QMusExt: A Minimal (Un)satisfiable Core Extractor for Quantified Boolean Formulas

In this paper, we present QMusExt, a tool for the extraction of minimal unsatisfiable sets (MUS) from quantified Boolean formulas (QBFs) in prenex conjunctive normal form (PCNF). Our tool generalizes an efficient algorithm for MUS extraction from propositional formulas that analyses and rewrites resolution proofs generated by SAT solvers. In addition to extracting unsatisfiable cores from false formulas in PCNF, we apply QMusExt also to obtain satisfiable cores from Q-resolution proofs of true formulas in prenex disjunctive normal form (PDNF)

Preface

Collocated with the ACM/IEEE International Conference on Model-Driven Engineering Languages and Systems (MODELS), the Educators’ Symposium (EduSymp) focuses on the huge topic of software modeling education ranging from experience reports and case studies to novel pedagogical approaches. In the following, we shortly report on the 6th EduSymp held in October 2010 in Oslo

Position Paper: Software Modeling Education

Model-driven engineering (MDE) is a promising paradigm to deal with the ever increasing complexity of modern software systems. Its powerful abstraction mechanisms allow practitioners to focus on the essential development challenges, thereby hiding the irrelevant aspects of the system under development. Within the last few years, noticeable progress has been made in putting MDE into practice,particularly in those areas where the activity of programming is substituted by modeling. The recent availability of matured  concepts and stable tools has resulted in MDE becoming more applicable in software engineering projects. Nevertheless, the availability of the best technology is worthless, if it is not accepted  and used by practitioners in the field. To alleviate this problem there is a need for extensive training both in academia and industry to fully exploit the power of MDE.In this paper, we discuss the efforts being undertaken in the educational communities to promote the application of modeling and MDE technologies and discuss several challenges which still need to be addressed

A Duality-Aware Calculus for Quantified Boolean Formulas

Wir präsentieren ein formales Rahmenwerk, das es ermöglicht das Verhalten von QBF-Beweisen zu beschreiben.Learning and backjumping are essential features in search-based decision procedures for Quantified Boolean Formulas (QBF). To obtain a better understanding of such procedures, we present a formal framework, which allows to simultaneously reason on prenex conjunctive and disjunctive normal form. It captures both satisfying and falsifying search states in a symmetric way. This symmetry simplifies the framework and offers potential for further variants.W1255-N23S11408-N23(VLID)193237