Combinatorial Test Case Suite Generation Based on Differential Evolution Algorithm

Abstract

The combinatorial testing, an effective way to improve the efficiency of software testing, is an important means to ensure the quality of software. In the combinatorial testing, the key is the combinatorial test case suite generation. According to the characteristics of the combinatorial test case suite generation problem, this paper proposes a differential evolution algorithm based on the one-test-at-a-time strategy for the solution to this problem. Through the experiments, this paper compares optimized performances of different mutations, explores the influence of algorithm parameters on the optimized performance and verifies the effectiveness and advancement of the solution.

Optimized Differential Evolution Algorithm for Software Testing

“…An approach based on the one-test-at-a-time strategy DE algorithm was proposed in Refs. [17,18] to generate a test suite with a smaller scale. The authors observed and studied the effects of various parameters on the optimization algorithm performance, proving that the scale of the combinatorial test suite generated by the DE algorithm is smaller than those of other commonly used methods.…”

CTJ: Input-Output Based Relation Combinatorial Testing Strategy Using Jaya Algorithm

“…More specifically, combinatorial test generation relates to the process of searching the optimum number of test cases for test consideration based on the interaction of t-way parameters (where t indicates the interaction strength). Many different optimization strategies are used to generate the test cases for combinatorial testing such as Harmony Search (3), Genetic Algorithm (4), Ant Colony Algorithm (4), Simplified Swarm Optimization (5), Differential Evolution Algorithm (6) and so on.…”