.10327087}
}This research is published under IEEE copyright. Please refer to the original paper for licensing terms.
This repository contains research materials and is maintained for academic purposes. For questions or collaboration opportunities, please contact the authors.
For inquiries about this research, please refer to the contact information provided in the original IEEE publication.
This repository accompanies the IEEE ICICCS 2023 paper "Analysis ChatGPT Potential Transforming Software" and contains all source code, examples, and analysis materials used in the research.