This repository contains a collection of intentionally smelly code in multiple programming languages (Java, Python, JavaScript, and C++). The dataset is used to test and evaluate the capabilities of Large Language Models (LLMs) in detecting and refactoring common code smells.
Code smells included in this dataset belong to the following categories:
- Bloaters (Large Classes, Long Methods, Primitive Obsession)
- Couplers (Feature Envy, Inappropriate Intimacy)
- Change Preventers (Divergent Change, Shotgun Surgery)
- Dispensables (Duplicate Code, Lazy Class, Data Class)
- Object-Oriented Abusers (Refused Bequest, Alternative Classes with Different Interfaces)
The primary goal of this dataset is to assess how well LLMs like GPT-4, Code Llama, and other AI models can:
- Detect various types of code smells.
- Suggest effective refactoring strategies.
- Improve code maintainability based on automated analysis.
