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Identifying co-occurrences of message chains and member ignoring method in android applications using static program

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  • 1College of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China.

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|December 19, 2025
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Detecting co-occurring Android code smells like Message Chains (MC) and Member Ignoring Method (MIM) is crucial. Our new method significantly improves detection accuracy and efficiency for better software maintainability.

Keywords:
Android-specific smellsDeep learningDynamic stacking ensembleMachine learningMember ignoring method smellMessage chain smell

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Area of Science:

  • Software Engineering
  • Computer Science

Background:

  • Multiple code smells in Android apps threaten maintainability more than single smells.
  • Existing research often focuses on detecting individual code smells, neglecting co-occurrence.

Purpose of the Study:

  • To detect prevalent co-occurring Android-specific code smells: Message Chains (MC) and Member Ignoring Method (MIM).
  • To develop an approach integrating static analysis and ensemble learning for improved detection.

Main Methods:

  • Developed a heuristic-based static analysis for MC detection, extended for MC and MIM co-occurrence.
  • Utilized an automated sample generation tool (ASSD) for labeled data.
  • Employed a Dynamic Stacking Ensemble with Backward Elimination (DSE-BE) combining ML and DL models.

Main Results:

  • The DSE-BE approach significantly improved detection performance.
  • F1 score increased from 0.774 to 0.938, and MCC from 0.623 to 0.874 compared to manual methods.
  • The DSE-BE strategy outperformed individual models and enhanced computational efficiency.

Conclusions:

  • The proposed method offers a robust solution for detecting co-occurring code smells in Android applications.
  • The approach shows strong potential for practical application in improving software quality.