Test Cost Optimization Using Tabu Search

In order to deliver a complete reliable software product, testing is performed. As testing phase carries on, cost of testing process increases and it directly affects the overall project cost. Many a times it happens that the actual cost becomes more than the estimated cost. Cost is considered as the most important parameter with respect to software testing, in software industry. In recent year’s researchers have done a variety of work in the area of Cost optimization by using various concepts like Genetic Algorithm, simulated annealing and Automation in generation of test data etc. This paper proposes an efficient cost effective approach for optimizing the cost of testing using Tabu Search (TS), which will provide maximum code coverage along with the concepts of Dijkstra’s Algorithm which will be implemented in Aspiration criteria of Tabu Search in order to optimize the cost and generate a minimum cost path with maximum coverage.