Domain-Specific Languages (DSLs) are an efficient means to counter accidental complexity and are therefore a key technology for Model-Driven Engineering (MDE). Despite DSLs' potential, there is a lack of empirical research regarding the practical effects and developer perception of DSL-driven tools. In this paper, we present a controlled experiment with 28 participants around a previously developed DSL-based toolchain, which assists the migration of legacy software to REST. A direct comparison of developer performance for a) "DSL toolchain" and b) "classic manual software migration" allowed for analysis, quantification of effects and developer perception, as well as reasoning on general advantages, and DSL-related challenges. In certain cases, we measured a significant correlation between toolchain use and performance gains for developers. Detailed analysis of developer activities suggests the DSL toolchain alleviates tasks which show error-prone or time-consuming in the manual alternative. We then extracted acceptance-hindering factors from participant feedback and derived a series of recommendations for MDE practitioners who seek to develop DSL-based tools.

To succeed with the development of modern software, organizations must have the agility to adapt faster to constantly evolving environments to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, business, development, and IT. However, stakeholders do not have sufficient automated support for global decision making, considering the increasing variability of the solution space, the frequent lack of explicit representation of its associated variability and decision points, and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. The Multi-Plane Models and Data (MP-MODA) framework explicitly represents and manages variability, impacts, and decision points. It enables automation and tool support in aid of a multi-criteria decision making process involving different stakeholders within a feedback-driven software development process where feedback cycles aim to reduce uncertainty. We present the conceptual structure of the framework, discuss its potential benefits, and enumerate key challenges related to tool supported automation and analysis within MP-MODA.

Model-Driven Engineering (MDE) reduces complexity, improves Separation of Concerns and promotes reuse by structuring software development as a process of model production and refinement. Domain-Specific Modelling Languages and Aspect-Oriented Modelling techniques can reduce complexity and improve modularization of crosscutting concerns in situations where the features of general purpose modelling languages are not well aligned with the subject of study. In this article we present FIDDLR, a novel framework that integrates the ideas of Domain-Specific Modelling Languages, Concern-Oriented Reuse and MDE to modularize concerns that cross-cut multiple levels of abstraction of the software development process and streamline the reuse process. It also prescribes the integration of the different tooling along this process. We demonstrate the effectiveness of our framework and the potential for reduced complexity and leveraged reuse by building a reusable concern that exposes the services a system offers through a REST interface.