CodeFirst ModelDriven Engineering: On the Agile Adoption of MDE Tooling Domain models are the most important asset in widely accepted software development approaches, like DomainDriven Design (DDD), yet those models are still implicitly represented in programs. ModelDriven Engineering (MDE) regards those models as representable entities that are amenable to automated analysis and processing, facilitating quality assurance while increasing productivity in software development processes. Although this connection is not new, very few approaches facilitate adoption of MDE tooling without compromising existing value, their data. Moreover, switching to MDE tooling usually involves reengineering core parts of an application, hindering backward compatibility and, thereby, continuous integration, while requiring an upfront investment in training in specialized modeling frameworks. In those approaches that overcome the previous problem, there is no clear indication  from a quantitative point of view  of the extent to which adopting stateoftheart MDE practices and tooling is feasible or advantageous. In this work, we advocate a codefirst approach to modeling through an approach for applying MDE techniques and tools to existing objectoriented software applications that fully preserves the semantics of the original application, which need not be modified. Our approach consists both of a semiautomated method for specifying explicit view models out of existing objectoriented applications and of a conservative extension mechanism that enables the use of such view models at run time, where view model queries are resolved on demand and view model updates are propagated incrementally to the original application. This mechanism enables an iterative, flexible application of MDE tooling to software applications, where metamodels and models do not exist explicitly. An evaluation of this extension mechanism, implemented for Java applications and for view models atop the Eclipse Modeling Framework (EMF), has been conducted with an industrytargeted benchmark for decision support systems, analyzing performance and scalability of the synchronization mechanism. Backward propagation of large updates over very large views is instant.

Encoding Adaptability of Software Engineering Tools as Algorithm Configuration Problem: A Case Study Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, handcrafted configurations are often far from optimal. In this paper, we study the software configuration problem in the context of the model comparison tool SiDiff, which needs to be carefully adapted to domainspecific modeling languages used in modeldriven engineering. To tackle the configuration challenge, we propose to draw from the field of automated algorithm configuration, a research area which has studied the optimization of parameterizable algorithms for many years and which has gained particular momentum through its applications to hyperparameter tuning in machine learning. Specifically, we report on ongoing work encoding the adaptability of SiDiff as an algorithm configuration problem which is amenable to a sequential modelbased optimization tool known as SMAC. While empirical evaluation results are left for future work, the main goal of this paper is to foster active discussions at the workshop and to collect early feedback on our ongoing research.