Graph Query by Example

Abstract. Model-driven tools use model queries for many purposes, including validation of well-formedness rules, specication of derived features, and directing rule-based model transformation. Query languages such as graph patterns may facilitate capturing complex structural relationships between model elements. Specifying such queries, however, may prove dicult for engineers familiar with the concrete syntax only, not with the underlying abstract representation of the modeling language. The current paper presents an extension to the EMF-IncQuery model query tool that lets users point out, using familiar concrete syntax, an example of what the query results should look like, and automatically derive a graph query that nds other similar results. Keywords: by example, model query, graph pattern, EMF-IncQuery 1 Introduction Model-driven Engineering (MDE) approaches treat models as primary artifacts of the engineering process, relying on automated model processing steps. Models are usually thought of as typed, attributed graphs. This underlying structure is dened by the metamodel of the modeling language and is called the abstract syntax. On the other hand, the preferred way the model is presented to (and edited by) humans is in the form of visual diagrams, textual notations, tree structures, etc., called the concrete syntax. The two representations can have substantial dierences, e.g., an edge in concrete syntax may correspond to a node in abstract syntax, or to a structure of several elements (see Model queries are important components in model-driven tool chains: they are widely used for specifying derived features, well-formedness constraints, reports, and guard conditions for behavioural models, design space rules or model transformations. Although model queries can be implemented using a generalpurpose programming language (Java), specialized query languages may be mor

Weaving Rules into [email protected] for Embedded Smart Systems

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware.Comment: pre-print version, published in the proceedings of MOMO-17 Worksho

Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use

The creation of Domain Specific Languages(DSL) counts as one of the main goals in the field of Model-Driven Software Engineering (MDSE). The main purpose of these DSLs is to facilitate the manipulation of domain specific concepts, by providing developers with specific tools for their domain of expertise. A natural approach to create DSLs is to reuse existing modeling standards and tools. In this area, the Eclipse Modeling Framework (EMF) has rapidly become the defacto standard in the MDSE for building Domain Specific Languages (DSL) and tools based on generative techniques. However, the use of EMF generated tools in domains like Internet of Things (IoT), Cloud Computing or Models@Runtime reaches several limitations. In this paper, we identify several properties the generated tools must comply with to be usable in other domains than desktop-based software systems. We then challenge EMF on these properties and describe our approach to overcome the limitations. Our approach, implemented in the Kevoree Modeling Framework (KMF), is finally evaluated according to the identified properties and compared to EMF.Comment: ISBN 978-2-87971-131-7; N° TR-SnT-2014-11 (2014

Saying Hello World with GReTL - A Solution to the TTC 2011 Instructive Case

This paper discusses the GReTL solution of the TTC 2011 Hello World case. The submitted solution covers all tasks including the optional ones.Comment: In Proceedings TTC 2011, arXiv:1111.440

A Model-Derivation Framework for Software Analysis

Model-based verification allows to express behavioral correctness conditions like the validity of execution states, boundaries of variables or timing at a high level of abstraction and affirm that they are satisfied by a software system. However, this requires expressive models which are difficult and cumbersome to create and maintain by hand. This paper presents a framework that automatically derives behavioral models from real-sized Java programs. Our framework builds on the EMF/ECore technology and provides a tool that creates an initial model from Java bytecode, as well as a series of transformations that simplify the model and eventually output a timed-automata model that can be processed by a model checker such as UPPAAL. The framework has the following properties: (1) consistency of models with software, (2) extensibility of the model derivation process, (3) scalability and (4) expressiveness of models. We report several case studies to validate how our framework satisfies these properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581

A Model-Derivation Framework for Software Analysis

Model-based verification allows to express behavioral correctness conditions like the validity of execution states, boundaries of variables or timing at a high level of abstraction and affirm that they are satisfied by a software system. However, this requires expressive models which are difficult and cumbersome to create and maintain by hand. This paper presents a framework that automatically derives behavioral models from real-sized Java programs. Our framework builds on the EMF/ECore technology and provides a tool that creates an initial model from Java bytecode, as well as a series of transformations that simplify the model and eventually output a timed-automata model that can be processed by a model checker such as UPPAAL. The framework has the following properties: (1) consistency of models with software, (2) extensibility of the model derivation process, (3) scalability and (4) expressiveness of models. We report several case studies to validate how our framework satisfies these properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581

Evaluation of Kermeta for Solving Graph-based Problems

Kermeta is a meta-language for specifying the structure and behavior of graphs of interconnected objects called models. In this paper,\ud we show that Kermeta is relatively suitable for solving three graph-based\ud problems. First, Kermeta allows the specification of generic model\ud transformations such as refactorings that we apply to different metamodels\ud including Ecore, Java, and Uml. Second, we demonstrate the extensibility\ud of Kermeta to the formal language Alloy using an inter-language model\ud transformation. Kermeta uses Alloy to generate recommendations for\ud completing partially specified models. Third, we show that the Kermeta\ud compiler achieves better execution time and memory performance compared\ud to similar graph-based approaches using a common case study. The\ud three solutions proposed for those graph-based problems and their\ud evaluation with Kermeta according to the criteria of genericity,\ud extensibility, and performance are the main contribution of the paper.\ud Another contribution is the comparison of these solutions with those\ud proposed by other graph-based tools

Solving the TTC 2011 Reengineering Case with VIATRA2

The current paper presents a solution of the Program Understanding: A Reengineering Case for the Transformation Tool Contest using the VIATRA2 model transformation tool.Comment: In Proceedings TTC 2011, arXiv:1111.440