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Journal of Artificial Intelligence
  Year: 2012 | Volume: 5 | Issue: 2 | Page No.: 85-90
DOI: 10.3923/jai.2012.85.90
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A Greedy Particle Swarm Optimization Strategy for T-way Software Testing

Bestoun S. Ahmed and Kamal Z. Zamli

Combinatorial strategies are used as methods or mechanisms for selecting test cases using combinations of test input parameters. We normally want that all t-way combinations of parameter values occur in the test suit at least once. Artificial intelligence base search algorithms have been used within strategies for constructing near optimal test suites. In this paper, we propose a new test generation strategy, for combinatorial testing based on greedy Particle Swarm Optimization. The basic design concepts of the strategy are demonstrated through the paper. The experimental results and comparisons of our strategy showed impressive results as far as the test suite size is considered.
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  •    A Novel Metric for Comparing the Intelligence of Two Swarm Multiagent Systems
  •    The Development of a Particle Swarm Based Optimization Strategy for Pairwise Testing
  •    Voltage Control and Reactive Power Optimisation using the Meta Heuristics Method: Application in the Western Algerian Transmission System
  •    Nonlinear Optimization of Enzyme Kinetic Parameters
  •    Generalization Improvement of Radial Basis Function Network Based on Multi-Objective Particle Swarm Optimization
How to cite this article:

Bestoun S. Ahmed and Kamal Z. Zamli, 2012. A Greedy Particle Swarm Optimization Strategy for T-way Software Testing. Journal of Artificial Intelligence, 5: 85-90.

DOI: 10.3923/jai.2012.85.90






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