Engineering a ROVER language in GEMOC STUDIO & MONTICORE: A comparison of language reuse support

Domain-specific languages (DSLs) improve engineering productivity through powerful abstractions and automation. To support the development of DSLs, the software language engineering (SLE) community has produced various solutions for the systematic engineering of DSLs that manifest in language workbenches. In this paper, we investigate the applicability of the language workbenches GEMOC STUDIO and MONTICORE to the MDETools’17 ROVER challenge. To this effect, we refine the challenge’s requirements and show how GEMOC STUDIO and MONTICORE can be leveraged to engineer a Rover-specific DSL by reusing existing DSLs and tooling of GEMOC STUDIO and MONTICORE. Through this, we reflect on the SLE state of the art, detail capabilities of the two workbenches focusing particularly on language reuse support, and sketch how modelers can approach ROVER programming with modern modeling tools

Articulating design-time uncertainty with DRUIDE

Les modélisateurs rencontrent souvent des incertitudes sur la manière de concevoir un modèle logiciel particulier. Les recherches existantes ont montré comment les modélisateurs peuvent travailler en présence de ce type d' ''incertitude au moment de la conception''. Cependant, le processus par lequel les développeurs en viennent à exprimer leurs incertitudes reste flou. Dans cette thèse, nous prenons des pas pour combler cette lacune en proposant de créer un langage de modélisation d'incertitude et une approche pour articuler l'incertitude au moment de la conception. Nous illustrons notre proposition sur un exemple et l'évaluons non seulement sur deux scénarios d'ingénierie logicielle, mais aussi sur une étude de cas réel basée sur les incertitudes causées par la pandémie COVID-19. Nous menons également un questionnaire post-étude avec les chercheurs qui ont participé à l'étude de cas. Afin de prouver la faisabilité de notre approche, nous fournissons deux outils et les discutons. Enfin, nous soulignons les avantages et discutons des limites de notre travail actuel.Modellers often encounter uncertainty about how to design a particular software model. Existing research has shown how modellers can work in the presence of this type of ''design-time uncertainty''. However, the process by which developers come to elicit and express their uncertainties remains unclear. In this thesis, we take steps to address this gap by proposing to create an uncertainty modelling language and an approach for articulating design-time uncertainty. We illustrate our proposal on a worked example and evaluate it not only on two software engineering scenarios, but also on a real case study based on uncertainties caused by the COVID-19 pandemic. We also conduct a post-study questionnaire with the researchers who participated in the case study. In order to prove the feasibility of our approach, we provide two tool supports and discuss them. Finally, we highlight the benefits and discuss the limitations of our current work