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KEADA: Identifying Key Classes in Software Systems Using Dynamic Analysis and Entropy-Based Metrics.

Liuhai Wang1, Xin Du1, Bo Jiang1

  • 1School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China.

Entropy (Basel, Switzerland)
|May 28, 2022
PubMed
Summary

Identifying key classes in software maintenance is crucial. Our approach, KEADA (identifying KEy clAsses based on Dynamic Analysis and entropy-based metrics), uses dynamic analysis and entropy metrics to pinpoint essential classes, improving software understanding.

Keywords:
dynamic analysiskey classesone-order structural entropyprogram comprehensionsoftware network

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Area of Science:

  • Software Engineering
  • Computer Science

Background:

  • Software maintenance requires significant developer effort to understand complex codebases.
  • Existing methods for identifying key classes often rely on static analysis, which may not reflect runtime behavior.
  • Understanding software structure through key classes can accelerate maintenance tasks.

Purpose of the Study:

  • To propose a novel approach for identifying key classes in Java GUI software systems.
  • To leverage dynamic analysis and entropy-based metrics for more accurate key class identification.
  • To enhance developer efficiency in software maintenance by quickly pinpointing critical components.

Main Methods:

  • Developed KEADA (identifying KEy clAsses based on Dynamic Analysis and entropy-based metrics), utilizing dynamic analysis to capture runtime class interactions.
  • Represented software structure as a weighted directed network.
  • Applied an entropy-based metric, One-order Structural Entropy (OSE), to quantify class importance.

Main Results:

  • KEADA successfully identified key classes by considering actual class interactions during software execution.
  • Experimental results on three Java GUI systems demonstrated superior performance compared to seven state-of-the-art approaches.
  • Statistical evaluation using the Friedman test confirmed the effectiveness of KEADA across all tested systems.

Conclusions:

  • The proposed KEADA approach effectively identifies key classes in Java GUI software.
  • Dynamic analysis combined with entropy metrics provides a more accurate representation of class importance than static methods.
  • KEADA offers a promising solution for improving software maintainability and reducing developer effort.