On requirement verification for evolving Statecharts specifications

Software development processes have been evolving from rigid, pre-specified, and sequential to incremental, and iterative. This evolution has been dictated by the need to accommodate evolving user requirements and reduce the delay between design decision and feedback from users. Formal verification techniques, however, have largely ignored this evolution and even when they made enormous improvements and found significant uses in practice, like in the case of model checking, they remained confined into the niches of safety-critical systems. Model checking verifies if a system's model M satisfies a set of requirements, formalized as a set of logic properties \u3a6. Current model-checking approaches, however, implicitly rely on the assumption that both the complete model M and the whole set of properties \u3a6 are fully specified when verification takes place. Very often, however, M is subject to change because its development is iterative and its definition evolves through stages of incompleteness, where alternative design decisions are explored, typically to evaluate some quality trade-offs. Evolving systems specifications of this kind ask for novel verification approaches that tolerate incompleteness and support incremental analysis of alternative designs for certain functionalities. This is exactly the focus of this paper, which develops an incremental model-checking approach for evolving Statecharts. Statecharts have been chosen both because they are increasingly used in practice natively support model refinements

Learning and evolution in dynamic software product lines

A Dynamic Software Product Line (DSPL) aims at managing run-time adaptations of a software system. It is built on the assumption that context changes that require these adaptations at run-time can be anticipated at design-time. Therefore, the set of adaptation rules and the space of configurations in a DSPL are predefined and fixed at design-time. Yet, for large-scale and highly distributed systems, anticipating all relevant context changes during design-time is often not possible due to the uncertainty of how the context may change. Such design-time uncertainty therefore may mean that a DSPL lacks adaptation rules or configurations to properly reconfigure itself at run-time. We propose an adaptive system model to cope with design-time uncertainty in DSPLs. This model combines learning of adaptation rules with evolution of the DSPL configuration space. It takes particular account of the mutual dependencies between evolution and learning, such as using feedback from unsuccessful learning to trigger evolution. We describe concrete steps for learning and evolution to show how such feedback can be exploited. We illustrate the use of such a model with a running example from the cloud computing domain

Verification for Reliable Product Lines

Many product lines are critical, and therefore reliability is a vital part of their requirements. Reliability is a probabilistic property. We therefore propose a model for feature-aware discrete-time Markov chains as a basis for verifying probabilistic properties of product lines, including reliability. We compare three verification techniques: The enumerative technique uses PRISM, a state-of-the-art symbolic probabilistic model checker, on each product. The parametric technique exploits our recent advances in parametric model checking. Finally, we propose a new bounded technique that performs a single bounded verification for the whole product line, and thus takes advantage of the common behaviours of the product line. Experimental results confirm the advantages of the last two techniques