Extending relational model transformations to better support the verification of increasingly autonomous systems

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

A satisficing bi-directional model transformation engine using mixed integer linear programming

The use of model transformation in software engineering has increased significantly during the past decade, with the ability to rapidly transform models and ensure consistency between those models being a key property of Model Driven Architecture. However, these approaches can be applied to a wide variety of different model types and some of these models and associated transformations require different semantics than those popularised by current model transformation tools. Specifically, current relational model transformation languages typically prioritise matching relation patterns in the source model over creating a target model that is compliant with its meta-model. In this paper we describe a relational model transformation engine implemented as a series of Mixed Integer Linear Programs (MILP). This engine has a key novel feature; it prioritises target model compliance with its meta-model by considering multiple interpretations of applying the transformation specification in order to ensure a correct target model is generated. In this paper the MILP transformation engine and the representations it uses are described, followed by the results of applying it to examples of varying complexity. © JOT 2011