7th International Workshop on Graph Based Tools (GraBaTs 2012): Preface

The 7th International Workshop on Graph Based Tools is the continuation of the GraBaTs series of workshops which provides a forum for researchers and practitioners interested in the development and application of graph-based tools. The purpose of this workshop is to survey the state of the art of graph-based tools, bring together developers of graph-based tools in different application fields and to encourage new tool development cooperations. GraBaTs 2012 is the 7th edition in a line of workshops which started 10 years ago at ICGT'02

ModGraph meets Xcore: Combining Rule-Based and Procedural Behavioral Modeling for EMF

Model-driven software engineering aims at increasing productivity bydeveloping high-level executable models. The Eclipse Modeling Framework (EMF)significantly contributes toward this goal. Unfortunately, EMF supports only structural models based on the Ecore metamodel. Recently, Xcore has been developed to extend EMF with behavioral modeling. To this end, Xcore provides a single textual language for both structural and behavioral modeling. While Xcore follows a procedural approach to behavioral modeling, ModGraph is an EMF-based tool based on a rule-based paradigm (graph transformation rules, which allow to specify behavior in a declarative way). The combination of EMF, Xcore, and ModGraph results in an environment for model-driven software engineering which provides full-fledged support for both structural and behavioral modeling. Altogether, we obtain an environment in which software engineers are concerned only with models rather than with programs

Triple Graph Grammars or Triple Graph Transformation Systems? A Case Study from Software Configuration Management

Abstract. Triple graph grammars have been used to specify consistency maintenance between inter-dependent and evolving models at a high level of abstraction. On a lower level, consistency maintenance may be specified by a triple graph transformation system, which takes care of all operational details required for executing consistency maintenance operations. This paper presents a case study from software configuration management in which we decided to hand-craft a triple graph transformation system rather than to generate it from a triple graph grammar. The case study demonstrates some limitations concerning the kinds of consistency maintenance problems which can be handled by triple graph grammars

Assessment of treatment change for sexual offenders against children: comparing different methodologies based on psychometric self-report

Communities seek success when it comes to preventing the sexual abuse of children. Thus, how best to measure treatment gains for incarcerated offenders and how those gains are linked to reductions in recidivism are important topics for research. This study examines the relationship between psychometric changes and recidivism in a sample of 495 sex offenders who completed treatment in the prison-based Kia Marama treatment programme in Rolleston prison, New Zealand. The specific goals of this study were threefold. Firstly; to characterise offender progress overall on the administered psychometric battery in terms of five different methods of calculating change. Two methods of calculating clinically significant change were employed. Firstly, change was calculated using the Jacobson and Truax (1991) method of establishing a cut off score based on normative data for each measure. Secondly change was defined as clinically significant when the post treatment score fell 1SD away from the pre-treatment mean in the direction of functionality, a methodology used by Wakeling, Beech, and Freemantle (2013). Two methods of calculating reliable change were then employed. Firstly the Jacobson, Follette, Revenstorf, et al. (1984) calculation adopted by Wakeling et al. (2013) was applied, followed by the more stringent formula proposed by Christensen and Mendoza (1986) which takes account of the standard error of difference. Finally, residual change scores were calculated, replicating the change methodology adopted by Beggs and Grace (2011). The second goal of this study was to compare the five different methodologies above for assessing change based on participants’ scores on the administered psychometric battery. The third and final goal was to determine which of the five identified methods of measuring change demonstrated the strongest correlation with recidivism. Measures of clinically significant change were found to be significantly correlated with recidivism. However, this was not necessarily true when change was defined as both reliable and clinically significant. Results indicated that the Wakeling et al. (2013) method of calculating clinically significant change outperformed all of the others in regards to predicting recidivism. Overall, the present results support the use of self-report psychometrics in measuring treatment change and predicting recidivism