Sascha Klüppelholz

Christel Baier, Sascha Klüppelholz, Timm Spork

Families of deterministic finite automata (FDFA) have been introduced as a concise automaton model that characterizes $ω$-regular languages by processing their ultimately periodic words. FDFA are known to enjoy many good properties and can be exponentially more succinct than deterministic $ω$-automata with Rabin, Streett or parity acceptance. This paper addresses two main questions: (1) Are FDFA suitable for probabilistic model checking purposes? and (2) Is it possible to obtain an even more compact representation of $ω$-regular languages by allowing the components of an FDFA to be unambiguous instead of deterministic? Question (1) is answered in the affirmative by presenting the first polynomial-time algorithm for computing the probability that a discrete-time Markov chain satisfies an $ω$-regular property represented as an FDFA. Question (2) is motivated by the fact that unambiguous finite automata may require exponentially fewer states than deterministic ones. This paper introduces a model of families of unambiguous finite automata (FUFA) that captures the class of $ω$-regular languages. FUFA can be exponentially more succinct than both FDFA and unambiguous Büchi automata, and there is a single-exponential translation from linear temporal logic (LTL) to FUFA. This stands in contrast to a double-exponential lower bound for the translation from LTL to FDFA. Moreover, the polynomial-time probabilistic model checking algorithm for discrete-time Markov chains against FDFA-specifications is extended to the case where the property is represented by an FUFA with a deterministic leading automaton.

Clemens Dubslaff, Sascha Klüppelholz, Christel Baier

In a software product line (SPL), a collection of software products is defined by their commonalities in terms of features rather than explicitly specifying all products one-by-one. Several verification techniques were adapted to establish temporal properties of SPLs. Symbolic and family-based model checking have been proven to be successful for tackling the combinatorial blow-up arising when reasoning about several feature combinations. However, most formal verification approaches for SPLs presented in the literature focus on the static SPLs, where the features of a product are fixed and cannot be changed during runtime. This is in contrast to dynamic SPLs, allowing to adapt feature combinations of a product dynamically after deployment. The main contribution of the paper is a compositional modeling framework for dynamic SPLs, which supports probabilistic and nondeterministic choices and allows for quantitative analysis. We specify the feature changes during runtime within an automata-based coordination component, enabling to reason over strategies how to trigger dynamic feature changes for optimizing various quantitative objectives, e.g., energy or monetary costs and reliability. For our framework there is a natural and conceptually simple translation into the input language of the prominent probabilistic model checker PRISM. This facilitates the application of PRISM's powerful symbolic engine to the operational behavior of dynamic SPLs and their family-based analysis against various quantitative queries. We demonstrate feasibility of our approach by a case study issuing an energy-aware bonding network device.