Path-oriented test cases generation based adaptive genetic algorithm

Xiaoan Bao, Zijian Xiong, Na Zhang, Junyan Qian, Biao Wu and Wei Zhang

PLOS ONE, 2017, vol. 12, issue 11, 1-17

Abstract: The automatic generation of test cases oriented paths in an effective manner is a challenging problem for structural testing of software. The use of search-based optimization methods, such as genetic algorithms (GAs), has been proposed to handle this problem. This paper proposes an improved adaptive genetic algorithm (IAGA) for test cases generation by maintaining population diversity. It uses adaptive crossover rate and mutation rate in dynamic adjustment according to the differences between individual similarity and fitness values, which enhances the exploitation of searching global optimum. This novel approach is experimented and tested on a benchmark and six industrial programs. The experimental results confirm that the proposed method is efficient in generating test cases for path coverage.

Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0187471 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 87471&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0187471

DOI: 10.1371/journal.pone.0187471

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().