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

Evaluation of Kermeta on Graph Transformation Problems

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

Evaluation of Model Transformation Approaches for Model Refactoring

This paper provides a systematic evaluation framework for comparing model transformation approaches, based upon the ISO/IEC 9126-1 quality characteristics for software systems. We apply this framework to compare five transformation approaches (QVT-R, ATL, Kermeta, UMLRSDS and GrGen.NET) on a complex model refactoring case study: the amalgamation of apparent attribute clones in a class diagram. The case study highlights the problems with the specification and design of the refactoring category of model transformations, and provides a challenging example by which model transformation languages and approaches can be compared. We take into account a wide range of evaluation criteria aspects such as correctness, efficiency, flexibility, interoperability, reusability and robustness, which have not been comprehensively covered by other comparative surveys of transformation approaches. The results show clear distinctions between the capabilities and suitabilities of different approaches to address the refactoring form of transformation problem

Static Analysis of Model Transformations for Effective Test Generation

International audienceModel transformations are an integral part of several computing systems that manipulate interconnected graphs of objects called models in an input domain specified by a metamodel and a set of invariants. Test models are used to look for faults in a transformation. A test model contains a specific set of objects, their interconnections and values for their attributes. Can we automatically generate an effective set of test models using knowledge from the transformation? We present a white-box testing approach that uses static analysis to guide the automatic generation of test inputs for transformations. Our static analysis uncovers knowledge about how the input model elements are accessed by transformation operations. This information is called the input metamodel footprint due to the transformation. We transform footprint, input metamodel, its invariants, and transformation pre-conditions to a constraint satisfaction problem in Alloy. We solve the problem to generate sets of test models containing traces of the footprint. Are these test models effective? With the help of a case study transformation we evaluate the effectiveness of these test inputs. We use mutation analysis to show that the test models generated from footprints are more effective (97.62% avg. mutation score) in detecting faults than previously developed approaches based on input domain coverage criteria (89.9% avg.) and unguided generation (70.1% avg.)

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

Using slicing techniques to support scalable rigorous analysis of class models

2015 Spring.Includes bibliographical references.Slicing is a reduction technique that has been applied to class models to support model comprehension, analysis, and other modeling activities. In particular, slicing techniques can be used to produce class model fragments that include only those elements needed to analyze semantic properties of interest. However, many of the existing class model slicing techniques do not take constraints (invariants and operation contracts) expressed in auxiliary constraint languages into consideration when producing model slices. Their applicability is thus limited to situations in which the determination of slices does not require information found in constraints. In this dissertation we describe our work on class model slicing techniques that take into consideration constraints expressed in the Object Constraint Language (OCL). The slicing techniques described in the dissertation can be used to produce model fragments that each consists of only the model elements needed to analyze specified properties. The slicing techniques are intended to enhance the scalability of class model analysis that involves (1) checking conformance between an object configuration and a class model with specified invariants and (2) analyzing sequences of operation invocations to uncover invariant violations. The slicing techniques are used to produce model fragments that can be analyzed separately. An evaluation we performed provides evidence that the proposed slicing techniques can significantly reduce the time to perform the analysis

Recommender systems in model-driven engineering: A systematic mapping review

Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of researchThis work has been funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 813884 (Lowcomote [134]), by the Spanish Ministry of Science (projects MASSIVE, RTI2018-095255-B-I00, and FIT, PID2019-108965GB-I00) and by the R&D programme of Madrid (Project FORTE, P2018/TCS-431