Software Testing is one of the indispensable parts of the software development lifecycle and structural testing is one of the most widely used testing paradigms to test various software. Structural testing relies on code path identification, which in turn leads to identification of effective paths. Aim of the current paper is to present a simple and novel algorithm with the help of an ant colony optimization, for the optimal path identification by using the basic property and behavior of the ants. This novel approach uses certain set of rules to find out all the effective/optimal paths via ant colony optimization (ACO) principle. The method concentrates on generation of paths, equal to the cyclomatic complexity. This algorithm guarantees full path coverage.

Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and webbased type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing.