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Related Experiment Video

Updated: Jan 9, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Enhancing GUI test case generation with multi-objective quasi-oppositional genetic sparrow.

N Jayalakshmi1, K Sakthivel2

  • 1Department of Computer Applications, PSNA College of Engineering and Technology, Dindigul, 624622, India. jayalakshmi908765@gmail.com.

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|December 5, 2025
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Summary
This summary is machine-generated.

This study introduces the Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) for efficient Graphical User Interface (GUI) test case generation. OOGSSA enhances test coverage and fault detection, improving software reliability.

Keywords:
Graphical user interfaces (GUI)Quasi-oppositional genetic sparrow search algorithm (OOGSSA)Search-based software testing (SBST)Sparrow search algorithm (SSA)User friendly

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Computational Intelligence

Background:

  • Ensuring Graphical User Interface (GUI) quality is vital for robust and user-friendly software.
  • Traditional automated testing methods often struggle with complex GUI interactions and achieving comprehensive coverage.

Purpose of the Study:

  • To propose a novel, efficient method for GUI test case generation using an advanced optimization algorithm.
  • To enhance GUI testing by maximizing test coverage, minimizing redundancy, and improving fault detection capabilities.

Main Methods:

  • Development of the Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA), an enhancement of the Sparrow Search Algorithm.
  • Integration of elite opposition-based learning and genetic evolution to boost population diversity and convergence speed.
  • Automatic examination of GUI event interactions and adaptive learning for test suite refinement.

Main Results:

  • Experimental evaluation demonstrated high mouse event coverage (95%) with a test suite size of 75.
  • Achieved high similarity scores (Jaccard Index: 0.75-0.82) and low dissimilarity scores (DiceJaroWinkler: 0.18-0.31) in test case generation.
  • OOGSSA exhibited superior adaptability and intelligence compared to traditional automation and dynamic test generation tools.

Conclusions:

  • OOGSSA presents a scalable and intelligent approach for GUI testing, significantly improving software reliability and efficiency.
  • The method effectively addresses multi-objective optimization goals in GUI test generation.
  • Potential computational cost for highly complex GUIs is a consideration, but overall, OOGSSA offers a significant advancement.