Data is all-pervasive and is found in all aspects of modern computer systems, and yet many engineers seem reluctant to recognise the importance of data integrity. The conventional view of data, as simply an aspect of software, underestimates the role played by data errors in the behaviour of the system and their potential effect on the integrity of the overall system. In many cases hazard analysis is not applied to data in the same way that it is applied to other system components. Without data integrity requirements, data development and data provision may not attract the degree of rigour that would be required of other system components of a similar integrity. This omission also has implications for safety assessment where the data is often ignored or neglected. This position becomes self reenforcing, as without integrity requirements the importance of data integrity remains hidden.\ud This research provides a wide-ranging overview of the use (and abuse) of data within safety systems, and proposes a range of strategies and techniques to improve the safety of such systems. A literature review and a survey of industrial practice confirmed the conventional view of data, and showed that there is little consistency in the methods used for data development. To tackle these problems this work proposes a novel paradigm, in which data is considered as a separate and distinct system component. This approach not only ensures that data is given the importance that it deserves, but also simplifies the task of providing guidance that is specific to data. Having developed this conceptual framework for data, the work then goes on to develop lifecycle models to assist with data development, and to propose a range of techniques appropriate for the various lifecycle phases. An important aspect of the development of any safety-related system is the production of a safety argument, and this research looks in some detail at the treatment of data, and data development, within this justification. The industrial survey reveals that in data-intensive systems data is often developed quite separately from other elements of the system. It also reveals that data is often produced by an extended data supply chain that may involve a number of disparate organisations. These characteristics of data distinguish it from other system components and greatly complicate the achievement and demonstration of safety. This research proposes methods of modelling complex data supply chains and proposes techniques for tackling the difficult task of safety justification for such systems
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