Path-oriented test cases generation based adaptive genetic algorithm

<div><p>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.</p></div

Description and the corresponding parameter settings to target path of six industrial programs.

<p>Description and the corresponding parameter settings to target path of six industrial programs.</p

Triangle type program and its corresponding control flow graph.

<p>Triangle type program and its corresponding control flow graph.</p

Experiments settings and results of triangle classifier program.

<p>Experiments settings and results of triangle classifier program.</p

The success rate of six industrial programs with four methods.

<p>The success rate of six industrial programs with four methods.</p

The automatic generation of test case model based on genetic algorithm.

<p>The automatic generation of test case model based on genetic algorithm.</p

Test cases generation based on improved adaptive genetic algorithm (IAGA).

<p>Test cases generation based on improved adaptive genetic algorithm (IAGA).</p

Results obtained in the experiment comparison between IAGA and Random approach.

<p>Results obtained in the experiment comparison between IAGA and Random approach.</p

Branch distance functions for several kinds of branch predicates.

<p>Branch distance functions for several kinds of branch predicates.</p

Experiments results of evaluations of six industrial programs.

<p>Experiments results of evaluations of six industrial programs.</p