Generating Tests for Detecting Faults in Feature Models

Abstract

We present a novel fault-based approach for testing feature models (FMs). We identify several fault classes that represent possible mistakes one can make during feature modeling. We introduce the concept of distinguishing configuration, i.e., a configuration that is able to detect a given fault. Starting from this definition, we devise a technique, based on the use of a logic solver, able either to find distinguishing configurations to be used as tests or to prove that a mutation produces an equivalent feature model. Compact test suites can be produced by exploiting an SMT solver. The experiments show that our methodology is viable and produces reasonable sized test suites in a short time. W.r.t. the approaches that use only the products, our approach has a better fault detection capability and requires fewer tests.