Connected Automated Driving: A Model-Based Approach to the Analysis of Basic Awareness Services

Cooperative awareness basic services are key components of several Connected Autonomous Vehicles (CAV) functions. We present a rigorous approach to the analysis of cooperative awareness basic services in a CAV setup. Our approach addresses a major challenge in the traditional analysis techniques of such services, namely, coming up with effective scenarios that can meaningfully cover their various behaviours, exercise the limits of these services and come up with a quantitative means for design-space exploration.Our approach integrates model-based testing and search-based testing to automatically generate scenarios and steer the scenario generation process towards generating inputs that can lead to the most severe hazards. Additionally we define other objectives that maximise the coverage of the model and the diversity of the generated test inputs. The result of applying our technique to the analysis of cooperative awareness services leads to automatically generated hazardous scenarios for parameters that abide by the ETSI ITS-G5 vehicular communications standard. We show that our technique can be used as an effective design-space exploration method and can be used to design adaptive protocols that can mitigate the hazards detected through our initial analysis

Learning by Sampling: Learning Behavioral Family Models from Software Product Lines

Family-based behavioral analysis operates on a single specification artifact, referred to as family model, annotated with feature constraints to express behavioral variability in terms of conditional states and transitions. Family-based behavioral modeling paves the way for efficient model-based analysis of software product lines. Family-based behavioral model learning incorporates feature model analysis and model learning principles to efficiently unify product models into a family model and integrate the behavior of various products into a behavioral family model. Albeit reasonably effective, the exhaustive analysis of product lines is often infeasible due to the potentially exponential number of valid configurations. In this paper, we first present a family-based behavioral model learning techniques, called FFSMDiff. Subsequently, we report on our experience on learning family models by employing product sampling. Using 105 products of six product lines expressed in terms of Mealy machines, we evaluate the precision of family models learned from products selected from different settings of the T-wise product sampling criterion. We show that product sampling can lead to models as precise as those learned by exhaustive analysis and hence, reduce the costs for family model learning