Towards Sampling and Simulation-Based Analysis of Featured Weighted Automata

International audienceWe consider the problem of model checking Variability-Intensive Systems (VIS) against non-functional requirements. These requirements are typically expressed as an optimization problem over quality attributes of interest, whose value is determined by the executions of the system. Identifying the optimal variant can be hard for two reasons. First, the state-explosion problem inherent to model checking makes it increasingly complex to find the optimal executions within a given variant. Second, the number of variants can grow exponentially with respect to the number of variation points in the VIS. In this paper, we lay the foundations for the application of smart sampling and statistical model checking to solve this problem faster. We design a simple method that samples variants and executions in a uniform manner from a featured weighted automaton and that assesses which of the sampled variants/executions are optimal. We implemented our approach on top of ProVeLines, a tool suite for model-checking VIS and carried out a preliminary evaluation on an industrial embedded system design case study. Our results tend to show that sampling-based approaches indeed holds the potential to improve scalability but should be supported by better sampling heuristics to be competitive

Reducing V&V Cost of Flight Critical Systems: Myth or Reality?

This paper presents an overview of NASA research program on the V&V of flight critical systems. Five years ago, NASA started an effort to reduce the cost and possibly increase the effectiveness of V&V for flight critical systems. It is the right time to take a look back and realize what progress has been made. This paper describes our overall approach and the tools introduced to address different phases of the software lifecycle. For example, we have improved testing by developing a statistical learning approach tor defining test cases. The tool automatically identifies possible unsafe conditions by analyzing outliers in output data; using an iterative learning process, it can then generate more test cases that represent potentially unsafe regions of operation. At the code level, we have developed and made available as open source a static analyzer for C and C++ programs called IKOS. We have shown that IKOS is very precise in the analysis of embedded C programs (very few false positives) and a bit less for regular C and C++ code. At the design level, in collaboration with our NRA partners, we have developed a suite of analysis tools for Simulink models. The analysis is done in a compositional framework for scalability

Weaving an Assurance Case from Design: A Model-Based Approach

Assurance cases are used to demonstrate confidence in properties of interest for a system, e.g. For safety or security. A model-based assurance case seeks to bring the benefits of model-driven engineering, such as automation, transformation and validation, to what is currently a lengthy and informal process. In this paper we develop a model-based assurance approach, based on a weaving model, which allows integration between assurance case, design and process models and meta-models. In our approach, the assurance case itself is treated as a structured model, with the aim that all entities in the assurance case become linked explicitly to the models that represent them. We show how it is possible to exploit the weaving model for automated generation of assurance cases. Building upon these results, we discuss how a seamless model-driven approach to assurance cases can be achieved and examine the utility of increased formality and automation