Open AccessPath-oriented test cases generation based adaptive genetic algorithm
Author(s) -
Xiaoan Bao,
Zijian Xiong,
Na Zhang,
Junyan Qian,
Biao Wu,
Wei Zhang
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0187471
Subject(s) - crossover , benchmark (surveying) , path (computing) , computer science , genetic algorithm , population , algorithm , test case , mathematical optimization , artificial intelligence , mathematics , machine learning , demography , regression analysis , geodesy , sociology , programming language , geography
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.