Generation of Failure Models through Automata Learning

Abstract

In the context of the AUTO-CAAS project that deals with model-based testing techniques applied in the automotive domain, we present the preliminary ideas and results of building generalised failure models for non-conformant software components. These models are a necessary building block for our upcoming efforts to detect and analyse failure causes in automotive software built with AUTOSAR components. Concretely, we discuss how to build these generalised failure models using automata learning techniques applied to a guided model-based testing procedure of a failing component. We illustrate our preliminary findings and experiments on a simple integer queue implemented in the C programming language.