Random Generation of Test Inputs for Implicitly Defined Subdomains

In traditional random testing, samples are taken from the set of all possible values for the input types. However, for many programs testing effectiveness can be improved by focusing on a relevant subdomain defined implicitly by the program behavior. This paper presents an algorithm for identifying and randomly selecting inputs from implicitly defined subdomains. The algorithm dynamically constructs and refines a model of the input domain and is biased toward sparsely covered regions in order to accelerate boundary identification and uniform coverage. This method has several desirable qualities: (1) it requires no knowledge of the source code of the software being tested, (2) inputs are selected from an approximately uniform distribution across the subdomain, and (3) algorithmic running time overhead is negligible. We present the requirements for a solution and our algorithm. We also evaluate our solution for both an artificial model and a real-world aircraft collision-avoidance program.