Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology. We present a novel method that has the potential to improve the calibration of complex infectious disease models (hereafter called simulators). We present this in the form of a tutorial and a case study where we history match a dynamic, event-driven, individual-based stochastic HIV simulator, using extensive demographic, behavioural and epidemiological data available from Uganda. The tutorial describes history matching and emulation. History matching is an iterative procedure that reduces the simulator's input space by identifying and discarding areas that are unlikely to provide a good match to the empirical data. History matching relies on the computational efficiency of a Bayesian representation of the simulator, known as an emulator. Emulators mimic the simulator's behaviour, but are often several orders of magnitude faster to evaluate. In the case study, we use a 22 input simulator, fitting its 18 outputs simultaneously. After 9 iterations of history matching, a non-implausible region of the simulator input space was identified that was 10(11) times smaller than the original input space. Simulator evaluations made within this region were found to have a 65% probability of fitting all 18 outputs. History matching and emulation are useful additions to the toolbox of infectious disease modellers. Further research is required to explicitly address the stochastic nature of the simulator as well as to account for correlations between outputs.

Dominance trees have been used as a means for reengineering legacy systems into potential reuse candidates. The dominance relation suggests the reuse candidates which are identified by strongly directly dominated subtrees. We review the approach and illustrate how the dominance tree may fail to show the relationship between the strongly direct dominated procedures and the directly dominated procedures. We introduce a relation of generalized conditional independence which strengthens the argument for the adoption of the potential reuse candidates suggested by the dominance tree and explains their relationship with the directly dominated vertices. This leads to an improved dominance tree, the moral dominance tree, which helps aid program comprehension available from the tree. The generalized conditional independence relation also identifies potential reuse candidates that are missed by the dominance relation.

This paper describes a new approach to the problem of software testing. The approach is based on Bayesian graphical models and presents formal mechanisms for the logical structuring of the software testing problem, the probabilistic and statistical treatment of the uncertainties to be addressed, the test design and analysis process, and the incorporation and implication of test results. Once constructed, the models produced are dynamic representations of the software testing problem. They may be used to drive test design, answer what-if questions, and provide decision support to managers and testers. The models capture the knowledge of the software tester for further use. Experiences of the approach in case studies are briefly discussed.