Assistance in Model Driven Development: Toward an Automated Transformation Design Process

Model driven engineering aims to shorten the development cycle by focusing on abstractions and partially automating code generation. We long lived in the myth of automatic Model Driven Development (MDD) with promising approaches, techniques, and tools. Describing models should be a main concern in software development as well as model verification and model transformation to get running applications from high level models. We revisit the subject of MDD through the prism of experimentation and open mindness. In this article, we explore assistance for the stepwise transition from the model to the code to reduce the time between the analysis model and implementation. The current state of practice requires methods and tools. We provide a general process and detailed transformation specifications where reverse-engineering may play its part. We advocate a model transformation approach in which transformations remain simple, the complexity lies in the process of transformation that is adaptable and configurable. We demonstrate the usefulness, and scalability of our proposed MDD process by conducting experiments. We conduct experiments within a simple case study in software automation systems. It is both representative and scalable. The models are written in UML; the transformations are implemented mainly using ATL, and the programs are deployed on Android and Lego EV3. Last we report the lessons learned from experimentation for future community work

A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

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

Few-shot prompt learning for automating model completion

Les modélisateurs rencontrent souvent des défis ou des difficultés lorsqu’il s’agit de concevoir un modèle logiciel particulier. Dans cette thèse, nous avons exploré différentes voies et examiné différentes approches pour résoudre cette problématique. Nous proposons enfin une approche simple mais novatrice qui améliore la complétion des activités de modélisation de domaines. Cette approche exploite la puissance des modèles de langage de grande taille en utilisant l’apprentissage par seulement quelques exemples, éliminant ainsi la nécessité d’un apprentissage profond ou d’un ajustement fin (fine tuning) sur des ensembles de données rares dans ce domaine. L’un des points forts de notre approche est sa polyvalence, car elle peut s’intégrer fa cilement à de nombreuses activités de modélisation, fournissant un aide précieux et des recommendations aux modélisateurs. De plus, nous avons mené une étude utilisateur pour évaluer l’utilité de cette méthode et la valeur de l’assistance en modélisation; nous avons cherché à savoir si l’effort investi dans l’assistance en modélisation vaut la peine en recueillant les commentaires des concepteurs de modèles logiciels.Modelers often encounter challenges or difficulties when it comes to designing a particular software model. Throughout this thesis, we have explored various paths and examined different approaches to address this issue. We finally propose a simple yet novel approach enhancing completion in domain modeling activities. This approach leverages the power of large language models by utilizing few-shot prompt learning, eliminating the need for extensive training or fine-tuning on scarce datasets in this field. One of the notable strengths of our approach lies in its versatility, as it can be seamlessly integrated into various modeling activities, providing valuable support and recommendations to software modelers. Additionally, we conducted a user study to evaluate the usefulness of this approach and determine the value of providing assistance in modeling; we aimed to determine if the effort invested in modeling assistance is worthwhile by gathering feedback from software modelers

Multi-model Consistency through Transitive Combination of Binary Transformations

Softwaresysteme werden häufig durch eine Vielzahl an Modellen beschrieben, von denen jedes unterschiedliche Systemeigenschaften abbildet. Diese Modelle können geteilte Informationen enthalten, was zu redundanten Beschreibungen und Abhängigkeiten zwischen den Modellen führt. Damit die Systembeschreibung korrekt ist, müssen alle geteilten Informationen zueinander konsistent beschrieben sein. Die Weiterentwicklung eines Modells kann zu Inkonsistenzen mit anderen Modellen des gleichen Systems führen. Deshalb ist es wichtig einen Mechanismus zur Konsistenzwiederherstellung anzuwenden, nachdem Änderungen erfolgt sind. Manuelle Konsistenzwiederherstellung ist fehleranfallig und zeitaufwändig, weshalb eine automatisierte Konsistenzwiederherstellung notwendig ist. Viele existierende Ansätze nutzen binäre Transformationen, um Konsistenz zwischen zwei Modellen wiederherzustellen, jedoch werden Systeme im Allgemeinen durch mehr als zwei Modelle beschrieben. Um Konsistenzerhaltung für mehrere Modelle mit binären Transformationen zu erreichen, müssen diese durch transitive Ausführung kombiniert werden. In dieser Masterarbeit untersuchen wir die transitive Kombination von binären Transformationen und welche Probleme mit ihr einhergehen. Wir entwickeln einen Katalog aus sechs Fehlerpotentialen, die zu Konsistenzfehlern führen können. Das Wissen über diese Fehlerpotentiale kann den Transformationsentwickler über mögliche Probleme beim Kombinieren von Transformationen informieren. Eines der Fehlerpotentiale entsteht als Folge der Topologie des Transformationsnetzwerks und der benutzten Modelltypen, und kann nur durch Topologieänderungen vermieden werden. Ein weiteres Fehlerpotential entsteht, wenn die kombinierten Transformationen versuchen zueinander widersprüchliche Konsistenzregeln zu erfüllen. Dies kann nur durch Anpassung der Konsistenzregeln behoben werden. Beide Fehlerpotentiale sind fallabhängig und können nicht behoben werden, ohne zu wissen, welche Transformationen kombiniert werden. Zusätzlich wurden zwei Implementierungsmuster entworfen, um zwei weitere Fehlerpotentiale zu verhindern. Sie können auf die einzelnen Transformationsdefinitionen angewendet werden, unabhängig davon welche Transformationen letztendlich kombiniert werden. Für die zwei übrigen Fehlerpotentiale wurden noch keine generellen Lösungen gefunden. Wir evaluieren die Ergebnisse mit einer Fallstudie, bestehend aus zwei voneinander unabhängig entwickelten binären Transformationen zwischen einem komponentenbasierten Softwarearchitekturmodell, einem UML Klassendiagramm und der dazugehörigen Java-Implementierung. Alle gefundenen Fehler konnten einem der Fehlerpotentiale zugewiesen werden, was auf die Vollständigkeit des Fehlerkatalogs hindeutet. Die entwickelten Implementierungsmuster konnten alle Fehler beheben, die dem Fehlerpotential zugeordnet wurden, für das sie entworfen wurden, was 70% aller gefundenen Fehler ausgemacht hat. Dies zeigt, dass die Implementierungsmuster tatsächlich anwendbar sind und Fehler verhindern können

