Abstract
Hybrid meta-heuristics algorithms have gained popularity in recent years to solve t-way test suite generation problems due to better exploration and exploitation capabilities of the hybridization. This paper presents the implementation of meta-heuristic search algorithms that are Migrating Birds Optimization (MBO) algorithm and Genetic Algorithm (GA) hybrid to a t-way test data generation strategy. The proposed strategy is called Elitist Hybrid MBO-GA Strategy (EMBO-GA). Based on the published benchmarking results, the result of these strategies are competitive with most existing strategies in terms of the generated test size in many of the parameter configurations. In the case where this strategy is not the most optimal, the resulting test size is sufficiently competitive.
Highlights
The need for error free software is vital today due to our dependency on software in every day’s jobs
This paper presents the implementation of meta-heuristic search algorithms that are Migrating Birds Optimization (MBO) algorithm and Genetic Algorithm (GA) hybrid to a tway test data generation strategy
A proper planning is needed for the software testing phase because half of the labor expended to develop a working program is typically spent on testing activities [1]
Summary
The need for error free software is vital today due to our dependency on software in every day’s jobs. Hybrid meta-heuristics algorithms have gained popularity in recent years to solve tway test suite generation problems due to better exploration and exploitation capabilities of the hybridization. This paper presents the implementation of meta-heuristic search algorithms that are Migrating Birds Optimization (MBO) algorithm and Genetic Algorithm (GA) hybrid to a tway test data generation strategy.
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