Theoretical runtime analyses of search algorithms on the test data generation for the Triangle Classification problem

Software Testing plays an important role in the life cycle of software development. Because software testing is very costly and tedious, many techniques have been proposed to automate it. One technique that has achieved good results is the use of Search Algorithms. Because most previous work on search algorithms has been of an empirical nature, there is a need for theoretical results that confirm the feasibility of search algorithms applied to software testing. Such theoretical results might shed light on the limitations and benefits of search algorithms applied in this context. In this paper, we formally analyse the expected runtime of three different search algorithms on the problem of Test Data Generation for an instance of the Triangle Classification program. The search algorithms that we analyse are Random Search Hill Climbing and Alternating Variable Method. We believe that this is a necessary first step that will lead and help the Software Engineering community to better understand the role of Search Based Techniques applied to software testing. © 2008 IEEE.