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This study introduces a new multi-label dataset for code smell detection, improving realism for software quality analysis. The dataset supports advanced detection methods, enhancing maintainability and refactoring efforts.

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Area of Science:

  • Software Engineering
  • Computer Science
  • Data Science

Background:

  • Code smells signify poor software design, impacting maintainability and necessitating accurate detection for effective refactoring.
  • Current datasets often use single-label classification, which does not reflect the complex, multi-faceted nature of code smell occurrences in real-world projects.

Purpose of the Study:

  • To develop a novel multi-label dataset for code smell detection.
  • To integrate textual and numerical features from open-source Java projects for a more realistic representation.
  • To facilitate advanced research and improve the accuracy of code smell detection tools.

Main Methods:

  • Collected code from 103 open-source Java projects.
  • Parsed code into Abstract Syntax Trees (ASTs) and extracted relevant features.
  • Annotated samples for four specific code smells (God Class, Data Class, Feature Envy, Long Method) using data cleaning and unification techniques.

Main Results:

  • Created a dataset with 107,554 samples featuring multi-label annotations, enhancing detection realism.
  • Achieved high F1 scores: 95.89% for Data Class, 94.48% for God Class, 88.68% for Feature Envy, and 88.87% for Long Method.
  • The dataset provides a robust foundation for evaluating and improving code smell detection algorithms.

Conclusions:

  • The developed dataset is valuable for advanced code smell detection studies, including fine-tuning Large Language Models (LLMs).
  • Future work can extend the dataset to include other programming languages and additional code smells, increasing its applicability and diversity.
  • This resource will contribute to better software quality and maintainability through more accurate code smell identification.