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Reverse engineering approach for improving the quality of mobile applications.

Eman K Elsayed1, Kamal A ElDahshan2, Enas E El-Sharawy1,3

  • 1Department of Mathematical and Computer Science, Faculty of Science, Al-Azhar University, (Girls Branch), Cairo, Egypt.

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Summary
This summary is machine-generated.

This study introduces an automated method to detect design anti-patterns in Android applications, improving software quality. The approach uses reverse-engineering and ontologies to identify and resolve 15 types of anti-patterns, enhancing app maintainability.

Keywords:
Anti-patternsMobile applicationsOntoUMLOntology engineeringReverse engineeringUML

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Area of Science:

  • Software Engineering
  • Computer Science
  • Mobile Computing

Background:

  • Mobile applications are complex and evolve rapidly, increasing the risk of design anti-patterns.
  • Anti-patterns degrade software quality and hinder maintenance and evolution.
  • Automatic detection of anti-patterns is crucial for improving Android application development.

Purpose of the Study:

  • To propose a general method for detecting semantic and structural design anti-patterns in mobile applications.
  • To facilitate the refactoring and quality enhancement of Android applications through automated detection.
  • To reduce the incidence of anti-patterns using ontology merging and semantic integration.

Main Methods:

  • Utilized reverse-engineering and ontology-based modeling (OWL ontology) within a UML environment.
  • Developed a method to generate OWL ontologies for mobile applications and analyze anti-pattern relationships.
  • Detected and treated 15 distinct semantic and structural anti-patterns across 29 mobile applications.

Main Results:

  • Identified 1,262 occurrences of 15 anti-patterns in 29 mobile applications.
  • Classified anti-patterns into four groups, with 'class group' having the most occurrences and 'operation group' the least.
  • Demonstrated a high positive correlation between Modelio and Protégé, increasing anti-pattern detection accuracy by approximately 11.3% with ontology integration.

Conclusions:

  • The proposed ontology-based method effectively detects semantic and structural anti-patterns in mobile applications.
  • Combining tools like Modelio and Protégé enhances detection accuracy and consistency.
  • The method provides a foundation for improving the quality and maintainability of complex Android applications.