Cellular-genetic test data generation

Abstract

Test Data Generation is the soul of automated testing. The dream of having efficient and robust automated testing software can be fulfilled only if the task of designing a robust automated test data generator can be accomplished. In the work we explore the gaps in the existing techniques and intend to fill these gaps by proposing new algorithms. The following work presents algorithms that handle almost all the constructs of procedural programming languages. The proposed technique uses cellular automata as its base. The use of Cellular Automata brings a blend of artificial life to the work. The work is a continuation of our earlier attempt to amalgamate Cellular Automata based algorithms to generate test data. The technique has been applied to C programs and is currently being tested on a financial enterprise resource planning system. Since, the solution of most of the problems can be found by observing nature, we must explore artificial nature to accomplish the above task.