SEMI-AUTOMATED TEST MODEL GENERATION

Appropriate test models that can satisfy complex constraints are required for testing model management programs in order to build confidence in their correctness. Models have inherently complex structures and are often required to satisfy non-trivial constraints which makes them time consuming, labour intensive and error prone to construct manually. Automated capabilities are therefore required, however, existing fully-automated model generation tools cannot generate models that satisfy arbitrarily complex constraints. This thesis addresses this problem by proposing a semi-automated approach towards the generation of such models. A new framework named Epsilon Model Generator (EMG) that implements this approach is presented. The framework supports the development of model generators that can produce random and reproducible test models that satisfy complex constraints

ATLTest: A White-Box Test Generation Approach for ATL Transformations

International audienceMDE is being applied to the development of increasingly complex systems that require larger model transformations. Given that the specification of such transformations is an error-prone task, techniques to guarantee their quality must be provided. Testing is a well-known technique for finding errors in programs. In this sense, adoption of testing techniques in the model transformation domain would be helpful to improve their quality. So far, testing of model transformations has focused on black-box testing techniques. Instead, in this paper we provide a white-box test model generation approach for ATL model transformations

Employing Classifying Terms for Testing Model Transformations

This contribution proposes a new technique for developing test cases for UML and OCL models. The technique is based on an approach that automatically constructs object models for class models enriched by OCL constraints. By guiding the construction process through so-called classifying terms, the built test cases in form of object models are classified into equivalence classes. A classifying term can be an arbitrary OCL term on the class model that calculates for an object model a characteristic value. From each equivalence class of object models with identical characteristic values one representative is chosen. The constructed test cases behave significantly different with regard to the selected classifying term. By building few diverse object models, properties of the UML and OCL model can be explored effectively. The technique is applied for automatically constructing relevant source model test cases for model transformations between a source and target metamodel.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Meta-model Pruning

Large and complex meta-models such as those of Uml and its profiles are growing due to modelling and inter-operability needs of numerous\ud stakeholders. The complexity of such meta-models has led to coining\ud of the term meta-muddle. Individual users often exercise only a small\ud view of a meta-muddle for tasks ranging from model creation to construction\ud of model transformations. What is the effective meta-model that represents\ud this view? We present a flexible meta-model pruning algorithm and\ud tool to extract effective meta-models from a meta-muddle. We use\ud the notion of model typing for meta-models to verify that the algorithm\ud generates a super-type of the large meta-model representing the meta-muddle.\ud This implies that all programs written using the effective meta-model\ud will work for the meta-muddle hence preserving backward compatibility.\ud All instances of the effective meta-model are also instances of the\ud meta-muddle. We illustrate how pruning the original Uml metamodel\ud produces different effective meta-models

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

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

Maintenance Testing of Mixed-Signal Boards

In the context of maintenance and diagnosis of faulty boards, we introduce a functional FSM-based model for mixed-signal circuits. We target effi cient test sequences generation for ATE based on a high-level, functional modeling of components assemblies. The approach is flexible, allows to handle digital as well as analog and mixed-signal components in a similar way. A primary prototype has been developped, and two industrial cases partially processed

On the Realization of TractsTool

Model transformations play an important role in Model-Driven Engineering (MDE), and as their size and complexity grow, there is an increasing need to count on tool support for testing their correctness. In this presentation, we introduce TractsTool, a tool for specifying and testing several different kinds of model transformations, e.g., model-to-model, model-to-text, and text-to-model transformations, based on contracts. We explain the main principles behind the tool, demonstrate some of its capabilities by a running example, and show how it is internally realized by using MDE techniques. In particular, we describe the transformation chain that is used to compute the test results. TractsTool with accompanying information is available at: http://atenea.lcc.uma.es/index.php/Main_Page/Resources/Tract