Derivation and consistency checking of models in early software product line engineering

Dissertação para obtenção do Grau de Doutor em Engenharia InformáticaSoftware Product Line Engineering (SPLE) should offer the ability to express the derivation of product-specific assets, while checking for their consistency. The derivation of product-specific assets is possible using general-purpose programming languages in combination with techniques such as conditional compilation and code generation. On the other hand, consistency checking can be achieved through consistency rules in the form of architectural and design guidelines, programming conventions and well-formedness rules. Current approaches present four shortcomings: (1) focus on code derivation only, (2) ignore consistency problems between the variability model and other complementary specification models used in early SPLE, (3) force developers to learn new, difficult to master, languages to encode the derivation of assets, and (4) offer no tool support. This dissertation presents solutions that contribute to tackle these four shortcomings. These solutions are integrated in the approach Derivation and Consistency Checking of models in early SPLE (DCC4SPL) and its corresponding tool support. The two main components of our approach are the Variability Modelling Language for Requirements(VML4RE), a domain-specific language and derivation infrastructure, and the Variability Consistency Checker (VCC), a verification technique and tool. We validate DCC4SPL demonstrating that it is appropriate to find inconsistencies in early SPL model-based specifications and to specify the derivation of product-specific models.European Project AMPLE, contract IST-33710; Fundação para a Ciência e Tecnologia - SFRH/BD/46194/2008

Component-Based Model-Driven Software Development

Model-driven software development (MDSD) and component-based software development are both paradigms for reducing complexity and for increasing abstraction and reuse in software development. In this thesis, we aim at combining the advantages of each by introducing methods from component-based development into MDSD. In MDSD, all artefacts that describe a software system are regarded as models of the system and are treated as the central development artefacts. To obtain a system implementation from such models, they are transformed and integrated until implementation code can be generated from them. Models in MDSD can have very different forms: they can be documents, diagrams, or textual specifications defined in different modelling languages. Integrating these models of different formats and abstraction in a consistent way is a central challenge in MDSD. We propose to tackle this challenge by explicitly separating the tasks of defining model components and composing model components, which is also known as distinguishing programming-in-the-small and programming-in-the-large. That is, we promote a separation of models into models for modelling-in-the-small (models that are components) and models for modelling-in-the-large (models that describe compositions of model components). To perform such component-based modelling, we introduce two architectural styles for developing systems with component-based MDSD (CB-MDSD). For CB-MDSD, we require a universal composition technique that can handle models defined in arbitrary modelling languages. A technique that can handle arbitrary textual languages is universal invasive software composition for code fragment composition. We extend this technique to universal invasive software composition for graph fragments (U-ISC/Graph) which can handle arbitrary models, including graphical and textual ones, as components. Such components are called graph fragments, because we treat each model as a typed graph and support reuse of partial models. To put the composition technique into practice, we developed the tool Reuseware that implements U-ISC/Graph. The tool is based on the Eclipse Modelling Framework and can therefore be integrated into existing MDSD development environments based on the framework. To evaluate the applicability of CB-MDSD, we realised for each of our two architectural styles a model-driven architecture with Reuseware. The first style, which we name ModelSoC, is based on the component-based development paradigm of multi-dimensional separation of concerns. The architecture we realised with that style shows how a system that involves multiple modelling languages can be developed with CB-MDSD. The second style, which we name ModelHiC, is based on hierarchical composition. With this style, we developed abstraction and reuse support for a large modelling language for telecommunication networks that implements the Common Information Model industry standard