First International Workshop on Bidirectional Transformations (BX 2012): Preface

Bidirectional transformations (bx) are a mechanism for maintaining the consistency of two (or perhaps more) related sources of information. Such sources can be databases, software models, documents, or their abstract models like graphs or trees. BX are an emerging topic in a wide range of research areas with prominent presence at top conferences in different fields. The methodologies used for bx range from classical program transformation to graph transformation techniques, from ad-hoc techniques for data synchronization to the development of domain-specific languages and their integration. The First International Workshop on Bidirectional Transformations (BX 2012) established a dedicated venue for bx in all relevant areas. It was initiated by the participants of the Dagstuhl Seminar "Bidirectional Transformations" (bx) in Germany 2011, which continued the GRACE International Meeting on Bidirectional Transformations held in Japan 2008.The workshop BX 2012 took place at the Tallinn University of Technology,Estonia on the 25th of March 2012, as a satellite event of ETAPS 2012, the European Joint Conferences on Theory and Practice of Software

Graph Subsumption in Abstract State Space Exploration

In this paper we present the extension of an existing method for abstract graph-based state space exploration, called neighbourhood abstraction, with a reduction technique based on subsumption. Basically, one abstract state subsumes another when it covers more concrete states; in such a case, the subsumed state need not be included in the state space, thus giving a reduction. We explain the theory and especially also report on a number of experiments, which show that subsumption indeed drastically reduces both the state space and the resources (time and memory) needed to compute it.Comment: In Proceedings GRAPHITE 2012, arXiv:1210.611

Metamodel Instance Generation: A systematic literature review

Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page

Category theory : definitions and examples

Category theory was invented as an abstract language for describing certain structures and constructions which repeatedly occur in many branches of mathematics, such as topology, algebra, and logic. In recent years, it has found several applications in computer science, e.g., algebraic specification, type theory, and programming language semantics. In this paper, we collect definitions and examples of the basic concepts in category theory: categories, functors, natural transformations, universal properties, limits, and adjoints

Measurement of statistical evidence on an absolute scale following thermodynamic principles

Statistical analysis is used throughout biomedical research and elsewhere to assess strength of evidence. We have previously argued that typical outcome statistics (including p-values and maximum likelihood ratios) have poor measure-theoretic properties: they can erroneously indicate decreasing evidence as data supporting an hypothesis accumulate; and they are not amenable to calibration, necessary for meaningful comparison of evidence across different study designs, data types, and levels of analysis. We have also previously proposed that thermodynamic theory, which allowed for the first time derivation of an absolute measurement scale for temperature (T), could be used to derive an absolute scale for evidence (E). Here we present a novel thermodynamically-based framework in which measurement of E on an absolute scale, for which "one degree" always means the same thing, becomes possible for the first time. The new framework invites us to think about statistical analyses in terms of the flow of (evidential) information, placing this work in the context of a growing literature on connections among physics, information theory, and statistics.Comment: Final version of manuscript as published in Theory in Biosciences (2013