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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Code reuse through copy-pasting leads to code clones, impacting software quality and maintainability.
  • Existing code clone detection methods struggle with semantic clones and often overlook low-level code representations.

Purpose of the Study:

  • To develop a novel code representation for identifying both syntactic and semantic code clones in Java source code.
  • To enhance the accuracy and effectiveness of code clone detection techniques.

Main Methods:

  • Integrated high-level Abstract Syntax Tree (AST) features with low-level features from static analysis intermediate representations (e.g., Soot framework).
  • Trained fifteen machine-learning models, including ensemble classifiers like LightGBM, on a combined code representation.
  • Employed linear feature combination, outperforming multiplication and distance techniques.

Main Results:

  • The proposed method demonstrated high accuracy in detecting semantic clones on a large dataset.
  • Ensemble classifiers, particularly LightGBM, achieved exceptional accuracy.
  • Linear feature combination proved more effective than other combination methods.

Conclusions:

  • The novel code representation and machine learning models significantly improve semantic clone detection in Java.
  • This approach outperforms existing code clone detection techniques, particularly for semantic clones.
  • The findings offer a more robust solution for maintaining software quality by addressing code duplication.