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A Parallel Genetic Algorithm Based on Spark for Pairwise Test Suite Generation

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Abstract

Pairwise testing is an effective test generation technique that requires all pairs of parameter values to be covered by at least one test case. It has been proven that generating minimum test suite is an NP-complete problem. Genetic algorithms have been used for pairwise test suite generation by researchers. However, it is always a time-consuming process, which leads to significant limitations and obstacles for practical use of genetic algorithms towards large-scale test problems. Parallelism will be an effective way to not only enhance the computation performance but also improve the quality of the solutions. In this paper, we use Spark, a fast and general parallel computing platform, to parallelize the genetic algorithm to tackle the problem. We propose a two-phase parallelization algorithm including fitness evaluation parallelization and genetic operation parallelization. Experimental results show that our algorithm outperforms the sequential genetic algorithm and competes with other approaches in both test suite size and computational performance. As a result, our algorithm is a promising improvement of the genetic algorithm for pairwise test suite generation.

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Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, 211106, China

    Rong-Zhi Qi & Zhi-Jian Wang

  2. College of Science, Hohai University, Nanjing, 211106, China

    Shui-Yan Li

Authors
  1. Rong-Zhi Qi
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  2. Zhi-Jian Wang
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  3. Shui-Yan Li
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Correspondence to Rong-Zhi Qi.

Additional information

This work is supported by the Fundamental Research Funds for the Central Universities of China under Grant Nos. 2010B06914 and 2013B07514.

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Qi, RZ., Wang, ZJ. & Li, SY. A Parallel Genetic Algorithm Based on Spark for Pairwise Test Suite Generation. J. Comput. Sci. Technol. 31, 417–427 (2016). https://doi.org/10.1007/s11390-016-1635-5

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  • DOI: https://doi.org/10.1007/s11390-016-1635-5

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