A New Software Data-Flow Testing Approach via Ant Colony Algorithms

Search-based optimization techniques (e.g., hill climbing, simulated annealing, and genetic algorithms) have been applied to a wide variety of software engineering activities including cost estimation, next release problem, and test generation. Several search based test generation techniques have been developed. These techniques had focused on finding suites of test data to satisfy a number of control-flow or data-flow testing criteria. Genetic algorithms have been the most widely employed search-based optimization technique in software testing issues. Recently, there are many novel search-based optimization techniques have been developed such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Immune System (AIS), and Bees Colony Optimization. ACO and AIS have been employed only in the area of control-flow testing of the programs. This paper aims at employing the ACO algorithms in the issue of software data-flow testing. The paper presents an ant colony optimization based approach for generating set of optimal paths to cover all definition-use associations (du-pairs) in the program under test. Then, this approach uses the ant colony optimization to generate suite of test-data for satisfying the generated set of paths. In addition, the paper introduces a case study to illustrate our approach.