Rodin: an open toolset for modelling and reasoning in Event-B

Event-B is a formal method for system-level modelling and analysis. Key features of Event-B are the use of set theory as a modelling notation, the use of refinement to represent systems at different abstraction levels and the use of mathematical proof to verify consistency between refinement levels. In this article we present the Rodin modelling tool that seamlessly integrates modelling and proving. We outline how the Event-B language was designed to facilitate proof and how the tool has been designed to support changes to models while minimising the impact of changes on existing proofs. We outline the important features of the prover architecture and explain how well-definedness is treated. The tool is extensible and configurable so that it can be adapted more easily to different application domains and development methods

An open extensible tool environment for Event-B

Abstract. We consider modelling indispensable for the development of complex systems. Modelling must be carried out in a formal notation to reason and make meaningful conjectures about a model. But formal modelling of complex systems is a difficult task. Even when theorem provers improve further and get more powerful, modelling will remain difficult. The reason for this that modelling is an exploratory activity that requires ingenuity in order to arrive at a meaningful model. We are aware that automated theorem provers can discharge most of the onerous trivial proof obligations that appear when modelling systems. In this article we present a modelling tool that seamlessly integrates modelling and proving similar to what is offered today in modern integrated development environments for programming. The tool is extensible and configurable so that it can be adapted more easily to different application domains and development methods.

A review of information flow diagrammatic models for product-service systems

A product-service system (PSS) is a combination of products and services to create value for both customers and manufacturers. Modelling a PSS based on function orientation offers a useful way to distinguish system inputs and outputs with regards to how data are consumed and information is used, i.e. information flow. This article presents a review of diagrammatic information flow tools, which are designed to describe a system through its functions. The origin, concept and applications of these tools are investigated, followed by an analysis of information flow modelling with regards to key PSS properties. A case study of selection laser melting technology implemented as PSS will then be used to show the application of information flow modelling for PSS design. A discussion based on the usefulness of the tools in modelling the key elements of PSS and possible future research directions are also presented

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Heterogeneity in pure microbial systems: experimental measurements and modeling

Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale

Safety Proofs for Automated Driving using Formal Methods

The introduction of driving automation in road vehicles can potentially reduce road traffic crashes and significantly improve road safety. Automation in road vehicles also brings other benefits such as the possibility to provide independent mobility for people who cannot and/or should not drive. Correctness of such automated driving systems (ADSs) is crucial as incorrect behaviour may have catastrophic consequences.Automated vehicles operate in complex and dynamic environments, which requires decision-making and control at different levels. The aim of such decision-making is for the vehicle to be safe at all times. Verifying safety of these systems is crucial for the commercial deployment of full autonomy in vehicles. Testing for safety is expensive, impractical, and can never guarantee the absence of errors. In contrast, formal methods, techniques that use rigorous mathematical models to build hardware and software systems, can provide mathematical proofs of the correctness of the systems.The focus of this thesis is to address some of the challenges in the safety verification of decision and control systems for automated driving. A central question here is how to establish formal methods as an efficient approach to develop a safe ADS. A key finding is the need for an integrated formal approach to prove correctness of ADS. Several formal methods to model, specify, and verify ADS are evaluated. Insights into how the evaluated methods differ in various aspects and the challenges in the respective methods are discussed. To help developers and safety experts design safe ADSs, the thesis presents modelling guidelines and methods to identify and address subtle modelling errors that might inadvertently result in proving a faulty design to be safe. To address challenges in the manual modelling process, a systematic approach to automatically obtain formal models from ADS software is presented and validated by a proof of concept. Finally, a structured approach on how to use the different formal artifacts to provide evidence for the safety argument of an ADS is shown