Enhanced Particle Swarm Optimization for Automated Compatibility Testing in Cloud-based Distributed Systems

Authors

  • Dai Li Financial Statistics & Risk Management, Rutgers University, NJ, USA Author
  • Weishuo Lan Accounting, University of Rochester, NY, USA Author

DOI:

https://doi.org/10.64229/c54gew59

Keywords:

Enhanced particle swarm optimization, Distributed software compatibility, Cloud computing, Automated testing, TLA+, Jepsen

Abstract

Ensuring software compatibility in cloud-based distributed systems presents significant challenges due to the
heterogeneous nature of cloud environments and the complexity of distributed architectures. This paper proposes an
enhanced particle swarm optimization (PSO) approach for automated compatibility testing that addresses the limitations
of traditional testing methods. The methodology integrates improved PSO algorithms with TLA+ formal verification
and Jepsen distributed testing frameworks, incorporating dispersion adjustment mechanisms to prevent premature
convergence and enhance testing coverage. Key improvements include adaptive weight adjustment, collision radius
optimization, and fitness function modification based on branch path coverage metrics. Experimental validation
demonstrates significant improvements in compatibility testing efficiency, coverage breadth, and system robustness
across diverse cloud computing environments. The proposed approach effectively optimizes software performance and
reliability while ensuring seamless operation of distributed systems in dynamic cloud infrastructures.

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Published

2025-06-30

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Vol. 1 No. 1 (2025)

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