Over the past decade the capabilities of autonomous systems have been steadily increasing. Unmanned systems are moving from systems that are predominantly remotely operated, to systems that include a basic decision making capability. This is a trend that is expected to continue with autonomous systems making decisions in increasingly complex environments, based on more abstract, higher-level missions and goals. These changes have significant implications for how these systems should be designed and engineered. Indeed, as the goals and tasks these systems are to achieve become more abstract, and the environments they operate in become more complex, are current approaches to verification and validation sufficient? Domain Specific Modelling is a key technology for the verification of autonomous systems. Verifying these systems will ultimately involve understanding a significant number of domains. This includes goals/tasks, environments, systems functions and their associated performance. Relational Model Transformations provide a means to utilise, combine and check models for consistency across these domains. In this thesis an approach that utilises relational model transformation technologies for systems verification, Systems MDD, is presented along with the results of a series of trials conducted with an existing relational model transformation language (QVT-Relations). These trials identified a number of problems with existing model transformation languages, including poorly or loosely defined semantics, differing interpretations of specifications across different tools and the lack of a guarantee that a model transformation would generate a model that was compliant with its associated meta-model. To address these problems, two related solvers were developed to assist with realising the Systems MDD approach. The first solver, MMCS, is concerned with partial model completion, where a partial model is defined as a model that does not fully conform with its associated meta-model. It identifies appropriate modifications to be made to a partial model in order to bring it into full compliance. The second solver, TMPT, is a relational model transformation engine that prioritises target models. It considers multiple interpretations of a relational transformation specification, chooses an interpretation that results in a compliant target model (if one exists) and, optionally, maximises some other attribute associated with the model. A series of experiments were conducted that applied this to common transformation problems in the published literature.

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Les modélisateurs rencontrent souvent des défis ou des difficultés lorsqu’il s’agit de concevoir un modèle logiciel particulier. Dans cette thèse, nous avons exploré différentes voies et examiné différentes approches pour résoudre cette problématique. Nous proposons enfin une approche simple mais novatrice qui améliore la complétion des activités de modélisation de domaines. Cette approche exploite la puissance des modèles de langage de grande taille en utilisant l’apprentissage par seulement quelques exemples, éliminant ainsi la nécessité d’un apprentissage profond ou d’un ajustement fin (fine tuning) sur des ensembles de données rares dans ce domaine. L’un des points forts de notre approche est sa polyvalence, car elle peut s’intégrer fa cilement à de nombreuses activités de modélisation, fournissant un aide précieux et des recommendations aux modélisateurs. De plus, nous avons mené une étude utilisateur pour évaluer l’utilité de cette méthode et la valeur de l’assistance en modélisation; nous avons cherché à savoir si l’effort investi dans l’assistance en modélisation vaut la peine en recueillant les commentaires des concepteurs de modèles logiciels. / Modelers often encounter challenges or difficulties when it comes to designing a particular software model. Throughout this thesis, we have explored various paths and examined different approaches to address this issue. We finally propose a simple yet novel approach enhancing completion in domain modeling activities. This approach leverages the power of large language models by utilizing few-shot prompt learning, eliminating the need for extensive training or fine-tuning on scarce datasets in this field. One of the notable strengths of our approach lies in its versatility, as it can be seamlessly integrated into various modeling activities, providing valuable support and recommendations to software modelers. Additionally, we conducted a user study to evaluate the usefulness of this approach and determine the value of providing assistance in modeling; we aimed to determine if the effort invested in modeling assistance is worthwhile by gathering feedback from software modelers.