Boosting Search Based Software Testing by Using Ensemble Methods

Abstract

Search Based Software Testing (SBST) formulates testing as an optimization problem, hence some search algorithms (e.g., Genetic Algorithms) can be used to tackle it. There are different types of coverage criteria, and the goal of SBST is to improve various test adequacy criteria. However, the major limitation of SBST is the insufficiently informed fitness functions and the inefficient search algorithms. Besides, although there are various fitness functions and search algorithms for SBST, there is little guidance on when to use one fitness function (resp., search algorithm) over another. To address these problems, we propose an ensemble strategy to boost the performance of SBST. In this paper, we deal with path coverage. Concretely, by combining multiple weak fitness functions, the heuristic information of the problem instances can be expressed more sufficiently, and therefore, a stronger fitness function can be obtained. On the other hand, by combining multiple complementary search algorithms, a hyper-heuristic search algorithm is generated and the search performance can be improved. The empirical study reveals the promising results of our proposal. Especially, for the paths that are very difficult to be covered, our ensemble method proposed in this paper outperforms other approaches significantly.