Knowledge-based software testing agent using evolutionary learning with cultural algorithms
David Alfred Ostrowski, Robert G. Reynolds · 2003
Software testing is extremely difficult in the context of large scale engineering applications. We suggest that the application of the white and black box testing methods within a cultural algorithm environment will present a successful approach to fault detection. In order to utilize both a functional approach and a structural approach, two cultural algorithms will be applied within this tool. The first cultural algorithm will utilize the black box testing by learning equivalence classes of faulty input/output pairs. These equivalence classes are then passed over to the second cultural algorithm that will apply program slicing techniques to determine program slices from the data. The goal will be to pinpoint specific faults within the program design. Through the searching of the program code, this approach can be considered as behavioral mining of a program.