Formalizing Cyber--Physical System Model Transformation via Abstract Interpretation

Model transformation tools assist system designers by reducing the labor--intensive task of creating and updating models of various aspects of systems, ensuring that modeling assumptions remain consistent across every model of a system, and identifying constraints on system design imposed by these modeling assumptions. We have proposed a model transformation approach based on abstract interpretation, a static program analysis technique. Abstract interpretation allows us to define transformations that are provably correct and specific. This work develops the foundations of this approach to model transformation. We define model transformation in terms of abstract interpretation and prove the soundness of our approach. Furthermore, we develop formalisms useful for encoding model properties. This work provides a methodology for relating models of different aspects of a system and for applying modeling techniques from one system domain, such as smart power grids, to other domains, such as water distribution networks.Comment: 8 pages, 4 figures; to appear in HASE 2019 proceeding

Instrumentation, modeling, and sound metamodeling foundations for complex hybrid systems

Many of our critical infrastructures, from power grids to water distribution networks, are complex hybrid systems that use software to control their non-trivial physical dynamics. These systems must be able to capably serve their purpose, while also being reliable, dependable, safe, secure, and efficient. Representation and analysis of these features requires the creation of several distinct models. These models may encode design goals or be derived from collected instrumentation data, reflecting both how a system ought to operate and how it does operate. It is essential to ensure that all of these models consistently and accurately describe the same system. Adding or removing detail in one model may necessitate changes to several others. This work focuses on system instrumentation, modeling, and metamodeling. Our instrumentation and modeling work studies the behavior of control systems when exposed to electromagnetic disturbances. These disturbances, which may lead to data corruption, system crashes, or hardware damage, present a challenge to engineers. We develop instrumentation for monitoring systems for such disturbances, methods for analyzing the data from our instrumentation, and models of system function which can detect electromagnetic disturbances, including many that do not cause user-visible failures. Metamodeling offers a means of relating disparate models of a system, describing changes to models, and propagating those changes to other models. Our metamodeling work focuses on adding and removing detail from models -- model refinement and generalization, respectively — and on connecting models that use different formalisms -- model transformation. In order for these operations to produce meaningful results, we must ensure that they are sound; that is, they must produce models which describe, to the greatest extent possible, the same system as the models from which they are produced. We begin by creating a theory of abstract interpretation for system modeling. This theory defines a relationship between models and systems and enables verification of the soundness of our metamodeling operations. From this foundation, we create model refinement and generalization operations for specific modeling formalisms. Finally, we show how these operations can be used to perform sound model transformations”--Abstract, page iii

Facilitating Model-Based Design and Evaluation for Sustainability

Relating various models of a system is an essential part of model transformation, model composition, and other metamodeling tasks. The objective of this doctoral research is to create a provably correct approach to this problem

Towards Refinement and Generalization of Reliability Models based on Component States

Complex system design often proceeds in an iterative fashion, starting from a high-level model and adding detail as the design matures. This process can be assisted by metamod-eling techniques that automate some model manipulations and check for or eliminate modeling mistakes. Our work focuses on metamodeling reliability models: we describe generalization and refinement operations for these models. Generalization relaxes constraints that may be infeasible or costly to evaluate; refinement adds further detail to produce a model that more closely describes the desired system. We define these operations in terms of operations on system constraints. To illustrate the proposed method, we relate these constraints to a common Markov chain-based reliability modeling formalism