Test Data Generation Based on Test Path Discovery Using Intelligent Water Drop

Background

Various techniques (Korel, 1990; Pedrycz & Peters, 1998; Briand, 2002; McMinn, 2004; Harman, 2007; Kewen et al., 2009; Srivastava et al., 2009; Srivastava & Baby, 2010) have been proposed for automated testing to reduce efforts to a remarkable extent. Swarm optimization techniques are widely used for test data generation. Ant Colony Optimization (ACO) is one of them (Li & Lam, 2005; Kewen et al., 2009; Srivastava et al., 2009). Automatic test data generation based on ACO (Kewen et al., 2009) has introduced a model to generate test data using the branch function technique. It has solved the problem of local optimization, but this approach is applicable only for numeric data types and also the model is not suitable for object oriented programs and other types of input behavior. Software test data generation using ACO (Li & Lam, 2005) has also proposed a solution for state-based software testing but complete coverage is not possible by the proposed approach.