Type Inference in Flexible Model-Driven Engineering

Model-driven Engineering (MDE) is an approach to software development that promises increased productivity and product quality. Domain models that conform to metamodels, both of which are the core artefacts in MDE approaches, are manipulated to perform different development processes using specific MDE tools. However, domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using MDE tools. Flexible or bottom-up Model-driven Engineering is an emerging approach to domain and systems modelling that tackles this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in MDE processes. In this approach, no metamodel is created upfront but instead the process starts with the definition of example models that will be used to infer a draft metamodel. When complete knowledge of the domain is acquired, a final metamodel is devised and a transition to traditional MDE approaches is possible. However, the lack of a metamodel that encodes the semantics of conforming models and of tools that impose these semantics bears some drawbacks, among others that of having models with nodes that are unintentionally left untyped. In this thesis we propose the use of approaches that use algorithms from three different research areas, that of classification algorithms, constraint programming and graph similarity to help with the type inference of such untyped nodes. We perform an evaluation of the proposed approaches in a number of randomly generated example models from 10 different domains with results suggesting that the approaches could be used for type inference both in an automatic or a semi-automatic style

Type inference in flexible model-driven engineering using classification algorithms

Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used

Constraint programming for type inference in flexible model-driven engineering

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance

Language engineering : Challenges, opportunities and potential disasters for interactive systems

Language engineering underpins model-driven engineering and the application of domain-specific languages. In this talk, I will introduce language engineering and its principles and practices, using model-driven engineering as an exemplar. I will suggest how the engineering of interactive systems offers opportunities, challenges and the potential for chaos for language engineering

Discovering meta-models of low-code applications

Low-code platforms (LCAP) enable users to create apps of various types quickly and with little or no coding in general purpose programming languages. Despite their popularity, these platforms are often closed source and do not adhere to standards. Users of these platforms face two major issues: the first is the difficulty in the evolution of applications in terms of platform updates, and the second is the inability to migrate the applications to another platform, constraining the user into using the original platform. Thus, the goal of this work is to investigate the feasibility of discovering these platforms’ meta-models using models exported by them as a starting point. This will enable apps to be migrated, for example, to a new version of the platform or to a different platform by describing transformations using the discovered meta-models.As plataformas low-code (LCAP) são plataformas que permitem a utilizadores construir aplicações de todo o tipo rapidamente, com recurso a pouco ou nenhum código escrito numa linguagem de programação de uso genérico. Apesar do sucesso que estas plataformas têm usufruido, geralmente são plataformas de natureza codigo fechado e não seguem padrões. Dois grandes problemas que os utilizadores destas plataformas geralmente tem são evoluir as applicações à medida que a plataforma é atualizada e a impossíbilidade de migrar as suas applicações para outra plataforma, sem ter que as refazer. Assim, o objetivo deste trabalho é explorar a possibilidade de descoberta dos meta-modelos destas plataformas usando como base aplicações exportadas pelas mesmas. Isto irá permitir, por exemplo, que as applicações possam ser migradas para uma nova versão da plataforma ou para uma plataforma diferente, usando os meta-modelos encontrados para definir uma transformação. Posto isto, a solução desenvolvida é capaz de descobrir a estrutura dos modelos de entrada e ainda é capaz de deduzir quais campos são referencias a conceitos no meta-modelo

Chatbots for Modelling, Modelling of Chatbots

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202

Type inference in flexible model-driven engineering

In Model-Driven Engineering (MDE), models conform to metamodels. In flexible modelling, engineers construct example models with free-form drawing tools; these examples may later need to conform to a metamodel. Flexible modelling can lead to errors: drawn elements that should represent the same domain concept could instantiate different types; other drawn elements could be left untyped. We propose a novel type inference approach to calculating types from example models, based on the Classification and Regression Trees (CART) algorithm. We describe the approach and evaluate it on a number of randomly generated models, considering the accuracy and precision of the resultant classifications. Experimental results suggest that on average 80% of element types are correctly identified. In addition, the results reveal a correlation between the accuracy and the ratio of known-to-unknown types in a model

Constraint programming for type inference in flexible model-driven engineering

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance