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The Modified Sparrow Search Algorithm with Brown Motion and Levy Flight Strategy for the Class Integration Test Order

Chongyang Jiao1,2, Qinglei Zhou3, Wenning Zhang4

  • 1Laboratory for Advanced Computing and Intelligence Engineering, Information Engineering University, Zhengzhou 450001, China.

Biomimetics (Basel, Switzerland)
|April 25, 2025
PubMed
Summary
This summary is machine-generated.

A modified sparrow search algorithm (MSSA) effectively generates class integration test orders (CITOs), reducing stubbing costs and improving efficiency in software testing. This novel approach enhances optimization for complex integration testing scenarios.

Keywords:
Brownian motionLevy flightintegration testingsparrow search algorithmstubbing complexitytest order

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Integration testing is crucial for software quality, with Class Integration Test Orders (CITOs) being a key challenge.
  • Search-based algorithms offer effective solutions for CITO generation.
  • The Sparrow Search Algorithm (SSA) shows promise but suffers from premature convergence and local optima.

Purpose of the Study:

  • To develop a modified Sparrow Search Algorithm (MSSA) to address the limitations of the standard SSA for CITO generation.
  • To improve the efficiency and effectiveness of generating class integration test orders.

Main Methods:

  • A modified Sparrow Search Algorithm (MSSA) incorporating a good point set initialization, Brownian motion for discoverers, Levy flight for followers, and random wandering for escaping local optima.
  • Utilizing overall stubbing complexity as the fitness function to evaluate CITO sequences.
  • Experimental validation on open-source Java systems.

Main Results:

  • The MSSA significantly reduced overall stubbing complexity by 13.776% compared to the BSSA across nine systems.
  • The MSSA achieved faster convergence and reduced execution time by 23.814 seconds.
  • The proposed algorithm demonstrated superior performance across five evaluation metrics: overall stubbing complexity, attribute complexity, method complexity, convergence speed, and running time.

Conclusions:

  • The MSSA is a highly effective algorithm for generating class integration test orders, outperforming existing methods.
  • The enhancements to the SSA improve its ability to balance global and local search, leading to better optimization outcomes.
  • The MSSA offers a practical and efficient solution for reducing costs and time in software integration testing.