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

Conservative and traceable executions of heterogeneous model management workflows

One challenge of developing large scale systems is knowing how artefacts are interrelated across tools and languages, especially when traceability is mandated e.g., by certifying authorities. Another challenge is the interoperability of all required tools to allow the software to be built, tested, and deployed efficiently as it evolves. Build systems have grown in popularity as they facilitate these activities. To cope with the complexities of the development process, engineers can adopt model-driven practices that allow them to raise the system abstraction level by modelling its domain, therefore, reducing the accidental complexity that comes from e.g., writing boilerplate code. However, model-driven practices come with challenges such as integrating heterogeneous model management tasks e.g., validation, and modelling technologies e.g., Simulink (a proprietary modelling environment for dynamic systems). While there are tools that support the execution of model-driven workflows, some support only specific modelling technologies, lack the generation of traceability information, or do not offer the cutting-edge features of build systems like conservative executions i.e., where only tasks affected by changes to resources are executed. In this work we propose ModelFlow, a workflow language and interpreter able to specify and execute model management workflows conservatively and produce traceability information as a side product. In addition, ModelFlow reduces the overhead of model loading and disposal operations by allowing model management tasks to share already loaded models during the workflow execution. Our evaluation shows that ModelFlow can perform conservative executions which can improve the performance times in some scenarios. ModelFlow is designed to support the execution of model management tasks targeting various modelling frameworks and can be used in conjunction with models from heterogeneous technologies. In addition to EMF models, ModelFlow can also handle Simulink models through a driver developed in the context of this thesis which was used to support one case study