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An approach for test data generation using program slicing and particle swarm optimization

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Abstract

Heuristic search-based test data generation has a potential higher efficiency in software testing with path covering. However, these approaches are suffered in covering the long and complex path. In this paper, we propose a method for generating test data based on program slicing and particle swarm optimization. With the interest points selected from a target path, we perform a program slicing to remove the statements which are irrelevant to the interest points. Our method simplifies the target path and the actual path to get a better fitness value. After program slices obtained, the population is evolved using particle swarm optimization to improve the efficiency of test data generation.

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Acknowledgments

This work is supported in part by the NSFC Project under Grant Nos. 60970032, 61202006, and 61340037, the “QingLan” Project and “333” Project of Jiangsu Province, the Fundamental Research Funds for the Central Universities under Grant No. 2013NB17, the University Natural Science Research Project of Jiangsu Province under Grant No. 12KJB520014, and the Graduate Training Innovative Projects Foundation of Jiangsu Province under Grant No. CXZZ12_0935.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shujuan Jiang, Dandan Yi, Xiaolin Ju, Lingsai Wang & Yingqi Liu

  2. School of Computer Science and Technology, Nantong University, Nantong, 226019, China

    Xiaolin Ju

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  1. Shujuan Jiang
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  2. Dandan Yi
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  3. Xiaolin Ju
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  4. Lingsai Wang
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  5. Yingqi Liu
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Correspondence to Shujuan Jiang.

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Jiang, S., Yi, D., Ju, X. et al. An approach for test data generation using program slicing and particle swarm optimization. Neural Comput & Applic 25, 2047–2055 (2014). https://doi.org/10.1007/s00521-014-1692-z

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  • DOI: https://doi.org/10.1007/s00521-014-1692-z

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