Gehan M. K. Selim

Model transformation intents and their properties

The notion of model transformation intent is proposed to capture the purpose of a transformation. In this paper, a framework for the description of model transformation intents is defined, which includes, for instance, a description of properties a model transformation has to satisfy to qualify as a suitable realization of an intent. Several common model transformation intents are identified, and the framework is used to describe six of them in detail. A case study from the automotive industry is used to demonstrate the usefulness of the proposed framework for identifying crucial properties of model transformations with different intents and to illustrate the wide variety of model transformation intents that an industrial model-driven software development process typically encompasses.

Towards a model transformation intent catalog

We report on our ongoing effort to build a catalog of model transformation intents that describes common uses of model transformations in Model-Driven Engineering (MDE) and the properties they must or may possess. We present a preliminary list of intents and common properties. One intent (transformation for analysis) is described in more detail and the description is used to identify transformations with the same intent in a case study on the use of MDE techniques for the development of control software for a power window.

Model transformation testing: the state of the art

Model Driven Development (MDD) is a software engineering approach in which models constitute the basic units of software development. A key part of MDD is the notion of automated model transformation, in which models are stepwise refined into more detailed models, and eventually into code. The correctness of transformations is essential to the success of MDD, and while much research has concentrated on formal verification, testing remains the most efficient method of validation. Transformation testing is however different from testing code, and presents new challenges. In this paper, we survey the model transformation testing phases and the approaches proposed in the literature for each phase.

Enhancing Source-Based Clone Detection Using Intermediate Representation

Detecting software clones in large scale projects helps improve the maintainability of large code bases. The source code representation (e.g., Java or C files) of a software system has traditionally been used for clone detection. In this paper, we propose a technique that transforms the source code to an intermediate representation, and then reuses established source-based clone detection techniques to detect clones in the intermediate representation. The clones are mapped back to the source code and are used to augment the results reported by source-based clone detection. We demonstrate the performance of our new technique using systems from the Bellon clone evaluation benchmark. The result shows that our technique can detect Type 3 clones. Our technique has higher recall with minimal drop in precision using Bellon corpus. By examining the complete clone groups, our technique has higher precision than the standalone string based and token based clone detectors.

Model transformations for migrating legacy models: an industrial case study

Many companies in the automotive industry have adopted MDD in their vehicle control software development. As a major automotive company, General Motors has been using a custom-built, domain-specific modeling language, implemented as an internal proprietary metamodel, to meet the modeling needs in its control software development. As AUTOSAR (AUTomotive Open System ARchitecture) is being developed as a standard to ease the process of integrating components provided by different suppliers and manufacturers, there is a growing demand to migrate these GM-specific, legacy models to AUTOSAR models. Given that AUTOSAR defines its own metamodel for various system artifacts in automotive software development, we explore using model transformations to address the challenges in migrating GM legacy models to their AUTOSAR equivalents. As a case study, we have built a model transformation using the MDWorkbench tool and the Atlas Transformation Language (ATL). This paper reports on the case study, makes observations based on our experience to assist in the development of similar types of transformations, and provides recommendations for further research.

Specification and Verification of Graph-Based Model Transformation Properties

We extend a previously proposed symbolic model transformation property prover for the DSLTrans transformation language. The original prover generated the set of path conditions (i.e., symbolic transformation executions), and verified atomic contracts (constraints on input-output model relations) on these path conditions. The prover evaluated atomic contracts to yield either true or false for the transformation when run on any input model. In this paper we extend the prover such that it can verify atomic contracts and more complex properties composed of atomic contracts. Besides demonstrating our prover on a simple transformation, we use it to verify different kinds of properties of an industrial transformation. Experiments on this transformation using our prover show a speed-up in verification run-time by two orders of magnitude over another verification tool that we evaluated in previous research.

Automated Verification of Model Transformations in the Automotive Industry

Many companies have adopted MDD for developing their software systems. Several studies have reported on such industrial experiences by discussing the effects of MDD and the issues that still need to be addressed. However, only a few studies have discussed using automated verification of industrial model transformations. We previously demonstrated how transformations can be used to migrate GM legacy models to AUTOSAR models. In this study, we investigate using automated verification for such industrial transformations. We report on applying an automated verification approach to the GM-to-AUTOSAR transformation that is based on checking the satisfiability of a relational transformation representation, or a transformation model, with respect to well-formedness OCL constraints. An implementation of this approach is available as a prototype for the ATL language. We present the verification results of this transformation and discuss the practicality of using such tools on industrial size problems.

Model transformations for migrating legacy deployment models in the automotive industry

Many companies in the automotive industry have adopted model-driven development in their vehicle software development. As a major automotive company, General Motors (GM) has been using a custom-built, domain-specific modeling language, implemented as an internal proprietary metamodel, to meet the modeling needs in its control software development. Since AUTomotive Open System ARchitecture (AUTOSAR) has been developed as a standard to ease the process of integrating components provided by different suppliers and manufacturers, there has been a growing demand to migrate these GM-specific, legacy models to AUTOSAR models. Given that AUTOSAR defines its own metamodel for various system artifacts in automotive software development, we explore applying model transformations to address the challenges in migrating GM-specific, legacy models to their AUTOSAR equivalents. As a case study, we have built and validated a model transformation using the MDWorkbench tool, the Atlas Transformation Language, and the Metamodel Coverage Checker tool. This paper reports on the case study, makes observations based on our experience to assist in the development of similar types of transformations, and provides recommendations for further research.

How is ATL Really Used? Language Feature Use in the ATL Zoo

Studies of code repositories have long been used to understand the use of programming languages and to provide insight into how they should evolve. Such studies can highlight features that are rarely used and can safely be removed to simplify the language. Conversely, combinations of features that are frequently used together can be identified and possibly replaced with new features to improve the user experience. Unfortunately, this kind of research has not been as popular in Model Driven Development (MDD). More specifically, using repositories of model transformations (in any language) to understand how the features of these languages are used has not been investigated much, despite its potential benefits. In this paper, we study the use of the ATL model transformation language in an ATL transformation repository. We identify three research questions aimed at providing insight into how ATLs features are actually used. Using the TXL source transformation language, we implement a parser-based analyzer to extract information from the ATL Zoo. We use this information to answer these research questions and provide additional observations based on manual inspection of ATL artifacts.