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Abstract. Strategies (and certificates) for quantified Boolean formulas (QBFs) are of high practical relevance as they facilitate the verification of results returned by QBF solvers and the generation of solutions to problems formulated as QBFs. State of the art approaches to obtain strategies require traversing a Q-resolution proof of a QBF, which for many real-life instances is too large to handle. In this work, we consider the long-distance Q-resolution (LDQ) calculus, which allows particular tautological resolvents. We show that for a family of QBFs using the LDQ-resolution allows for exponentially shorter proofs compared to Q-resolution. We further show that an approach to strategy extraction originally presented for Q-resolution proofs can also be applied to LDQ-resolution proofs. As a practical application, we consider search-based QBF solvers which are able to learn tautological clauses based on resolution and the conflict-driven clause learning method. We prove that the resolution proofs produced by these solvers correspond to proofs in the LDQ calculus and can therefore be used as input for strategy extraction algorithms. Experimental results illustrate the potential of the LDQ calculus in search-based QBF solving.

Abstract. The employment of optimistic model versioning systems allows multiple developers of a team to work independently on their local copies of a software model. The merging process towards one consolidated version obviously turns out to be challenging when performed without any tool support. Recently, several sophisticated approaches for model merging have been presented. However, even for multi-view modeling languages like UML, which distribute the information on the system under development over different diagrams, diagrams of different views are merged independently of each other. Hence, inconsistencies between different views are likely to be introduced into the merged model. We suggest to solve this problem by exploiting information stored in one view as constraint for the computation of a consolidated version of another view. More specifically, we demonstrate how state machines can guide the integration of parallel changes performed on a sequence diagram. We give a concise formal description of this problem and suggest a translation to propositional logic.

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Abstract. Current optimistic model versioning systems, which are indispensable to coordinate the collaboration within teams, are able to detect several kinds of conflicts between two concurrently modified versions of one model. These systems support the detection of syntactical problems such as contradicting changes, violations of the underlying metamodel, and violations of OCL constraints. However, violations of the semantics remain unreported. In this paper, we suggest to use redundant information inherent in models to check if the semantics is violated during the merge process. In particular, we exploit the information encoded in state machine diagrams to validate evolving sequence diagrams by means of the model checker SPIN.

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