Representing Legacy System Interoperability by Extending KDM

International audienceThe complexity of software systems is continuously growing. An important part of this complexity issue concerns the interoperability between existing systems (i.e. legacy systems), where problems often occur due to heterogeneity in e.g. data, involved technologies or models. The Knowledge Discovery Metamodel (KDM) standardised by the Object Management Group (OMG) facilitates representation of existing systems, allowing them to be treated in a homogenous way at the model abstraction level. This paper defines a language suitable for modelling interoperability between these systems by extending KDM and introducing concepts that are specifically aimed at representing relevant interoperability information

An Overview of Quality Frameworks in Model-Driven Engineering and Observations on Transformation Quality

Quality is often defined as fitness for purpose which is the key property to determine when evaluating quality. This paper presents some general requirements for evaluating quality frameworks. It also discusses characteristics of MDE that are important when building a quality framework, such as its use of models in several stages of development and maintenance, and its multi-abstraction level approach that requires consistency and traceability. MDE should strive for engineering of quality into software. As a step in extending quality frameworks for this purpose, we discuss integrating quality evaluation with quality engineering using MDE approaches. Applying it on transformations, we discuss both the transformability of models and the quality of transformations themselves. While completeness and precision of models are quality criteria for transformability of them, transformations should monitor and preserve model quality, be reusable, simple and efficient. We further discuss quality means and evaluation methods and propose future work.An Overview of Quality Frameworks in Model-Driven Engineering and Observations on Transformation Qualit

A Metamodel for Specifying Quality Models in Model-Driven Engineering

In the context of model-driven engineering, models act as the primary artifacts and are eventually transformed into other artifacts, such as code and documentation. Consequently, to evaluate and assure the quality of software systems, developers need tools and techniques that allow them to reflect upon the quality of the models themselves. This research paper discusses existing quality models before proposing a metamodel for specifying quality models in the context of model-driven engineering. A tool is being developed in Eclipse based on the proposed metamodel. For each project, developers can use the tool, consisting of predefined concepts relevant to quality, to select quality goals based on the context of their particular project. We will use the quality models to integrate previous work on the quality issues in model-driven engineering by relating the identified quality goals to quality-carrying properties, practices or means to achieve them, metrics for evaluation and collected empirical evidence

Where is the Proof? - A Review of Experiences from Applying MDE in Industry

Model-Driven Engineering (MDE) has been promoted as a solution to handle the complexity of software development by raising the abstraction level and automating labor-intensive and error-prone tasks. However, few efforts have been made at collecting evidence to evaluate its benefits and limitations, which is the subject of this review. We searched several publication channels in the period 2000 to June 2007 for empirical studies on applying MDE in industry, which produced 25 papers for the review. Our findings include industry motivations for investigating MDE and the different domains it has been applied to. In most cases the maturity of third-party tool environments is still perceived as unsatisfactory for large-scale industrial adoption. We found reports of improvements in software quality and of both productivity gains and losses, but these reports were mainly from small-scale studies. There are a few reports on advantages of applying MDE in larger projects, however, more empirical studies and detailed data are needed to strengthen the evidence. We conclude that there is too little evidence to allow generalization of the results at this stage. Where is the Proof? - A Review of Experiences from Applying MDE in Industr

Developing a Quality Framework for Model-Driven Engineering

This paper presents some related work on quality frameworks and requirements for evaluating them. It also discusses characteristics of model-driven engineering that are important when building a quality framework, such as its use of models in several stages of development and maintenance, generation of other artifacts from models and its multi-abstraction level approach that requires consistency and traceability. We present a 7-step process on how to define a quality framework that is adapted to model-driven engineering, and which integrates quality engineering with quality evaluation. As an example, the framework is applied on transformation quality. We maintain that the transformation process and transformation mapping should be discussed separately, as they require different approaches, and suggest quality goals, quality-carrying properties to achieve the quality goals and methods for evaluating these properties.Developing a Quality Framework for Model-Driven Engineerin

Existing Model Metrics and Relations to Model Quality

This paper presents quality goals for models and provides a state-of-the-art analysis regarding model metrics. While model-based software development often requires assessing the quality of models at different abstraction and precision levels and developed for multiple purposes, existing work on model metrics do not reflect this need. Model size metrics are descriptive and may be used for comparing models but their relation to model quality is not welldefined. Code metrics are proposed to be applied on models for evaluating design quality while metrics related to other quality goals are few. Models often consist of a significant amount of elements, which allows a large amount of metrics to be defined on them. However, identifying useful model metrics, linking them to model quality goals, providing some baseline for interpretation of data, and combining metrics with other evaluation models such as inspections requires more theoretical and empirical work.Existing Model Metrics and Relations to Model Qualit

Definitions and Approaches to Model Quality in Model-Based Software Development – A Review of Literature

More attention is paid to the quality of models along with the growing importance of modelling in software development. We performed a systematic review of studies discussing model quality published since 2000 to identify what model quality means and how it can be improved. From forty studies covered in the review, six model quality goals were identified; i.e., correctness, completeness, consistency, comprehensibility, confinement and changeability. We further present six practices proposed for developing high-quality models together with examples of empirical evidence. The contributions of the article are identifying and classifying definitions of model quality and identifying gaps for future research.Definitions and Approaches to Model Quality in Model-Based Software Development – A Review of Literatur

Existing model metrics and relations to model quality

This paper presents quality goals for models and provides a state-of-the-art analysis regarding model metrics. While model-based software development often requires assessing the quality of models at different abstraction and precision levels and developed for multiple purposes, existing work on model metrics do not reflect this need. Model size metrics are descriptive and may be used for comparing models but their relation to model quality is not welldefined. Code metrics are proposed to be applied on models for evaluating design quality while metrics related to other quality goals are few. Models often consist of a significant amount of elements, which allows a large amount of metrics to be defined on them. However, identifying useful model metrics, linking them to model quality goals, providing some baseline for interpretation of data, and combining metrics with other evaluation models such as inspections requires more theoretical and empirical work.Existing Model Metrics and Relations to Model Qualit

A Metamodel for Specifying Quality Models in Model-Driven Engineering

In the context of model-driven engineering, models act as the primary artifacts and are eventually transformed into other artifacts, such as code and documentation. Consequently, to evaluate and assure the quality of software systems, developers need tools and techniques that allow them to reflect upon the quality of the models themselves. This research paper discusses existing quality models before proposing a metamodel for specifying quality models in the context of model-driven engineering. A tool is being developed in Eclipse based on the proposed metamodel. For each project, developers can use the tool, consisting of predefined concepts relevant to quality, to select quality goals based on the context of their particular project. We will use the quality models to integrate previous work on the quality issues in model-driven engineering by relating the identified quality goals to quality-carrying properties, practices or means to achieve them, metrics for evaluation and collected empirical evidence