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We discuss the use of formal techniques to improve the concept phase of product realisation. As an industrial application, a new concept of interventional X-ray systems has been formalized, using model checking techniques and the simulation of formal models. cop. Springer International Publishing Switzerland 2015.

First-order translations of large mathematical repositories allow discovery of new proofs by automated reasoning systems. Large amounts of available mathematical knowledge can be re-used by combined AI/ATP systems, possibly in unexpected ways. But automated systems can be also more easily misled by irrelevant knowledge in this setting, and finding deeper proofs is typically more difficult. Both large-theory AI/ATP methods, and translation and data-mining techniques of large formal corpora, have significantly developed recently, providing enough data for an initial comparison of the proofs written by mathematicians and the proofs found automatically. This paper describes such an initial experiment and comparison conducted over the 50000 mathematical theorems from the Mizar Mathematical Library.

An important goal of research in description logics (DLs) and related logic-based KR formalisms is to identify the worst-case complexity of reasoning. Such results, however, measure the complexity of a logic as a whole. For example, reasoning in the basic DL is ExpTime-complete, which means that constructors can be used in a way so that exponential time is strictly required for solving a reasoning problem. It is, however, well known that, given two knowledge bases of roughly the same size, reasoning with one knowledge base may be much more difficult than with the other, depending on the interaction of the axioms in the KBs. Thus, existing worst-case complexity results provide only a very coarse measure of reasoning complexity, and they do not tell us much about the "hardness" of each individual knowledge base. © 2012 Springer-Verlag.

An important goal of research in description logics (DLs) and related logic-based KR formalisms is to identify the worst-case complexity of reasoning. Such results, however, measure the complexity of a logic as a whole. For example, reasoning in the basic DL is ExpTime-complete, which means that constructors can be used in a way so that exponential time is strictly required for solving a reasoning problem. It is, however, well known that, given two knowledge bases of roughly the same size, reasoning with one knowledge base may be much more difficult than with the other, depending on the interaction of the axioms in the KBs. Thus, existing worst-case complexity results provide only a very coarse measure of reasoning complexity, and they do not tell us much about the "hardness" of each individual knowledge base. © 2012 Springer-Verlag.

Modern architectures provide weaker memory consistency guarantees than sequential consistency. These weaker guarantees allow programs to exhibit behaviours where the program statements appear to have executed out of program order. Fortunately, modern architectures provide memory barriers (fences) to enforce the program order between a pair of statements if needed. Due to the intricate semantics of weak memory models, the placement of fences is challenging even for experienced programmers. Too few fences lead to bugs whereas overuse of fences results in performance degradation. This motivates automated placement of fences. Tools that restore sequential consistency in the program may insert more fences than necessary for the program to be correct. Therefore, we propose a property-driven technique that introduces "reorder-bounded exploration" to identify the smallest number of program locations for fence placement. We implemented our technique on top of CBMC; however, in principle, our technique is generic enough to be used with any model checker. Our experimental results show that our technique is faster and solves more instances of relevant benchmarks as compared to earlier approaches., 18 pages, 3 figures, 4 algorithms. Version change reason : new set of results and publication ready version of FM 2015

Loop under-approximation is a technique that enriches C programs with additional branches that represent the effect of a (limited) range of loop iterations. While this technique can speed up the detection of bugs significantly, it introduces redundant execution traces which may complicate the verification of the program. This holds particularly true for verification tools based on Bounded Model Checking, which incorporate simplistic heuristics to determine whether all feasible iterations of a loop have been considered. We present a technique that uses \emph{trace automata} to eliminate redundant executions after performing loop acceleration. The method reduces the diameter of the program under analysis, which is in certain cases sufficient to allow a safety proof using Bounded Model Checking. Our transformation is precise---it does not introduce false positives, nor does it mask any errors. We have implemented the analysis as a source-to-source transformation, and present experimental results showing the applicability of the technique.