Feasible test path selection by principal slicing

Abstract

We propose to improve current path-wise methods for automatic test data generation by using a new method named principal slicing. This method statically derives program slices with a near minimum number of influencing predicates, using both control and data flow information. Paths derived on principal slices to reach a certain program point are therefore very likely to be feasible. We discuss how our method improves on earlier proposed approaches, both static and dynamic. We also provide an algorithm for deriving principal slices. Then we illustrate the application of principal slicing to testing, considering a specific test criterion as an example, namely branch coverage. The example provided is an optimised method for automated branch testing: not only do we use principal slicing to obtain feasible test paths, but also we use the concept of spanning sets of branches to guide the selection of each next path, which prevents the generation of redundant tests.