Natural Computing for Automatic Test Data Generation Approach Using Spanning Tree Concepts

Abstract

The weight is assigned to all edges of graph according to a uniform policy and focus is on the leaves nodes of the Spanning Tree of Control Flow Graph of Program. If all leaves nodes of Spanning tree are covered by the test cases, then number of test cases required or testing the program can be reduced to great extent. Further the spanning tree is based on the concept of maximum spanning tree of CFG which helps in finding the critical paths of CFG. The chances of finding errors in critical are more as compare to the normal path. Covering of all the leaves nodes of Spanning Tree give guarantee for the coverage of Critical path also. Further the paper also shows that number of test cases required in case of Spanning tree is less as compare to the Dominance tree concept. For the generation of fast, unique and reliable test cases a optimization technique which is based on natural computing concept is used which is called as on Swine Influenza Models Based Optimization (SIMBO). The relation between nodes has been used as fitness function in this paper. Finally, the results in the paper show the effectiveness of SIMBO techniques as compare to Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).