Program Understanding: A Reengineering Case for the Transformation Tool Contest

In Software Reengineering, one of the central artifacts is the source code of the legacy system in question. In fact, in most cases it is the only definitive artifact, because over the time the code has diverged from the original architecture and design documents. The first task of any reengineering project is to gather an understanding of the system's architecture. Therefore, a common approach is to use parsers to translate the source code into a model conforming to the abstract syntax of the programming language the system is implemented in which can then be subject to querying. Despite querying, transformations can be used to generate more abstract views on the system's architecture. This transformation case deals with the creation of a state machine model out of a Java syntax graph. It is derived from a task that originates from a real reengineering project.Comment: In Proceedings TTC 2011, arXiv:1111.440

A Catalog of Reusable Design Decisions for Developing UML/MOF-based Domain-specific Modeling Languages

In model-driven development (MDD), domain-specific modeling languages (DSMLs) act as a communication vehicle for aligning the requirements of domain experts with the needs of software engineers. With the rise of the UML as a de facto standard, UML/MOF-based DSMLs are now widely used for MDD. This paper documents design decisions collected from 90 UML/MOF-based DSML projects. These recurring design decisions were gained, on the one hand, by performing a systematic literature review (SLR) on the development of UML/MOF-based DSMLs. Via the SLR, we retrieved 80 related DSML projects for review. On the other hand, we collected decisions from developing ten DSML projects by ourselves. The design decisions are presented in the form of reusable decision records, with each decision record corresponding to a decision point in DSML development processes. Furthermore, we also report on frequently observed (combinations of) decision options as well as on associations between options which may occur within a single decision point or between two decision points. This collection of decision-record documents targets decision makers in DSML development (e.g., DSML engineers, software architects, domain experts).Series: Technical Reports / Institute for Information Systems and New Medi

A Design Pattern for Executable DSML

Model executability is now a key concern in model-driven engineering, mainly to support early validation and verification (V&V). Some approaches have allowed to weave executability into metamodels, defining executable domain-specific modeling languages (DSML). Then, model validation may be achieved by direct interpretation of the conforming models. Other approaches address model executability by model compilation, allowing to reuse the virtual machines or V&V tools existing in the target domain. Nevertheless, systematic methods are not available to help the language designer in the definition of such an execution semantics and related support tools. For instance, simulators are mostly hand-crafted in a tool specific manner for each DSML. In this paper, we propose to reify the elements commonly used to support execution in a DSML. We infer a design pattern (called Executable DSML pattern) providing a general reusable solution for the expression of the executability concerns in DSML. It favors flexibility and improves reusability in the definition of semantics-based tools for DSML. We illustrate how this pattern can be applied to V&V and models at runtime, and give insights on the development of generic and generative tools for model animators

Model-Driven Engineering for Artificial Intelligence - A Systematic Literature Review

Objective: This study aims to investigate the existing body of knowledge in the field of Model-Driven Engineering MDE in support of AI (MDE4AI) to sharpen future research further and define the current state of the art. Method: We conducted a Systemic Literature Review (SLR), collecting papers from five major databases resulting in 703 candidate studies, eventually retaining 15 primary studies. Each primary study will be evaluated and discussed with respect to the adoption of (1) MDE principles and practices and (2) the phases of AI development support aligned with the stages of the CRISP-DM methodology. Results: The study's findings show that the pillar concepts of MDE (metamodel, concrete syntax and model transformation), are leveraged to define domain-specific languages (DSL) explicitly addressing AI concerns. Different MDE technologies are used, leveraging different language workbenches. The most prominent AI-related concerns are training and modeling of the AI algorithm, while minor emphasis is given to the time-consuming preparation of the data sets. Early project phases that support interdisciplinary communication of requirements, such as the CRISP-DM \textit{Business Understanding} phase, are rarely reflected. Conclusion: The study found that the use of MDE for AI is still in its early stages, and there is no single tool or method that is widely used. Additionally, current approaches tend to focus on specific stages of development rather than providing support for the entire development process. As a result, the study suggests several research directions to further improve the use of MDE for AI and to guide future research in this area