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Dynamic stacking ensemble for cross-language code smell detection.

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This study introduces dynamic ensembles for detecting code smells in Java and Python, achieving comparable accuracy to full stacking ensembles but with reduced complexity. These methods offer a more stable and efficient approach to identifying software design issues.

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Area of Science:

  • Software Engineering
  • Machine Learning
  • Computer Science

Background:

  • Code smells are indicators of poor software design and implementation.
  • Machine learning for code smell detection is an active research area, but models often lack stability and focus primarily on Java.
  • Existing methods for code smell detection can be complex and may not generalize well across programming languages.

Purpose of the Study:

  • To propose dynamic ensemble methods for code smell detection in both Java and Python.
  • To investigate the effectiveness of greedy search and backward elimination strategies in building these dynamic ensembles.
  • To compare the complexity and detection performance of dynamic ensembles against full stacking ensembles.

Main Methods:

  • Development of dynamic ensemble models using greedy search and backward elimination strategies.
  • Evaluation of detection performance on four Java and two Python code smells.
  • Comparative analysis of model complexity and detection accuracy with full stacking ensembles.

Main Results:

  • Greedy search and backward elimination yielded distinct sets of base models for dynamic ensembles.
  • Dynamic ensembles demonstrated comparable detection performance to full stacking ensembles with no significant loss.
  • Dynamic ensembles, particularly those using backward elimination, resulted in less complex models for most investigated code smells.

Conclusions:

  • Dynamic stacking ensembles provide an effective and stable method for detecting Java and Python code smells.
  • These dynamic ensembles offer a reduced complexity alternative to full stacking ensembles.
  • The proposed strategies facilitate improved code smell detection across multiple programming languages.