Relational databases have proved inadequate for supporting new classes of applications, and as a consequence, a number of new approaches have been taken (Blaha 1998), (Harrington 2000). The most salient alternatives are denormalisation and conversion to an object-oriented database (Douglas 1997). Denormalisation can provide better performance but has deficiencies with respect to data modelling. Object-oriented databases can provide increased performance efficiency but without the deficiencies in data modelling (Blaha 2000). Although there have been various benchmark tests reported, none of these tests have compared normalised, object oriented and de-normalised databases. This research shows that a non-normalised database for data containing type code complexity would be normalised in the process of conversion to an objectoriented database. This helps to correct badly organised data and so gives the performance benefits of de-normalisation while improving data modelling. The costs of conversion from relational databases to object oriented databases were also examined. Costs were based on published benchmark tests, a benchmark carried out during this study and case studies. The benchmark tests were based on an engineering database benchmark. Engineering problems such as computer-aided design and manufacturing have much to gain from conversion to object-oriented databases. Costs were calculated for coding and development, and also for operation. It was found that conversion to an object-oriented database was not usually cost effective as many of the performance benefits could be achieved by the far cheaper process of de-normalisation, or by using the performance improving facilities provided by many relational database systems such as indexing or partitioning or by simply upgrading the system hardware. It is concluded therefore that while object oriented databases are a better alternative for databases built from scratch, the conversion of a legacy relational database to an object oriented database is not necessarily cost effective.

Ensuring the resilience of self-adaptive systems used in critical infrastructure systems is a concern as their failure has severe societal and financial consequences. The current trends in the growth of the scale and complexity of society's workload demands and the systems built to cope with these demands increases the anxiety surrounding service disruptions. Self-adaptive mechanisms instill dynamic behavior to systems in an effort to improve their resilience to runtime changes that would otherwise result in service disruption or failure, such as faults, errors, and attacks. Thus, the evaluation of a self-adaptive system's resilience is critical to ensure expected operational qualities and elicit trust in their services. However, resilience benchmarking is often overlooked or avoided due to the high cost associated with evaluating the runtime behavior of large and complex self-adaptive systems against an almost infinite number of possible runtime changes. Researchers have focused on techniques to reduce the overall costs of benchmarking while ensuring the comprehensiveness of the evaluation as testing costs have been found to account for 50 to 80% of total system costs. These test suite minimization techniques include the removal of irrelevant, redundant, and repetitive test cases to ensure that only relevant tests that adequately elicit the expected system responses are enumerated. However, these approaches require an exhaustive test suite be defined first and then the irrelevant tests are filtered out, potentially negating any cost savings. This dissertation provides a new approach of defining a resilience changeload for self-adaptive systems by incorporating goal-oriented requirements engineering techniques to extract system information and guide the identification of relevant runtime changes. The approach constructs a goal refinement graph consisting of the system's refined goals, runtime actions, self-adaptive agents, and underlying runtime assumptions that is used to identify obstructing conditions to runtime goal attainment. Graph theory is then used to gauge the impact of obstacles on runtime goal attainment and those that exceed the relevance requirement are included in the resilience changeload for enumeration. The use of system knowledge to guide the changeload definition process increased the relevance of the resilience changeload while minimizing the test suite, resulting in a reduction of overall benchmarking costs. Analysis of case study results confirmed that the new approach was more cost effective on the same subject system over previous work. The new approach was shown to reduce the overall costs by 79.65%, increase the relevance of the defined test suite, reduce the amount of wasted effort, and provide a greater return on investment over previous work by a factor of two.