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

Automatically Discovering Hidden Transformation Chaining Constraints

Model transformations operate on models conforming to precisely defined metamodels. Consequently, it often seems relatively easy to chain them: the output of a transformation may be given as input to a second one if metamodels match. However, this simple rule has some obvious limitations. For instance, a transformation may only use a subset of a metamodel. Therefore, chaining transformations appropriately requires more information. We present here an approach that automatically discovers more detailed information about actual chaining constraints by statically analyzing transformations. The objective is to provide developers who decide to chain transformations with more data on which to base their choices. This approach has been successfully applied to the case of a library of endogenous transformations. They all have the same source and target metamodel but have some hidden chaining constraints. In such a case, the simple metamodel matching rule given above does not provide any useful information

WSCDL to WSBPEL: A Case Study of ATL-based Transformation

The ATLAS Transformation Language (ATL) is a hybrid transformation language that combines declarative and imperative programming elements and provides means to define model transformations. Most transformations using ATL reported in the literature show a simplified use of ATL, and often involve a single transformation. However, in more realistic situations, multiple transformations may be necessary, especially in case the original input/output models are not represented in the metametamodeling representation expected by the transformation engine. In this paper, we discuss a model transformation from service choreography (WSCDL) to service orchestration (WSBPEL), which cannot be performed in a single ATL transformation due to the mismatch between the concrete XML syntax of these languages and the metametamodeling representation expected by the ATL transformation engine. This requires auxiliary transformations in which this mismatch is resolved. In principle, the required auxiliary transformations can be implemented using XSLT or a general-purpose programming language like Java. However, in our case study, we evaluate the use of ATL to perform these transformations. We exploit ATL by leveraging the ATL's XML\ud injection and the XML extraction mechanisms to perform the overall transformation in terms of a transformation chain

Spectrum-Based Fault Localization in Model Transformations

Model transformations play a cornerstone role in Model-Driven Engineering (MDE), as they provide the essential mechanisms for manipulating and transforming models. The correctness of software built using MDE techniques greatly relies on the correctness of model transformations. However, it is challenging and error prone to debug them, and the situation gets more critical as the size and complexity of model transformations grow, where manual debugging is no longer possible. Spectrum-Based Fault Localization (SBFL) uses the results of test cases and their corresponding code coverage information to estimate the likelihood of each program component (e.g., statements) of being faulty. In this article we present an approach to apply SBFL for locating the faulty rules in model transformations. We evaluate the feasibility and accuracy of the approach by comparing the effectiveness of 18 different stateof- the-art SBFL techniques at locating faults in model transformations. Evaluation results revealed that the best techniques, namely Kulcynski2, Mountford, Ochiai, and Zoltar, lead the debugger to inspect a maximum of three rules to locate the bug in around 74% of the cases. Furthermore, we compare our approach with a static approach for fault localization in model transformations, observing a clear superiority of the proposed SBFL-based method.Comisión Interministerial de Ciencia y Tecnología TIN2015-70560-RJunta de Andalucía P12-TIC-186

Towards optimisation of model queries : A parallel execution approach

The growing size of software models poses significant scalability challenges. Amongst these challenges is the execution time of queries and transformations. In many cases, model management programs are (or can be) expressed as chains and combinations of core fundamental operations. Most of these operations are pure functions, making them amenable to parallelisation, lazy evaluation and short-circuiting. In this paper we show how all three of these optimisations can be combined in the context of Epsilon: an OCL-inspired family of model management languages. We compare our solutions with both interpreted and compiled OCL as well as hand-written Java code. Our experiments show a significant improvement in the performance of queries, especially on large models

SIGMA: Scala Internal Domain-Specific Languages for Model Manipulations

International audienceModel manipulation environments automate model operations such as model consistency checking and model transformation. A number of external model manipulation Domain-Specific Languages (DSL) have been proposed, in particular for the Eclipse Modeling Framework (EMF). While their higher levels of abstraction result in gains in expressiveness over general-purpose languages, their limitations in versatility, performance, and tool support together with the need to learn new languages may significantly contribute to accidental complexities. In this paper, we present Sigma, a family of internal DSLs embedded in Scala for EMF model consistency checking, model-to-model and model-to-text transformations. It combines the benefits of external model manipulation DSLs with general-purpose programming taking full advantage of Scala versatility, performance and tool support. The DSLs are compared to the state-of-the-art Epsilon languages in non-trivial model manipulation tasks that resulted in 20% to 70% reduction in code size and significantly better performance

Formally Defining and Iterating Infinite Models

International audienceThe wide adoption of MDE raises new situations where we need to manipulate very large models or even infinite model streams gathered at runtime. These new uses cases for MDE raise challenges that had been unforeseen by the time standard modeling framework were designed. This paper proposes a formal definition of an infinite model, as well as a formal framework to reason on queries over infinite models. This formal query definition aims at supporting the design and verification of operations that manipulate infinite models. First, we precisely identify the MOF parts which must be refined to support infinite structure. Then, we provide a formal coinductive definition dealing with unbounded and potentially infinite graph-based structure

Efficient execution of ATL model transformations using static analysis and parallelism

Although model transformations are considered to be the heart and soul of Model Driven Engineering (MDE), there are still several challenges that need to be addressed to unleash their full potential in industrial settings. Among other shortcomings, their performance and scalability remain unsatisfactory for dealing with large models, making their wide adoption difficult in practice. This paper presents A2L, a compiler for the parallel execution of ATL model transformations, which produces efficient code that can use existing multicore computer architectures, and applies effective optimizations at the transformation level using static analysis. We have evaluated its performance in both sequential and multi-threaded modes obtaining significant speedups with respect to current ATL implementations. In particular, we obtain speedups between 2.32x and 38.28x for the A2L sequential version, and between 2.40x and 245.83x when A2L is executed in parallel, with expected average speedups of 8.59x and 22.42x, respectively.Spanish Research Projects PGC2018-094905-B-I00, TIN2015-73968-JIN (AEI/FEDER/UE), Ramón y Cajal 2017 research grant, TIN2016-75944-R. Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and by the FWF under the Grant Numbers P28519-N31 and P30525-N31

On validation of ATL transformation rules by transformation models

International audienceModel-to-model transformations constitute an important ingredient in model-driven engineering. As real world transformations are complex, systematic approaches are required to ensure their correctness. The ATLAS Transformation Language (ATL) is a mature transformation language which has been successfully applied in several areas. However, the executable nature of ATL is a barrier for the validation of transformations. In contrast, transformation models provide an integrated structural description of the source and target metamodels and the transformation between them. While not being executable, transformation models are well-suited for analysis and verification of transformation properties. In this paper, we discuss (a) how ATL transformations can be translated into equivalent transformation models and (b) illustrate how these surrogates can be employed to validate properties of the original transformation

SIGMA: Scala Internal Domain-Specific Languages for Model Manipulations

International audienceModel manipulation environments automate model operations such as model consistency checking and model transformation. A number of external model manipulation Domain-Specific Languages (DSL) have been proposed, in particular for the Eclipse Modeling Framework (EMF). While their higher levels of abstraction result in gains in expressiveness over general-purpose languages, their limitations in versatility, performance, and tool support together with the need to learn new languages may significantly contribute to accidental complexities. In this paper, we present Sigma, a family of internal DSLs embedded in Scala for EMF model consistency checking, model-to-model and model-to-text transformations. It combines the benefits of external model manipulation DSLs with general-purpose programming taking full advantage of Scala versatility, performance and tool support. The DSLs are compared to the state-of-the-art Epsilon languages in non-trivial model manipulation tasks that resulted in 20% to 70% reduction in code size and significantly better performance