What is it about?
This research introduces a novel method for detecting applied software refactorings using a nature-inspired algorithm called the Andean Condor Algorithm (ACA). Unlike existing refactoring detection tools that rely heavily on thresholds—specific values that are often difficult to determine and can cause inaccuracies—our approach minimizes this reliance, enhancing the reliability of refactoring detection across diverse projects. When tested on over 500 software updates from open-source projects, ACA demonstrated superior accuracy and effectiveness compared to several state-of-the-art methods, identifying some true refactorings that other tools missed.
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Why is it important?
Our work provides a novel solution to a key challenge in software development: accurately detecting code refactorings as software systems become more complex. By reducing reliance on manually set thresholds, our approach ensures more consistent and adaptable detection across diverse projects. This is the first application of the Andean Condor Algorithm in this field, combining computational search with biological inspiration to enhance refactoring detection accuracy. This innovation can help developers better understand and maintain evolving software, reduce errors, and lower the long-term costs associated with software maintenance.
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This page is a summary of: Surpassing Threshold Barriers: Evaluating the Efficacy of Nature-Inspired Algorithms in Detecting Applied Refactorings, May 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3674558.3674568.
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