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In 1981 Structural Operational Semantics (SOS) was introduced as a systematic way to define operational semantics of programming languages by a set of rules of a certain shape [G.D. Plotkin, A structural approach to operational semantics, Technical Report DAIMI ]. Subsequently, the format of SOS rules became the object of study. Using so-called Transition System Specifications (TSS's) several authors syntactically restricted the format of rules and showed several useful properties about the semantics induced by any TSS adhering to the format. This has resulted in a line of research proposing several syntactical rule formats and associated meta-theorems. Properties that are guaranteed by such rule formats range from well-definedness of the operational semantics and compositionality of behavioral equivalences to security-, time-and probability-related issues. In this paper, we provide an overview of SOS rule formats and meta-theorems formulated around them.

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Considering operators defined using Structural Operational Semantics (SOS), commutativity axioms are intuitive properties that hold for many of them. Proving this intuition is usually a laborious task, requiring several pages of boring and standard proof. To save this effort, we propose a syntactic SOS format which guarantees commutativity for a set of composition operators.

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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 FFSM Diff . 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.