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Detecting refactoring type of software commit messages based on ensemble machine learning algorithms.

Dimah Al-Fraihat1, Yousef Sharrab2, Abdel-Rahman Al-Ghuwairi3

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This study introduces an advanced machine learning approach to accurately detect software refactoring types from commit messages. The novel method, utilizing XGBoost and TF-IDF, achieved 100% accuracy, significantly improving code quality analysis.

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Area of Science:

  • Software Engineering
  • Machine Learning
  • Natural Language Processing

Background:

  • Refactoring enhances software design without changing external behavior.
  • Commit messages are crucial for tracking code changes.
  • Classifying refactoring types from commit messages is challenging but vital for software quality.

Purpose of the Study:

  • To propose a novel ensemble machine learning approach for accurate refactoring type detection.
  • To improve upon existing methods for classifying refactoring documentation.

Main Methods:

  • Utilized four ensemble machine learning algorithms.
  • Employed text cleaning, preprocessing, and feature engineering (TF-IDF, bag-of-words).
  • Applied hyperparameter optimization and binary transformation (one-vs-one, one-vs-rest).

Main Results:

  • The TF-IDF feature engineering technique outperformed other methods.
  • The XGBoost algorithm with TF-IDF achieved 100% accuracy across all metrics.
  • Results surpassed current state-of-the-art performance on the same dataset.

Conclusions:

  • The proposed ensemble machine learning approach effectively detects refactoring types.
  • This method significantly enhances the internal quality assessment of software.
  • Achieving 100% accuracy demonstrates the potential of advanced ML in software engineering.