# Enhancing GUI test case generation with multi-objective quasi-oppositional genetic sparrow

## TL;DR

This paper introduces a new algorithm for generating GUI test cases that improves coverage and efficiency.

## Contribution

The novel Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) combines genetic evolution and opposition learning for GUI testing.

## Key findings

- OOGSSA achieves 95% mouse event coverage and a test suite size of 75.
- The algorithm shows better adaptability compared to traditional testing tools.
- It has a Jaccard Similarity Index of 0.75–0.82 and DiceJaroWinkler Dissimilarity of 0.18–0.31.

## Abstract

To achieve robust and user friendly software, it is crucial to make sure that Graphical User Interfaces (GUI) is of quality and reliable. The paper suggests a new method of Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) of generating test cases efficiently in GUI. The ultimate goal is to have multi-objective maximization through various goals of maximum test coverage, reduced redundancy and enhanced fault detection. It is an upgrading of the Sparrow Search Algorithm to combine the imitation of elite opposition based learning with genetic evolution in order to improve the population diversity and convergence rate. The suggested method automatically examines the interactions among GUI events and refines the obtained test suite with the help of adaptive learning. Oogssa is experimental evaluated, with a test suite size of 75, mouse event coverage of 95 and through various test cases with Jaccard Similarity Index (0.75–0.82) and DiceJaroWinkler Dissimilarity (0.18–0.31). OOGSSA has better adaptability which is intelligent as compared to traditional tools of automation testing, dynamic test generation and Non-manual coverage. Notice however its possibility to be costly in terms of computation in the event of very complicated GUI structure. Altogether, OOGSSA offers a scalable and intelligent scheme to test GUI and improve reliability and efficiency in today software.