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GALP: a hybrid artificial intelligence algorithm for generating covering array

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A Correction to this article was published on 05 May 2021

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Abstract

Today, there are a lot of useful algorithms for covering array (CA) generation, one of the branches of combinatorial testing. The major CA challenge is the generation of an array with the minimum number of test cases (efficiency) in an appropriate run-time (performance), for large systems. CA generation strategies are classified into several categories: computational and meta-heuristic, to name the most important ones. Generally, computational strategies have high performance and yield poor results in terms of efficiency, in contrast, meta-heuristic strategies have good efficiency and lower performance. Among the strategies available, some are efficient strategies but suffer from low performance; conversely, some others have good performance, but is not such efficient. In general, there is not a strategy that enjoys both above-mentioned metrics. In this paper, it is tried to combine the genetic algorithm and the Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity to produce the appropriate test suite in terms of efficiency and performance. Also, a simple and effective minimizing function is employed to increase efficiency. The evaluation results show that the proposed strategy outperforms the existing approaches in terms of both efficiency and performance.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, 38156-8-8349, Iran

    Sajad Esfandyari & Vahid Rafe

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  1. Sajad Esfandyari
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  2. Vahid Rafe
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SE was responsible for conceptualization, methodology, software and writing—original draft; VR was responsible for supervision, writing—reviewing and editing.

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Correspondence to Sajad Esfandyari.

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The original article has been updated: Due to reference update.

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Esfandyari, S., Rafe, V. GALP: a hybrid artificial intelligence algorithm for generating covering array. Soft Comput 25, 7673–7689 (2021). https://doi.org/10.1007/s00500-021-05788-0

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